Biopharmaceutics Applications in Drug Development Lawrence - PDFCOFFEE.COM (2024)

Biopharmaceutics Applications in Drug Development

Biopharmaceutics Applications in Drug Development Edited by

Rajesh Krishna Merck & Co., Inc. Rahway, NJ, USA

and

Lawrence Yu Center for Drug Evaluation & Research Rockville, MD, USA

Rajesh Krishna Merck Research Laboratories Quantitative Clinical Pharmacology Merck & Co., Inc. 126 East Lincoln Avenue Rahway, NJ 07065 USA

ISBN: 978-0-387-72378-5

Lawrence Yu Food and Drug Administration Center for Drug Evaluation & Research 5600 Fishers Lane Rockville, MD 20855 USA

e-ISBN: 978-0-387-72379-2

Library of Congress Control Number: 2007931199 © 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper. 9 8 7 6 5 4 3 2 1 springer.com

To my wife, Bhuvana, my mom, and dad for their unwavering support and lifelong inspiration (R.K.) To my family, Jenny, Alex, and Stephanie for their understanding, support, and love (L.Y.)

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Preface

Drug product performance is a vital aspect of drug development as it draws on interdisciplinary expertise from both pharmaceutics and pharmacokinetics disciplines. It is at the key interface that the discipline of biopharmaceutics has emerged. The past two decades have witnessed considerable advances in biopharmaceutics particularly with regard to bioavailability and bioequivalence, as they relate to product quality and regulatory standards of approval. While the foundation for biopharmaceutics has been laid by pioneers in the field and has been captured in early textbooks devoted to this area, a technical gap does exist on the current and emerging applications of regulatory aspects of biopharmaceutics. The current volume presents an integrated view linking pharmaceutics and the biological consequences to drug development decision making. The book is composed of carefully crafted chapters introducing fundamental concepts, methods, and advances in the area of dissolution, absorption, and permeability and their key applications in dosage form performance, with a specific focus on the applications of biopharmaceutical strategies in the development of successful drugs using case studies. Chapter 1 introduces the basic concepts of biopharmaceutics and discusses the role of biopharmaceutics in various stages of drug development. Chapter 2 describes the molecular and physicochemical properties influencing drug absorption. Chapter 3 examines the utilities and limitations of dissolution testing and discusses biorelevant dissolution methods. Chapter 4 introduces the principles governing drug absorption. It includes all aspects of drug absorption including transport phenomena, factors influencing drug absorption, and methods to evaluate drug absorption. The current industrial practices of evaluating permeability, absorption, and p-glycoprotein interaction are presented in Chap. 5. Chapter 6 investigates the uses of pharmaceutical excipients in drug absorption as enhancers and proposes applications in dosage form design of mucoadhesive materials. Chapter 7 comprehensively describes various transporter families and outlines the role of intestinal transporters in drug absorption. Chapter 8 critically examines bioavailability and bioequivalence from a regulatory perspective, addresses issues inherent in the assessment and demonstration of bioequivalence. The mechanisms and strategies toward BCS are further outlined in Chap. 9. Further, the chapter introduces several case studies encompassing drugs from all BCS classes and multiple formulation types as examples of where a science-based approach to

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dissolution method characterization has been employed to determine rational product quality testing strategies. Chapter 10 discusses the impact of food in regulatory assessment of bioequivalence and proposes recommendations on the design of appropriate biopharmaceutics studies. Chapters 11 and 12 explore the applications of in vitro and in vivo correlation (IVIVC) for parenteral (Chap. 11) and orally administered drug products (Chap. 12). We anticipate that the book will be helpful to individuals who work in the pharmaceutical industry in areas that apply pharmaceutics, biopharmaceutics, and pharmacokinetics, individuals who interact with formulation scientists and pharmacokineticists, as well as those who are in academic and research institutions. Since the fundamentals are also reviewed, we believe that the book will appeal to advanced undergraduate students and graduate students in pharmacy, pharmacology, and allied health professions. We welcome comments and suggestions for improvement from our readers.

Rahway, NJ Rockville, MD

Rajesh Krishna Lawrence Yu

Acknowledgments

This volume is clearly a team effort. We take this opportunity to extend our appreciation to our contributors, publishers, and reviewers. We are particularly indebted to Mr. Sankar and Mr. Venkataraman for their help during proofreading. A special thanks to Kathleen Lyons at Springer for her support and inspiring us to complete this volume in a reasonable time frame.

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Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi 1 Introduction to Biopharmaceutics and its Role in Drug Development . . 1.1 Introduction to Biopharmaceutics . . . . . . . . . . . . . . . . . . 1.1.1 What is Biopharmaceutics? . . . . . . . . . . . . . . . . . 1.1.2 Physical Pharmacy: Physical–Chemical Principles . . . . 1.1.2.1 Solubility . . . . . . . . . . . . . . . . . . . . 1.1.2.2 Hydrophilicity/Lipophilicity . . . . . . . . . . . 1.1.2.3 Salt Forms and Polymorphs . . . . . . . . . . . 1.1.2.4 Stability . . . . . . . . . . . . . . . . . . . . . 1.1.2.5 Particle and Powder Properties . . . . . . . . . 1.1.2.6 Ionization and pKa . . . . . . . . . . . . . . . . 1.1.3 Formulation Principles . . . . . . . . . . . . . . . . . . . 1.1.4 Physiological/Biological Principles . . . . . . . . . . . . 1.1.4.1 Pharmacokinetics . . . . . . . . . . . . . . . . 1.1.5 Biopharmaceutics: Integration of Physical/Chemical and Biological/Pharmacokinetic Principles and Impact on Clinical Efficacy . . . . . . . . . . . . . . 1.1.5.1 Introduction to the Biopharmaceutics Classification System . . . . . . . . . . . . . . 1.1.5.2 Impact of Physical/Chemical Properties on Absorption and Transport . . . . . . . . . . 1.1.5.3 Strategies to Achieve Target Pharmacokinetic Profile . . . . . . . . . . . . . . . . . . . . . . 1.2 Role of Biopharmaceutics in Drug Development . . . . . . . . . . 1.2.1 Importance of Biopharmaceutics in the Overall Development Process . . . . . . . . . . . . . . . . . . . . 1.2.2 Discovery and Preclinical Development: Candidate Selection . . . . . . . . . . . . . . . . . . . . 1.2.3 Preclinical Development: Preparation for Phase I Clinical Studies . . . . . . . . . . . . . . . . . 1.2.4 Early Clinical Development . . . . . . . . . . . . . . . . 1.2.5 Advanced Clinical Development . . . . . . . . . . . . . . 1.2.6 Postapproval Considerations . . . . . . . . . . . . . . . .

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1.2.7 Regulatory Considerations . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 Molecular and Physicochemical Properties Impacting Oral Absorption of Drugs . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Molecular and Physicochemical Properties Impacting Oral Absorption . . . . . . . . . . . . . . . . . . . . . 2.2.1 Molecular Weight, Log P, the Number of H-Bond Donors and Acceptors, Polar Surface Area, and the Number of Rotatable Bonds . . . . . . . . . . . . 2.2.2 Chirality . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Dissolution . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Solubility . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4.1 Definition of Solubility . . . . . . . . . . . . . 2.2.4.2 Factors Contributing to Poor Aqueous Solubility . . . . . . . . . . . 2.2.4.3 pH-Solubility Profile . . . . . . . . . . . . . . 2.2.4.4 Effect of Temperature on Solubility . . . . . . . 2.2.4.5 Solubility in Gastric and Intestinal Fluids . . . . 2.2.4.6 Solubility as a Limiting Factor to Absorption . . 2.2.4.7 Solubility Determination . . . . . . . . . . . . 2.2.4.8 Solubility Prediction . . . . . . . . . . . . . . . 2.2.5 Chemical Stability . . . . . . . . . . . . . . . . . . . . . 2.2.6 Solid State Properties . . . . . . . . . . . . . . . . . . . . 2.2.6.1 Polymorphism . . . . . . . . . . . . . . . . . . 2.2.6.2 Amorphous Material . . . . . . . . . . . . . . . 2.2.6.3 Particle Size . . . . . . . . . . . . . . . . . . . 2.3 Physicochemical Properties and Drug Delivery Systems . . . . . . 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Dissolution Testing . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Significance of Dissolution in Drug Absorption . . . . . . 3.3 Theories of Dissolution . . . . . . . . . . . . . . . . . . . 3.4 Factors Affecting Dissolution . . . . . . . . . . . . . . . . 3.4.1 Factors Related to the Physicochemical Properties of the Drug Substance . . . . . . . . . . . . . . . 3.4.1.1 Solubility . . . . . . . . . . . . . . . . 3.4.1.2 Particle Size . . . . . . . . . . . . . . . 3.4.1.3 Solid Phase Characteristics . . . . . . . 3.4.1.4 Salt Effects . . . . . . . . . . . . . . . . 3.4.2 Factors Related to Drug Product Formulation . . . 3.4.3 Factors Related to Manufacturing Processes . . . . 3.4.4 Factors Related to Dissolution Testing Conditions . 3.5 Roles of Dissolution Testing . . . . . . . . . . . . . . . .

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In Vitro Dissolution Testing as a Quality Control Tool . . . . . . . 3.6.1 Dissolution Method for Quality Control of Immediate-Release Dosage Forms . . . . . . . . . . . 3.6.1.1 Dissolution Media . . . . . . . . . . . . . . . . 3.6.1.2 Apparatus and Test Conditions . . . . . . . . . 3.6.2 Dissolution Method for Quality Control of Modified-Release Dosage Forms . . . . . . . . . . . . 3.6.2.1 Dissolution Media . . . . . . . . . . . . . . . . 3.6.2.2 Apparatus and Test Conditions . . . . . . . . . 3.6.3 Limitations of Quality Control Dissolution Tests . . . . . Biorelevant Dissolution Testing . . . . . . . . . . . . . . . . . . . 3.7.1 In Vivo–In Vitro Correlations . . . . . . . . . . . . . . . . 3.7.2 The Importance of BCS on Biorelevant Dissolution Testing . . . . . . . . . . . . . . . . . . . . . 3.7.3 Biorelevant Dissolution Methods . . . . . . . . . . . . . . 3.7.3.1 Biorelevant Dissolution Media for Gastric Conditions . . . . . . . . . . . . . . 3.7.3.2 Apparatus and Test Conditions for Simulating the Stomach . . . . . . . . . . . 3.7.3.3 Biorelevant Dissolution Media for Intestinal Conditions . . . . . . . . . . . . . 3.7.3.4 Apparatus and Test Conditions for Simulating Small Intestine . . . . . . . . . . 3.7.3.5 Biorelevant Methods for Extended-Release Dosage Forms . . . . . . . . . . . . . . . . . . 3.7.3.6 Remaining Challenges . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Drug Absorption Principles . . . . . . . . . . . . . . . . . . . . . . 4.1 Drug Absorption and Bioavailability . . . . . . . . . . . . . . 4.2 Types of Intestinal Membrane Transport . . . . . . . . . . . . 4.2.1 Passive Diffusion . . . . . . . . . . . . . . . . . . . . 4.2.2 Carrier-Mediated Transport . . . . . . . . . . . . . . 4.2.2.1 Facilitated Diffusion . . . . . . . . . . . . . 4.2.2.2 Active Transport . . . . . . . . . . . . . . . 4.2.3 Paracellular Transport . . . . . . . . . . . . . . . . . 4.2.4 Endocytosis . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Which Absorption Path Dominates Drug Absorption? 4.3 Three Primary Factors Influence Drug Absorption . . . . . . . 4.3.1 Membrane Permeability . . . . . . . . . . . . . . . . 4.3.1.1 Effective Permeability . . . . . . . . . . . . 4.3.1.2 Fraction of Drug Absorbed . . . . . . . . . 4.3.1.3 Permeability and Absorption Rate Constant 4.3.2 Solubility . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Dissolution of Solid Dosage Forms . . . . . . . . . .

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4.4

4.5

4.6

Secondary Factors Influencing Drug Absorption . . . . . . . . 4.4.1 Biological Factors of Gastrio Intestinal Tract . . . . . 4.4.1.1 Gastric Emptying Time . . . . . . . . . . . 4.4.1.2 Surface Area . . . . . . . . . . . . . . . . . 4.4.1.3 GI Transit Time . . . . . . . . . . . . . . . 4.4.1.4 Intestinal Motility . . . . . . . . . . . . . . 4.4.1.5 Components, Volume, and Properties of Gastrointestinal Fluids . . . . . . . . . . 4.4.1.6 Food . . . . . . . . . . . . . . . . . . . . . 4.4.1.7 Blood Flow . . . . . . . . . . . . . . . . . 4.4.1.8 Age . . . . . . . . . . . . . . . . . . . . . 4.4.2 Dosage Factors Influencing Absorption . . . . . . . . Evaluation of Oral Drug Absorption in Humans . . . . . . . . 4.5.1 Drug Absorption Assessment Using In Vivo Data . . . 4.5.1.1 Estimation of Fraction of Drug Absorbed Using Experimental Intestinal Permeability In Vivo . . . . . . . . . . . . . . . . . . . . 4.5.1.2 Estimation of Maximum Absorbable Dose Using In Vivo Absorption Rate Constant and Drug Solubility . . . . . . . . 4.5.1.3 Estimation of MAD from Drug In Vivo Permeability in Human and Drug Solubility 4.5.2 Drug Absorption Assessment Using In Vitro Data . . . 4.5.2.1 In Vitro Testing Conditions for Determining Drug Permeability in Caco-2 Cells and In Vitro/In Vivo Permeability Correlation . . . . . . . . . . . . . . . . . . 4.5.2.2 Estimation of Fraction of Drug Absorbed In Humans Using In Vitro Drug Permeability in Caco-2 Cells . . . . . . . . . . . . . . . 4.5.2.3 Estimation of MAD in Human Based on In Vitro Data . . . . . . . . . . . . . . . 4.5.3 Correlation of Oral Drug Bioavailability and Intestinal Permeability Between Rat and Human . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Evaluation of Permeability and P-glycoprotein Interactions: Industry Outlook . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Anatomy and Physiology of the Small Intestine . . . . . . 5.3 Permeability Absorption Models . . . . . . . . . . . . . . 5.3.1 Physicochemical Methods . . . . . . . . . . . . . 5.3.1.1 Lipophilicity (Log P/Log D) . . . . . . 5.3.1.2 Absorption Potential . . . . . . . . . . . 5.3.1.3 Immobilized Artificial Membrane (IAM)

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In Vitro Methods . . . . . . . . . . . . . . . . . 5.3.2.1 Animal Tissue-Based Methods . . . . 5.3.2.2 Cell-Based Methods . . . . . . . . . . 5.3.3 In Situ Methods . . . . . . . . . . . . . . . . . . 5.3.4 In Vivo Methods . . . . . . . . . . . . . . . . . 5.3.5 In Silico Methods . . . . . . . . . . . . . . . . . Comparison of PAMPA and Caco-2 Cells . . . . . . . . 5.4.1 Parallel Artificial Membrane Permeability Assay 5.4.1.1 PAMPA Study Protocol . . . . . . . . 5.4.2 Caco-2 Cells . . . . . . . . . . . . . . . . . . . 5.4.2.1 Caco-2 Cell Culture . . . . . . . . . . 5.4.2.2 Caco-2 cells Study Protocol . . . . . . 5.4.3 PAMPA and Caco-2 Cell: Synergies . . . . . . . 5.4.4 PAMPA and Caco-2 Cell: Caveats . . . . . . . . 5.4.4.1 Transporter- and Paracellular-Mediated Absorption . . . . . . . . . . . . . . . 5.4.4.2 Incomplete Mass-Balance Due to Nonspecific Binding . . . . . . 5.4.4.3 Inadequate Aqueous Solubility . . . . 5.4.4.4 Other Experimental Variability . . . . P-gp Studies Using Caco-2 Cells . . . . . . . . . . . . . 5.5.1 Experimental Factors Effecting Efflux Ratio . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . .

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6 Excipients as Absorption Enhancers . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Basic Mechanisms in Transcellular and Paracellular Transport . . 6.2.1 Transcellular Transport . . . . . . . . . . . . . . . . . . . 6.2.2 Paracellular Transport . . . . . . . . . . . . . . . . . . . 6.2.3 Mechanisms of Action of Absorption Enhancers . . . . . 6.2.3.1 Action on the Mucus Layer . . . . . . . . . . . 6.2.3.2 Action on Membrane Components . . . . . . . 6.3 Mucoadhesive Polymers as Absorption Enhancers . . . . . . . . . 6.3.1 Theories of Mucoadhesion . . . . . . . . . . . . . . . . . 6.3.2 Material Properties of Mucoadhesives . . . . . . . . . . . 6.3.3 Classes of Mucoadhesive Polymers . . . . . . . . . . . . 6.3.3.1 Polyacrylates . . . . . . . . . . . . . . . . . . . 6.3.3.2 Chitosan . . . . . . . . . . . . . . . . . . . . . 6.3.3.3 N,N,N,-Trimethyl Chitosan Hydrochloride (TMC) . . . . . . . . . . . . . . . . . . . . . . 6.3.3.4 Monocarboxymethyl Chitosan . . . . . . . . . 6.3.3.5 Thiolated Polymers . . . . . . . . . . . . . . . 6.3.3.6 Solid Dosage Form Design Based on TMC and Thiolated Polymers and Their In Vivo Evaluation . . . . . . . . . . 6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Intestinal Transporters in Drug Absorption . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 ATP Binding Cassette Transporters . . . . . . . . . . . . . . . . . 7.2.1 P-Glycoprotein (P-gp; ABCB1) . . . . . . . . . . . . . . 7.2.1.1 The Expression of P-gp . . . . . . . . . . . . . 7.2.1.2 The Regulation of P-gp Expression . . . . . . . 7.2.1.3 P-gp Mediated Drug Transport . . . . . . . . . 7.2.1.4 The Substrate Specificity of P-gp . . . . . . . . 7.2.2 Multidrug Resistance-Associated Protein Family (MRP; ABCC) . . . . . . . . . . . . . . . . . . . . . . . 7.2.2.1 The Expression of MRPs . . . . . . . . . . . . 7.2.2.2 The Regulation of MRP Isoform Expression . . 7.2.2.3 The Substrate Specificity of MRP’s . . . . . . . 7.2.3 Breast Cancer Resistance Protein (BCRP; ABCG2) . . . . 7.3 Solute Carrier Transporters . . . . . . . . . . . . . . . . . . . . . 7.3.1 Proton/Oligopeptide Transporters (POT; SLC15A) . . . . 7.3.1.1 Peptide Transporter Mediated Transport . . . . 7.3.1.2 The Substrate Specificity of Peptide Transporters . . . . . . . . . . . . . . . . . . . 7.3.1.3 The Regulation of Peptide Transporters . . . . . 7.3.2 Organic Anion Transporters (OAT, SLC22A; OATP, SLCO) . . . . . . . . . . . . . . . 7.3.2.1 OAT (SLC22A) . . . . . . . . . . . . . . . . . 7.3.2.2 OATP (SLCO) . . . . . . . . . . . . . . . . . . 7.3.3 Organic Cation Transporters (OCT, OCTN; SLC22A) . . 7.3.3.1 The Substrate Specificity of Organic Cation Transporters . . . . . . . . . . . . . . . . . . . 7.3.3.2 Organic Cation Transporter Mediated Transport . . . . . . . . . . . . . . . . . . . . . 7.3.3.3 The Expression of Organic Cation Transporters . . . . . . . . . . . . . . . . . . . 7.3.3.4 The Regulation of Organic Cation Transporters . . . . . . . . . . . . . . . . . . . 7.3.4 Nucleoside Transporters (CNT, SLC28A; ENT, SLC29A) . . . . . . . . . . . . . . 7.3.4.1 The Molecular and Structural Characteristics of Nucleoside Transporters . . . . . . . . . . . 7.3.4.2 The Substrate Specificities of Nucleoside Transporters . . . . . . . . . . . . . . . . . . . 7.3.4.3 The Expression of Nucleoside Transporters . . . 7.3.4.4 The Regulation of Nucleoside Transporters . . . 7.3.5 Monocarboxylate Transporters (MCT; SLC16A) . . . . . 7.3.5.1 Molecular and Structural Characteristics of Monocarboxylate Transporters . . . . . . . .

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7.4

The Substrate Specificity of Monocarboxylate Transporters . . . . . . . . . . . . . . . . . . . 7.3.5.3 The Expression of Monocarboxylate Transporters . . . . . . . . . . . . . . . . . . . 7.3.5.4 The Regulation of Monocarboxylate Transporters . . . . . . . . . . . . . . . . . . . Impact of Intestinal Transporters on Bioavailability . . . . . . . .

8 Bioavailability and Bioequivalence . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Bioavailability and Bioequivalence . . . . . . . . . . . . . . 8.2.1 Bioavailability and its Utility in Drug Development and Regulation . . . . . . . . . . . . . . . . . . . . 8.2.2 Bioequivalence and its Utility in Drug Development and Regulation . . . . . . . . . . . . . . . . . . . . 8.2.3 Bioavailability and Bioequivalence Studies: General Approaches . . . . . . . . . . . . . . . . . . . . . . 8.3 Pharmacokinetic Bioavailability and Bioequivalence Studies 8.3.1 Bioavailability Studies: General Guidelines and Recommendations . . . . . . . . . . . . . . . . 8.3.2 Bioequivalence Studies: General Guidelines and Recommendations . . . . . . . . . . . . . . . . 8.3.2.1 Study Design . . . . . . . . . . . . . . . 8.3.2.2 Dose . . . . . . . . . . . . . . . . . . . . 8.3.2.3 Subjects . . . . . . . . . . . . . . . . . . 8.3.2.4 Statistical Analysis of Bioequivalence . . 8.4 Bioequivalence: Challenging Topics . . . . . . . . . . . . . 8.4.1 Drugs with Active Metabolites . . . . . . . . . . . . 8.4.2 Enantiomers vs. Racemates . . . . . . . . . . . . . 8.4.3 Endogenous Substances . . . . . . . . . . . . . . . 8.4.4 Highly Variable Drugs . . . . . . . . . . . . . . . . 8.4.4.1 Static Expansion of the BE Limits . . . . 8.4.4.2 Expansion of Bioequivalence Limits Based on Fixed Sample Size . . . . . . . . . . . 8.4.4.3 Scaled Average Bioequivalence . . . . . . 8.5 Biowaivers . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Solutions . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 Lower Strength . . . . . . . . . . . . . . . . . . . . 8.5.3 Biopharmaceutical Classification System . . . . . . 8.5.3.1 Biowaivers for BCS Class 2 Drugs with Dependent Solubility . . . . . . . . . . . 8.5.3.2 Biowaivers for BCS Class 3 Drugs . . . . 8.6 Locally Acting Drugs . . . . . . . . . . . . . . . . . . . . . 8.6.1 Topical Dermatological Products . . . . . . . . . . .

222 223 224 225

. . . 262 . . . 262 . . . 262 . . . 262 . . . 263 . . . 264 . . . 265 . . . 265 . . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

267 267 267 268 268 271 271 273 273 274 274

. . . . . . . . . . . . . . . . . . pH . . . . . . . . . . . .

275 275 276 276 278 279 280 280 281 281

xviii

Contents

8.6.2

8.7

Locally Acting Nasal and Oral Inhalation Drug Products . . . . . . . . . . . . . . . . . . . . 8.6.2.1 Nasal Spray Products . . . . . . . . 8.6.2.2 Oral Inhalation Products . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

283 284 285 287

9 A Biopharmaceutical Classification System Approach to Dissolution: Mechanisms and Strategies . . . . . . . . . . . . . . . . . . . . . . . . 290 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 9.2 Biopharmaceutical Classification System Approach to Dissolution 290 9.3 In Vitro–In Vivo Dissolution Correlation . . . . . . . . . . . . . . 292 9.4 Recent Climate: Pharmaceutical Quality Assessment . . . . . . . 294 9.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 9.5.1 BCS Class I and III Case Studies . . . . . . . . . . . . . . 296 9.5.1.1 Case Study 1: Fast Release (>85% Release in 15 min) with Disintegration Controlled Dissolution . . . . . . . . . . . . . . 298 9.5.1.2 Case Study 2: 5), which protects the drug from degradation in gastric condition and against drug stimulation of gastric mucosa. In such cases, the controlled release dosage form will have a completely different absorption profile as compared with immediate release dosage forms.

4.5 4.5.1

Evaluation of Oral Drug Absorption in Human Drug Absorption Assessment Using In Vivo Data

4.5.1.1 Estimation of Fraction of Drug Absorbed Using Experimental Intestinal Permeability In Vivo An in vivo method has been successfully established to measure human intestinal permeability by in situ intestinal perfusion (Lennernas et al., 1997; Sun et al., 2002, 2002; Cao et al., 2006). A perfusion tube, as illustrated in Fig. 4.8, is placed in the human jejunum to allow drug passage through a 10-cm intestinal segment. The drug concentration is measured at the inlet and outlet of the perfusion tube. The drug permeability is then calculated with the following equation Peff, human = Q(1 − Cout /Cin )/2π RL,

(4.17)

where Peff, human is drug permeability in the human intestine, Q is the perfusion flow rate (2 min ml−1 ), Cin is inlet drug concentration of the perfusion tube, Cout is outlet drug concentration of the perfusion tube, R is human small intestine radius (2 cm), and L is the 10-cm perfusion segment. When the permeability is plotted against the fraction of drug absorbed, the relationship can be established (4.18) (Fig. 4.9) (Amidon et al., 1988, 1995; Oh et al., 1993) Fa = 1 − exp(−2An) = 1 − exp(−2Peff, human Tres /R),

(4.18)

where Fa is the fraction of drug absorbed, Peff, human is drug permeability in human intestine, Tres is transit time in human small intestine (3 h), R is the radius of human small intestine (2 cm).

4. Drug Absorption Principles

87

F IGURE 4.8. Perfusion tube for in situ human intestinal permeability measurement

Human fraction absorbed (%)

120

100

80

60

40

20

0 0

2

4

6

8 −4

Human jejunum permeability ( 10

10

cm/s)

12

D-glucose Verapamil Piroxicam Phenylalanine Cyclosporin Enalapril Cephalexin Losartan Lisinopril Amoxicillin Methyldopa Naproxen Antipyrine Desipramine Propanolol Amiloride Metoprolol Terbutaline Mannitol Cimetidine Ranitidine Enalaprilate Atenolol Hydrochlorothiazide Trend line

F IGURE 4.9. Prediction of the fraction of drug absorbed using human jejunum permeability. Drugs are labeled with different symbols. Closed symbols are drugs absorbed through carrier-mediated process, while open symbols are drugs absorbed through passive diffusion (Sun et al., 2002)

An = Peff, human × Tres /R. However, when in situ intestinal perfusion is performed, low drug concentrations are used for permeability measurements. In this case, the drug concentration is always below its solubility limit. Since the fraction of drug absorbed is a function

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of its solubility and permeability, (4.18) is not suitable for predicting the fraction of drug absorbed when high drug concentration above in vivo solubility limit is used in the experiment. This model has been further modified to overcome this problem by utilizing different calculation methods according to the drug’s solubility (Yu et al., 1996; Yu and Amidon, 1999) Fa = 1 − exp(−2An), Fa = 2An/D0 ,

when Cin < S,

when Cin > S,

Cout < S,

Cout > S,

Fa = 1 − 1/[D0 exp(−2An + D0 − 1)],

when Cin > S,

(4.19) (4.20) Cout < S, (4.21)

where Fa is the fraction of drug absorbed, An = Peff, human × Tres /R, Cin is inlet drug concentration of the perfusion tube, Cout is outlet drug concentration of the perfusion tube, Peff, human is drug intestinal permeability in human, Tres is transit time in human small intestine (3 h), D0 is dose number [D0 = (dose/volume)/S], and S is drug solubility. The challenge for this method is that drug intestinal permeability has to be obtained in vivo in human, which is very difficult and not available during early stages of drug discovery and development. Meanwhile the relationship between Cout (or Cin ) and solubility is also difficult to determine in vivo. 4.5.1.2 Estimation of Maximum Absorbable Dose Using In Vivo Absorption Rate Constant and Drug Solubility Another method has been proposed to estimate maximum absorbable dose (MAD) based on the in vivo absorption rate constant (Curatolo, 1987) with the following equation MAD = S K a V T,

(4.22)

where S is drug solubility, K a is absorption rate constant, V is intake water volume (250 ml), and T is transit time in small intestine (3 h). For instance, MAD could be estimated using different K a values (Table 4.1). However, K a has to be obtained from in vivo pharmacokinetic studies in animals or humans, which are usually not available during early stages of drug discovery and development. Alternatively K a can be estimated by in vivo drug permeability if it is available by (4.23) K a = Peff, human (A/V ) = Peff, human (2π RL/π R 2 L) = Peff, human (2/R), (4.23) where A is the surface area, V is the volume, R is the radius, and L is the length of small intestine. However, it is also difficult to estimate the appropriate volume for the calculation in this method. Although standard water intake is 250 ml, the daily gastric secretion volume is 2,000 ml; intestine secretion volume is in the range of 1,500–2,000 ml; and bile and pancreatic secretion is 500–1,500 ml (Dressman et al., 1998).

4. Drug Absorption Principles

89

TABLE 4.1. Estimation of maximum absorbable dose (MAD) using absorption rate constant (K a ) in human with following equation: MAD = S K a V T K a (min−1 ) 0.001 0.001 0.001 0.001 0.01 0.01 0.01 0.01 0.1 0.1 0.1 0.1

Solubility (mg ml−1 )

MAD (mg)

0.001 0.01 0.1 1 0.001 0.01 0.1 1 0.001 0.01 0.1 1

0.045 0.45 4.5 45 0.45 4.5 45 450 4.5 45 450 4,500

4.5.1.3 Estimation of MAD from Drug In Vivo Permeability in Humans and Drug Solubility At the steady state of in situ human intestinal perfusion, drug flux J is a function of permeability, drug concentration, and absorption surface area (Amidon et al., 1988, 1995; Oh et al., 1993), J = dm/dt = Peff, human S d A.

(4.24)

MAD = Peff, human S AT = Peff, human S2πRLT,

(4.25)

Then, where J is drug flux, Peff, human is drug permeability in human intestine, S is drug solubility, A is absorption surface area, T is transit time in small intestine (3 h), R is the radius of small intestine (2 cm), and L is the length of small intestine (6 m). It is worth noting that the small intestine surface area for drug absorption should include surface area of villi and microvilli, but the surface area calculated in (4.24) is only the intestinal tube surface area without such consideration. However, since the permeability obtained in the in situ perfusion is calculated by (4.17), where the surface area also does not include villi and microvilli, the error is cancelled in the MAD calculation in (4.25), and it does not affect the MAD estimation if human intestinal permeability is used. The examples for estimation of MAD using permeability with (4.25), or using calculated K a from human permeability with (4.22) and (4.23) are summarized in Table 4.2. In comparison of the examples in Tables 4.1 and 4.2, it seems that MAD might be underestimated using the absorption rate constant in (4.22) due to the assumption of 250 ml of volume in the calculation. MAD might be overestimated using permeability in (4.9) due to the assumption that the drug is absorbed at the maximum concentration (at its solubility) in the whole small intestinal region (6 m)

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TABLE 4.2. Estimation of MAD using drug intestinal permeability in human with following equation: MAD = Peff, human S2π RL T, MAD = Peff, human S A eff T , or with calculated absorption rate constant (K a ) with following equations: K a = Peff, human (2/R) and MAD = S K a V T Peff, human Solubility −1 (×10−4 cm s−1 ) (mg ml )

0.1 0.1 0.1 0.1 1 1 1 1 10 10 10 10

0.001 0.01 0.1 1 0.001 0.01 0.1 1 0.001 0.01 0.1 1

MAD (mg) calculated from Peff, human

0.813 8.13 81.3 813 8.13 81.3 813 8,138 81.3 813 8,138 81,388

MAD (mg) calculated from effective absorption surface area

Calculated K a from Peff, human (min−1 )

MAD (mg) from calculated Ka

0.086 0.864 8.64 86.4 0.864 8.64 86.4 864 8.64 86.4 864 8,640

0.0006 0.0006 0.0006 0.0006 0.006 0.006 0.006 0.006 0.06 0.06 0.06 0.06

0.027 0.27 2.7 27 0.27 2.7 27 270 2.7 27 270 2,700

with maximum surface area over the entire 3 h absorption period, while in reality only partial small intestine is used at a given time. Therefore, the effective absorption surface area of 800 cm2 is proposed to calculate MAD (Curatolo, 1987). The examples for estimation of MAD using this effective surface area are also summarized in Table 4.2. MAD using the effective absorption surface area seems more appropriate. If the MAD based on permeability and solubility is below the required clinical dose, formulation development, and delivery would be very challenging.

4.5.2

Drug Absorption Assessment Using In Vitro Data

When MAD is estimated with in vivo data, either the in vivo absorption rate constant, or the drug in vivo intestinal permeability is required for the calculation. However, during early stages of drug discovery and development, in vivo data are usually unavailable. The challenge is to optimize the process for selecting compounds to evaluate in vivo human studies based on in vitro data. Fortunately, drug permeability in Caco-2 cells and drug solubility are routinely screened in the pharmaceutical industry. These data can be utilized to predict fraction of drug absorbed and MAD in humans to identify the best candidates for further clinical development. 4.5.2.1 In Vitro Testing Conditions for Determining Drug Permeability in Caco-2 Cells and In Vitro/In Vivo Permeability Correlation Many laboratories have established methods for measuring drug permeability in Caco-2 cells with different testing conditions (Chong et al., 1996; Yee, 1997;

4. Drug Absorption Principles 100

91

Furosemide

Human jejunum permeability (x10−4 cm/s) at pH 6.5

Hydrochlorothiazide Atenolol Cimetidine Mannitol 10

Terbutaline Amoxicillin (C) Lisinopril (C) Metoprolol Cephalexin (C)

1

Enalapril (C) Propranolol Phenylalanine (C) Desipramine 0.1

Antipyrine Piroxicam Verapamil (C) logY = 0.6532 logX - 0.3036, R2 = 0.7276 (all drugs) logY = 0.7524 logX - 0.5441, R2 = 0.8492 (passively diffusive) LogY = 0.542 LogX + 0.06, R2 = 0.7854 (Carrier-mediated)

Ketoprofen Naproxen

0.01 0.01

0.1

1

Caco-2 permeability (

10

10−6 cm/s)

100

1000

D-Glucose (C)

at pH 6.5

F IGURE 4.10. In vitro/in vivo permeability correlation of 20 drugs at pH 6.5. Correlation coefficient (R 2 = 0.7276) was calculated from the permeability of all 20 drugs. Correlation coefficient (R 2 = 0.8492) was calculated from the permeability of the following drugs: furosemide, hydrochlorothiazide, atenolol, cimetidine, mannitol, terbutaline, metoprolol, propranolol, desipramine, antipyrine, piroxicam, ketoprofen, and naproxen. Correlation coefficient (R 2 = 0.7854) was calculated from the permeability of the following drugs: cephalexin, enalapril, lisinopril, losartan, amoxicillin, phenylalanine, L -leucine, L -dopa, Dglucose, cyclosporin, and verapamil. Drugs are labeled with different symbols. Black symbols are drugs absorbed through carrier-mediated process, while gray and open symbols are drugs absorbed through passive diffusion (Sun et al., 2002)

Pade and Stavchansky, 1998; Yamashita et al., 2000). Some laboratories use buffer with pH 7.4 in both apical and basolateral sides of the Caco-2 cells, while others use pH 6.5 buffer at the apical side and pH 7.4 buffer at the basolateral side. When correlation analysis was performed between in vitro drug permeability in Caco-2 cells and in vivo drug permeability in humans, a better correlation was observed between human in vivo permeability and Caco-2 permeability measured at pH 6.5 than at pH 7.4 (Figs. 4.10 and 4.11). The correlation coefficient (R 2 ) of in vitro and in vivo permeability of 24 drugs assayed at pH 7.4 was 0.5126 in (4.26), while the in vitro and in vivo permeability correlation coefficient (R 2 ) of the 20 drugs determined at pH 6.5 was 0.7276 in (4.27) (Sun et al., 2002). Log Peff, human = 0.4926 Log Peff, Caco−2 − 0.1454, Log Peff, human = 0.6532 Log Peff, Caco−2 − 0.3036.

(4.26) (4.27)

However, if the drugs were absorbed through a carrier-mediated processes, such as cephalexin, enalapril, cyclosporin, amoxicillin, lisinopril, losartan, phenylalanine,

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X. Cao et al. Frosemide Hydrochlorothiazide Atenolol Ranitidine Cimetidine Mannitol

10

Terbutaline Creatine

−4

Human jejunum permeability (x 10 cm/s) at pH 6.5

100

Amoxicillin (C) Lisinopril (C) Losartan (C) 1

Metoprolol Cephalexin (C) Cyclosporin (C) Propranolol L-dopa (C)

0.1

Desipramine Antipyrine L-leucine (C) logY = 0.4926 logX -0.1454, R2 = 0.5126 (all drugs) logY = 0.6836 logX -0.5579, R2 = 0.8376 (passively diffusive) LogY = 0.4898 LogX + 0.3311, R2 = 0.6775 (Carrier-mediated)

0.01 0.01

0.1

1

−6

10

100

1000

Caco-2 permeability ( 10 cm/s) at pH 7.4

Piroxicam Verapamil (C) Naproxen Ketoprofen D-glucose (C)

F IGURE 4.11. In vitro/in vivo permeability correlation of 24 drugs at pH 7.4. Correlation coefficient (R 2 = 0.5126) was calculated from the permeability of all 24 drugs. Correlation coefficient (R 2 = 0.8376) was calculated from the permeability of the following drugs: furosemide, hydrochlorothiazide, atenolol, ranitidine, cimetidine, mannitol, terbutaline, creatine, metoprolol, propranolol, desipramine, antipyrine, piroxicam, ketoprofen, and naproxen. Correlation coefficient (R 2 = 0.6775) was calculated from the permeability of the following drugs: cephalexin, enalapril, lisinopril, losartan, amoxicillin, phenylalanine, L -leucine, L -dopa, D-glucose, cyclosporin, and verapamil. Drugs are labeled with different symbols. Black symbols are drugs absorbed through carrier-mediated process, while gray and open symbols are drugs absorbed through passive diffusion (Sun et al., 2002)

verapamil, L-dopa, D-glucose, and L-leucine were excluded, the in vitro/in vivo permeability correlation improves at both pHs, such that the permeability correlation coefficient (R 2 ) of 15 passively diffused drugs at pH 7.4 and 13 passively diffused drugs at pH 6.5 were 0.8376 in (4.28) and 0.8492 in (4.29), respectively. Log Peff, human = 0.6836 Log Peff, Caco−2 − 0.5579, Log Peff, human = 0.7524 Log Peff, Caco−2 − 0.5441.

(4.28) (4.29)

4.5.2.2 Estimation of Fraction of Drug Absorbed In Humans Using In Vitro Drug Permeability in Caco-2 Cells When in vitro drug permeability in Caco-2 cells is plotted against drug fraction absorbed in humans, a relationship could also be established as shown in Figs. 4.12 and 4.13 (Sun et al., 2002). As these data clearly indicate, it might be difficult to predict the fraction of drug absorbed for the drugs with low Caco-2 permeability.

4. Drug Absorption Principles

Human fraction absorbed (%)

120 100 80 60 40 20 0 0

10

20

30

Caco-2 permeability at pH 6.5 (

40

50

93

D-glucose Verapamil Phenylalanine Piroxicam Enalapril Cephalexin Lisinopril Amoxicillin Ketoprofen Antipyrine Desipramine Propanolol Metoprolol Terbutaline Mannitol Cimetidine Atenolol hydrochlorothiazide Furosemide Trend line

-6 10 cm/s)

F IGURE 4.12. Prediction of the fraction of drug absorbed using Caco-2 permeability at pH 6.5. Drugs are labeled with different symbols. Closed symbols are drugs absorbed through carrier-mediated process, while open symbols are drugs absorbed through passive diffusion (Sun et al., 2002)

Human fraction absorbed (%)

110 100 90 80 70 60 50 40 30 0

20

40

60

D-glucose Verapamil Piroxicam L-dopa Cyclosporin Cephalexin Losartan Lisinopril Amoxicillin Naproxen Desipramine Propanolol Metoprolol Terbutaline Mannitol Cimetidine Ranitidine Atenolol Hydrochlorothiazide Furosemide Trend line

Caco-2 permeability at pH 7.4 ( 10-6 cm/s)

F IGURE 4.13. Prediction of the fraction of drug absorbed using Caco-2 permeability at pH 7.4. Drugs are labeled with different symbols. Closed symbols are drugs absorbed through carrier-mediated process, while open symbols are drugs absorbed through passive diffusion (Sun et al., 2002)

More discrepancy was also observed for the drugs with carrier-mediated absorption routes especially when drug permeability in Caco-2 cells was obtained at pH 7.4 in the apical side. 4.5.2.3 Estimation of MAD in Human Based on In Vitro Data Since an in vitro and in vivo drug permeability correlation has been established in (4.26), in vivo drug permeability in human could be easily estimated by in vitro drug permeability in Caco-2 cells. Although some of the transporter substrates showed high discrepancy from the in vitro/in vivo permeability correlation when Caco-2 permeability was obtained at pH 7.4, the overall correlation has shown

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reasonable prediction when Caco-2 permeability was obtained at pH 6.5 (Sun et al., 2002). Since MAD could be estimated using in vivo drug permeability in human with (4.25), the MAD could be estimated with in vitro drug permeability in Caco-2 cells in the following (4.30) MAD = Peff, human S Aeff T = 10(0.6532 Log Peff, Caco −0.3036) S Aeff T,

(4.30)

where Peff, human is the in vivo drug permeability in human, Peff, Caco is the in vitro drug permeability in Caco-2 cells, S is the drug solubility, Aeff is the effective absorption surface area without considering villi and microvilli, and T is transit time in small intestine (3 h). As discussed earlier, the error associated when not considering surface area of villi and microvilli is cancelled in the MAD calculation using permeability in (4.25) and (4.30). In addition, the surface area of microvilli in Caco-2 cells is also irrelevant in calculation of MAD, since the human permeability is calculated with Caco-2 permeability by the correlation analysis. Alternatively, the MAD can be estimated using (4.22) and (4.23), where K a can be estimated with human permeability in vivo or Caco-2 permeability in vitro with the following (4.31) K a = Peff, human (2/R) = (2/R)10(0.6532 Log Peff, Caco −0.3036),

(4.31)

where Peff, human is drug in vivo permeability in human, Peff, Caco is drug in vitro permeability in Caco-2 cells, and R is the radius of small intestine (2 cm). The examples of MAD estimation using in vitro drug permeability in Caco-2 cells are summarized in Table 4.3. TABLE 4.3. Estimation of MAD using drug permeability in Caco-2 cells with following equation: MAD = 10(0.6532 Log Peff, caco −0.3036) S A eff T , or with calculated absorption rate constant (K a ) with following equations: K a = (2/R)10(0.6532 Log Peff, caco −0.3036) and MAD = S K a V T Peff, Caco−2 (×10−6 cm s−1 ) 0.1 0.1 0.1 0.1 1 1 1 1 10 10 10 10 100 100 100 100

Solubility (mg ml−1 ) 0.001 0.01 0.1 1 0.001 0.01 0.1 1 0.001 0.01 0.1 1 0.001 0.01 0.1 1

MAD (mg) calculated from Peff, Caco−2 0.0955 0.955 9.545 95.45 0.429 4.294 42.94 429.4 1.932 19.32 193.2 1,932 8.696 86.96 869.6 8,696

Calculated K a from Peff, Caco−2 (min−1 ) 0.000663 0.000663 0.000663 0.000663 0.002982 0.002982 0.002982 0.002982 0.01342 0.01342 0.01342 0.01342 0.060388 0.060388 0.060388 0.060388

MAD (mg) from calculated K a 0.0298 0.298 2.98 29.8 0.134 1.34 13.4 134 0.603 6.03 60.3 603 2.17 27.17 271.7 2,717

4. Drug Absorption Principles

95

4.5.3 Correlation of Oral Drug Bioavailability and Intestinal Permeability Between Rat and Human

Oral Bioavailbility in Human (%)

Animal models are widely used to evaluate drug pharmacokinetics and drug absorption. However, the correlation of oral bioavailability (F) values of 48 drugs in rat and human has been studied and no correlation (r 2 = 0.29) was found due to low correlation of drug metabolism in rat and human (Cao et al., 2006). Results of the F values comparison are shown in Fig. 4.14. In contrast, Chiou and Buehler observed low correlation in the bioavailability of 35 drugs between monkey and human with r 2 = 0.502 (Chiou and Buehler, 2002), which may be due to the closer physiological similarity between monkey and human. These data indicate that oral bioavailability in rat could not be used to predict oral drug bioavailability in human. Due to the structural similarities of intestinal membrane, drug absorption in animal models may be used to predict drug absorption in human. In order to depict the oral drug absorption process, in situ intestinal permeabilities of 17 drugs with different absorption mechanisms were evaluated in rat and human jejunum (Cao et al., 2006). Since permeability is one of the primary factors governing absorption (Amidon et al., 1995), studying the permeability correlation is useful when predicting human absorption from rat permeability. The tested drugs are absorbed by carrier-mediated processes as well as passive diffusion. For instance, valacyclovir, enalapril, and cephalexin are all absorbed through a peptide transporter (hPepT1). Leucine, phenylalanine, L-Dopa, and methyldopa are absorbed through amino acid transporters. Verapamil is a P-gp substrate. Cimetidine is an organic cation transporter substrate. Propranolol, atenolol, and furosemide are all absorbed through passive diffusion. The drug permeabilities in the rat jejunum were then correlated 120 100 80 60 40 20

Fhuman = 0.5918Frat + 37.358 R2 = 0.2917

0 0

20

40

60

80

100

120

Oral Bioavailability in Rat (%) F IGURE 4.14. Correlation of oral bioavailability between rat and human. Total of 48 drugs were plotted. The equation describes the correlations for rat oral bioavailability (Frat ) and human oral bioavailability (Fhuman ) (Cao et al., 2006)

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Intestinal Permeability in Human (× 10-4 cm/s)

10

1

0.1

Log Phuman = 1.28 log Prat + 2.064,

0.01 0.01

LogPhuman = 1.34 logPrat + 1.83,

0.1

R2 = 0.7

2

R = 0.8 (without verapamil)

1

10

Verapamil Leucine Phenylalanine L-Dopa Valacyclovir Enalapril Cephalexin Methyldopa Propranolol Cimetidine Atenolol Furosemide Ketoprofen Naproxen Antipyrine Metoprolol Tacrolimus

Intestinal Permeability in Rat (× 10-4 cm/s) F IGURE 4.15. Correlation of drug permeability in rat jejunum and in human jejunum. Permeability coefficients (Peff ) were determined by in situ intestinal perfusion. The equations describe the correlations for rat permeability (Prat ) and human permeability (Phuman ) (Cao et al., 2006)

with the drug permeabilities in the human jejunum (Fig. 4.15). It showed that drug permeability in the rat is generally five to ten-fold lower than the permeability in the human. However, both carrier-mediated and passively diffusing drugs showed a reasonable correlation (r 2 = 0.7). Interestingly, verapamil (a P-gp substrate) permeability in human deviates from the correlation curve. The permeability correlation between human and rat is highly increased (r 2 = 0.8) when verapamil is excluded in the analysis. This study is in agreement with the other report, that the percentage of absorption of 98 drugs was correlated between rat and human with a correlation of r 2 = 0.88 (Zhao et al., 2003). In vivo absorption in rats could be a useful method to predict the extent of absorption in humans. The permeability in rat for water soluble and poor water soluble compounds was used to predict the fraction of drug absorbed in humans (Watanabe et al., 2004). In another study, a high correlation was found for a variety of compounds displaying various physicochemical and pharmacologic activities between the two species in the dose-independent absorption range (Chiou and Barve, 1998). However, a previous study reported that effective permeability estimates of passively absorbed solutes correlate highly in rat and human jejunum while carrier-mediated transport requires scaling between the models because the substrate specificity and/or transport maximum may differ (Fagerholm et al., 1996). These discrepancies might be due to the different numbers of transporter substrates that are used in the correlation analysis. However, all of these studies indicate that reasonable permeability correlation between human and rat can be used to predict drug absorption in humans.

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To understand the underlying mechanisms in the similarity in drug intestinal absorption between humans and rats, correlation analysis of the expression levels of transporters and metabolizing enzymes between rat and human intestine were further conducted (Cao et al., 2006). Moderate correlations (with r 2 > 0.56) were found for the expression levels of transporters in the duodenum of human and rat. Although there is discrepancy observed in the expression of MDR1, MRP3, GLUT1, and GLUT3, other transporters (such as PepT1, SGLT-1, GLUT5, MRP2, NT2, and high affinity glutamate transporter) and the overall drug transporters expression share similar expression levels in both human and rat intestine with regional dependent expression patterns, which has high expression in the small intestine and low expression in the colon. These data provide the molecular mechanisms for the similarity and correlation of drug absorption (Fa ) in the small intestine between rat and human. In contrast, the expression of metabolizing enzymes (CYP3A4/CYP3A9 and UDPG) showed 12- to 193fold difference between human and rat intestine with distinct regional dependent expression patterns. No correlation was found for the expressions of metabolizing enzymes between rat and human intestine, which indicate the difference in drug metabolism in two different species and the challenges in predicting Fg and F from rat to human.

4.6

Summary

Drug absorption is a complicated process in which many physiological and physiochemical factors are involved. Understanding the principles of drug absorption benefits the designing of formulation strategies to enhance the bioavailability and in vivo drug activity. In summary, drug absorption mechanisms include passive diffusion and active transport. Permeability, solubility, and dissolution, GI physiological conditions, and dosage forms can influence the drug absorption rate. In general, if a drug has high water solubility and low membrane permeability (hydrophilic drugs), permeability usually limits absorption, unless it is carrier mediated or paracellular absorption dependent. Strategies which can enhance the drug permeability in dosage design could be used to increase this permeability controlled drug absorption. In contrast, if a drug has low solubility and high permeability (lipophilic drugs), solubility (and dissolution) usually limits absorption. Formulation strategies should be optimized in the dosage form to enhance the solubility (and dissolution) controlled drug absorption. If neither of the above two properties limits the absorption such as for drugs with high solubility and high permeability, then gastric emptying rate limits the drug absorption. Both in vivo and in vitro methods have been explored to assess drug absorption in human. Rat and human show similar drug absorption profiles and similar transporter expression patterns in the small intestine, while the two species exhibit distinct expression levels and patterns for metabolizing enzymes in the intestine. These data provide the molecular mechanisms for the similarity and correlation of drug absorption (Fa ) in the small intestine between rat and human. Therefore,

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rat can be used to predict oral drug absorption (Fa ) in the small intestine of human, but not to predict drug metabolism (Fg and Fh ) and oral bioavailability (F) in human.

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Daugherty AL and Mrsny RJ (1999) Transcellular uptake mechanisms of the intestinal epithelial barrier Part one. Pharm Sci Technol Today 4:144–151. Dressman JB, Amidon GL, Reppas C and Shah VP (1998) Dissolution testing as a prognostic tool for oral drug absorption: immediate release dosage forms. Pharm Res 15:11–22. Fagerholm U, Johansson M and Lennernas H (1996) Comparison between permeability coefficients in rat and human jejunum. Pharm Res 13:1336–1342. Feher JJ (1983) Facilitated calcium diffusion by intestinal calcium-binding protein. Am J Physiol 244:C303–307. Fleisher D, Lippert CL, Sheth N, Reppas C and Wlodyga J (1990) Nutrient effects on intestinal drug absorption. J Control Release 11:41–49. Frenning G and Stromme M (2003) Drug release modeled by dissolution, diffusion, and immobilization. Int J Pharm 250:137–145. Hansen JE, Weisbart RH and Nishimura RN (2005) Antibody mediated transduction of therapeutic proteins into living cells. Scientific World Journal 5:782–788. Higuchi WI and Ho NFH (1988) Membrane transfer of drugs. Int J Pharm 2:10–15. Jinno J, Kamada N, Miyake M, Yamada K, Mukai T, Odomi M, Toguchi H, Liversidge GG, Higaki K and Kimura T (2006) Effect of particle size reduction on dissolution and oral absorption of a poorly water-soluble drug, cilostazol, in beagle dogs. J Control Release 111:56–64. Kwan KC (1997) Oral bioavailability and first-pass effects. Drug Metab Dispos 25:1329–1336. Lennernas H (1998) Human intestinal permeability. J Pharm Sci 87:403–410. Lennernas H, Nylander S and Ungell AL (1997) Jejunal permeability: a comparison between the Using chamber technique and the single-pass perfusion in humans. Pharm Res 14:667–671. Liang XJ, Mukherjee S, Shen DW, Maxfield FR and Gottesman MM (2006) Endocytic recycling compartments altered in cisplatin-resistant cancer cells. Cancer Res 66: 2346–2353. Narawane L and Lee VHL (1994) Absorption barriers. Drug Target Deliv 3:1–66. Oh DM, Curl RL and Amidon GL (1993) Estimating the fraction dose absorbed from suspensions of poorly soluble compounds in humans: a mathematical model. Pharm Res 10:264–270. Oh DM, Han HK and Amidon GL (1999) Drug transport and targeting. Intestinal transport. Pharm Biotechnol 12:59–88. Pade V and Stavchansky S (1998) Link between drug absorption solubility and permeability measurements in Caco-2 cells. J Pharm Sci 87:1604–1607. Rouge N, Buri P and Doelker E (1996) Drug absorption sites in the gastrointestinal tract and dosage forms for site-specific delivery. Int J Pharm 136:117–139. Schreiner T, Schaefer UF and Loth H (2005) Immediate drug release from solid oral dosage forms. J Pharm Sci 94:120–133. Shin H-C, Landowski CP, Sun D, Vig BS, Kim I, Mittal S, Lane M, Rosania G, Drach JC and Amidon GL (2003) Functional expression and characterization of a sodiumdependent nucleoside transporter hCNT2 cloned from human duodenum. Biochem Biophys Res Commun 307:696–703. Sun D, Lennernas H, Welage LS, Barnett JL, Landowski CP, Foster D, Fleisher D, Lee KD and Amidon GL (2002) Comparison of human duodenum and Caco-2 gene expression profiles for 12,000 gene sequences tags and correlation with permeability of 26 drugs. Pharm Res 19:1400–1416.

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5 Evaluation of Permeability and P-glycoprotein Interactions: Industry Outlook Praveen V. Balimane and Saeho Chong

5.1

Introduction

New drug discovery and development is becoming an increasingly risky and costly endeavor. A recent report has tagged the final price of bringing a drug to the market at greater than a billion US dollars with an estimated research time running into multiple years (2004). Despite the considerable investment in terms of finance and resources, the number of drug approvals per year have held steady for the last few years. The advent of combinatorial chemistry, automation, and high-throughput screening (HTS) has afforded the opportunity to test thousands of compounds, but the success rate of progressing from initial clinical testing to final approval has remained disappointingly low. Greater than 90% of the compounds entering Phase-I clinical testing fail to reach the patients and as high as 50% entering Phase-III do not make the cut (Kola and Landis, 2004). Historically, drug discovery adopted a linear design; the new chemical entities were first selected on the basis of their pharmacological activity/potency followed by their sequential profiling to assess the absorption, distribution, metabolism, elimination, and toxicity (ADMET) characteristics. Such a strategy was generally more time and resource intensive and left little room for errors during the discovery process. Today, the new drug design effort integrates a parallel matrix approach where the pharmacological efficacy is screened in parallel with ADMET profiling, providing information in a timely manner to maximize the chance for selecting superior drug candidates with better quality for further development. Therefore, the availability of highly accurate, low cost and HTS techniques that can provide fast and reliable data on the developability characteristics of drug candidates is crucial. Screening a large number of drug candidates for biopharmaceutical properties (e.g., solubility, intestinal permeability, CYP inhibition, metabolic stability, and more recently drug–drug interaction potential involving drug transporters) has become a major challenge. Determination of permeability property

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Apical

1

2

3

4

5 6

7

Basolateral F IGURE 5.1. Multiple pathways for intestinal absorption of a compound. (1) passive, transcellular (2) active or secondary active, (3) facilitated diffusion, (4) passive, paracellular, (5) absorption limited by P-gp and/or other efflux transporters, (6) intestinal first-pass metabolism followed by absorption of parent and metabolite, (7) receptor mediated transport

and the drug–transporter interaction of drug candidates is fast becoming key characterization studies performed during the lead selection and lead optimization. Drug absorption across the intestinal membrane is a complex multipathway process as shown in Fig. 5.1. Passive absorption occurs most commonly through the cell membrane of enterocytes (transcellular route) or via the tight junctions between the enterocytes (paracellular route). Carrier-mediated absorption occurs via an active (or secondary active process) or by facilitated diffusion. Various efflux transporters such as P-gp, BCRP, MRP2 are also functional that could limit the absorption. Intestinal enzymes could be involved in metabolizing drugs to alternate moieties that might be absorbed. Finally, receptor mediated endocytosis could also play a role. Because of the multivariate processes involved in intestinal absorption of drugs, it is often difficult to use a single model to accurately predict the in vivo permeability characteristics. Currently, a variety of experimental models are available when evaluating intestinal permeability of drug candidates (Hillgren et al., 1995; Balimane et al., 2000, 2006; Hidalgo, 2001). A few commonly used models include: in vitro methods; artificial lipid membrane such as parallel artificial membrane permeability assay (PAMPA); cell-based systems such as Caco-2 cells, Mardin-Darby canine kidney (MDCK) cells, etc.; tissue-based Ussing chamber; in situ methods, intestinal single pass perfusion, and in vivo methods, whole animal absorption studies. Typically, a combination of these models is used routinely in assessing

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intestinal permeability. A tiered approach is often used, which involves highthroughput (but less predictive) models for primary screening followed by low throughput (but more predictive) models for secondary screening and mechanistic studies. PAMPA and cell culture-based models offer the right balance between predictability and throughput, and currently enjoy wide popularity throughout the pharmaceutical industry. Adequate permeability is required not only for oral absorption but also for sufficient drug distribution to pharmacological target organs (e.g., tumor, liver, etc.). In addition to simple passive diffusion across lipid bilayers, numerous transporters appear to play a critical role in selective accumulation and distribution of drugs into target organs. P-gp is one of the most extensively studied transporters that have been unequivocally known to impact the ADMET characteristics of drug molecules (Kim et al., 1999; Polli et al., 1999; Lin, 2003; Lin and Yamazaki, 2003). It’s role in influencing the pharmacokinetics of anti-cancer drugs has been extensively reviewed (Krishna and Mayer, 2000). It is a ubiquitous transporter, which is present on the apical surface of the enterocytes, canalicular membrane of hepatocytes, and on the apical surface of kidney, placenta and endothelial cells of brain membrane. Because of its strategic location, it is widely recognized that P-gp is a major determinant in disposition of a wide array of drugs in humans. The oral bioavailability of fexofenadine increased significantly when erythromycin or ketoconazole (well known inhibitor of P-gp) was co-administered in humans, suggesting P-gp as a permeability barrier at the absorption site (Simpson and Jarvis, 2000). Similarly, P-gp at the blood–brain barrier limits the entry of drugs into the brain. The biliary elimination of vincristine decreased significantly in the presence of verapamil (a known P-gp substrate/inhibitor) (Watanabe et al., 1992). Therefore, the early screening of drug candidates for their potential to interact with P-gp (either as a substrate or inhibitor) is becoming necessary and critical. There are various in vitro and in vivo models used for assessing P-gp interaction (Adachi et al., 2001; Polli et al., 2001; Yamazaki et al., 2001; Perloff et al., 2003). In vitro assays such as ATPase activity, rhodamine-123 uptake, calcein AM uptake, cellbased bidirectional transport, radio-ligand binding along with in vivo models such as transgenic (knockout mice) animals are often used to assess the involvement of P-gp. The cell-based bidirectional permeability assay is the most popular method for identification of P-gp substrate in drug discovery labs (Polli et al., 2001). This cell model provides the right balance of adequate throughput and functional utility. However, there are certain caveats that the user must be aware of in order to maximize the utility of the assay. In addition to P-gp, other pharmaceutically important drug transporters (e.g., MRP2, BCRP, OATP, OAT, OCT, etc.) can be examined using a plethora of models (e.g., transfected cell, vesicles, recombinant vaccinia, xenopus oocytes, etc.). A detailed discussion of these models is beyond the scope of this book chapter. This book chapter will focus on the various techniques that are currently used by drug discovery scientists in evaluating permeability/absorption and P-gp interaction potential of drug candidates. In particular, two experimental models (PAMPA and Caco-2 cells) will be discussed in detail with special emphasis on

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their complimentary aspects. PAMPA is used as the primary screening tool, and it is capable of providing the structure–permeability relationship that enables a successful lead optimization. Caco-2 cell model is often used as the secondary tool to perform in-depth mechanistic studies, to delineate the various pathways of absorption and to assess the P-gp interaction potential of drug candidates.

5.2

Anatomy and Physiology of the Small Intestine

The human small intestine is approximately 2–6 m and is loosely divided into three sections: duodenum, jejunum and ileum, which comprise 5, 50, and 45% of the length. The biological and physical parameters of human intestinal tract are listed in Table 5.1 (Carr and Toner, 1984; Daugherty and Mrsny, 1999). Approximately 90% of all absorption in the gastrointestinal tract occurs in the small intestinal region. The small intestine has projections that increase the potential surface area for digestion and absorption. Macroscopic valve like folds, called circular folds, encircling the inside of the intestinal lumen is estimated to increase the surface area of the small intestine threefold. Villi increase the area 30-fold and the microvilli increase it by a factor of 600. Thus, such unique structures lead to a tremendous increase in surface area available for absorption in the small intestine. The major role of the small intestine is the selective absorption of major nutrients and to serve as a barrier to digestive enzymes and ingested foreign substances. The epithelial cells in the intestinal region are a heterogeneous population of cells which include enterocytes or absorptive cells, goblet cells which secrete mucin, endocrine cells, paneth cells, M cells, tuft, and cup cells. The most common epithelial cells are the enterocytes or the absorptive cells. This cell is responsible for the majority of the absorption of both nutrients and drugs in the small intestine. It is polarized with distinct apical and basolateral membrane that are separated by tight junctions. Thus, the bulk of absorption takes place in the small intestine by various mechanisms such as passive diffusion (paracellular and transcellular) and carrier-mediated process (facilitated and active). TABLE 5.1. Biological and physical characteristics of human intestinal tract Gastrointestinal segment Stomach Duodenum Jejunum Ileum Colon and Rectum

Surface area (m2 ) 3.5 1.9 184 276 1.3

Segment length (cm) 0.25 ∼35 ∼280 ∼420 ∼150

pH of the segment 1–2 4.0–5.5 5.5–7.0 7.0–7.5 7.0–7.5

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Permeability Absorption Models Physicochemical Methods

For orally administered compounds, the systemic exposure depends on many different factors. In general, physicochemical properties such as molecular weight, pK a, lipophilicity, charge/ionization, solubility, gastrointestinal pH and molecular size are the major determinants of intestinal permeability. Aqueous solubility of the compound in the intestinal tract is also an important factor that dictates the dissolution characteristics of the compound eventually influencing the oral bioavailability. Physicochemical methods are attractive because of their high-throughput capacity, efficiency and reproducibility to predict passive diffusion. However, these models often lead to inaccurate prediction because of the lack of real physiological conditions that govern membrane permeability/absorption in vivo. 5.3.1.1 Lipophilicity (Log P/Log D) As a measure of drug–membrane interaction, lipophilicity is one of the most important physicochemical parameters in predicting and interpreting membrane permeability (Ho et al., 1977). In most of the early studies (Schanker et al., 1958; Houston et al., 1974; Dressman et al., 1985), the oral drug absorption was demonstrated to be dependent on the lipophilicity. Historically, the octanol–water partition coefficient (Log P) was accepted as a surrogate to biological systems for predicting absorption. But now it is widely recognized that use of Log P alone for predicting absorption is an over simplification of a complex process and often leads to inaccurate estimation. The transcellular diffusion of compounds from the luminal to serosal side in the intestinal epithelia involves the partitioning of the drug from the aqueous luminal region to nonpolar lipid bilayers of the cell membrane followed by the partitioning out from the lipid layers to the aqueous serosal region. Transport across biological membranes is a complex process that includes passive as well as carrier-mediated processes for influx and efflux. Log P values provide indirect information of the extent of passive transcellular transport possible for various drugs. For a structurally related series of compounds in which transport is largely mediated by a passive mechanism, the relationship between permeability and lipophilicity is generally bell shaped. However, for a diverse set of compounds in which parallel transport processes may be occurring in addition to the passive component, the correlation with lipophilicity is normally lacking (Ho et al., 1977). 5.3.1.2 Absorption Potential Dressman and colleagues (Dressman et al., 1985) proposed a parameter, Absorption Potential (AP), that incorporated the various basic physicochemical parameters in one single equation and was highly predictive of the extent of absorption in humans. So VL , AP = Log PFnon Xo

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where AP is the absorption potential, P is the octanol–water partition coefficient for the drug, Fnon is the fraction of drug nonionized at pH 6.5, So is the aqueous solubility of nonionized species at 37 ◦C, VL is the luminal volume (∼250 mL) and X o is the drug dose. A relatively good correlation was demonstrated between absorption potential vs. fraction absorbed in human subjects. There was a sigmoidal relationship observed between fraction absorbed and absorption potential. However, absorption potential does not account for carrier-mediated transport and thus is limited in its utility for predicting permeabilities only for passively transported compounds. 5.3.1.3 Immobilized Artificial Membrane (IAM) IAM is a chromatographic surface prepared by covalently immobilizing cell membrane phospholipids to solid surfaces at monolayer density. IAM chromatography column emulates the lipid environment of cell membranes (Pidgeon 1990a,b). IAM chromatography is experimentally simple and potentially capable of screening a large number of compounds. Pidgeon and colleagues have demonstrated the utility of this methodology as an accurate, cost effective and efficient predictor of permeability of test compounds. The predominant factor that regulates the passage of drugs across the gastrointestinal mucosa is their ability to passage through the lipid cell membranes. Log k derived from the IAM column showed reasonable correlation to drug partitioning into liposomes and permeability across Caco-2 cell monolayers. Various modifications of IAM have been studied by different investigators (Beigi et al., 1995; Yang et al., 1996; Stewart and Chan, 1998; Krause et al., 1999) and the results obtained have been shown to correlate with other parameters such as: partitioning into liposome membranes, Caco-2 cell permeability and intestinal absorption. The IAM methodology has been used to predict not only drug intestinal absorption but also solute partitioning into liposomes, brain uptake, and human skin permeability. But, it is important to recognize that lipid composition of various cell membranes in the body differs and is not necessarily consistent even within a given cell type under different conditions. Also, the artificial membranes themselves lack the paracellular pores that form an integral part of a biological membrane architecture and the transporter proteins that are involved in the carrier-mediated transport of drugs. Thus, even though these simplified artificial membranes might be capable of predicting the absorption of a series of compounds that are passively transported across cell membrane, it has severe limitations in screening compounds that are hydrophilic and small in size (candidates for paracellular transport) or predominantly transported by carrier proteins.

5.3.2

In Vitro Methods

Numerous in vitro methods have been used in the drug selection process for assessing the intestinal absorption potential of drug candidates. In vitro techniques for assessment of permeability are less labor and cost intensive compared to in vivo

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animal studies. One universal issue with all the in vitro systems is that the effect of physiological factors such as gastric emptying rate, gastrointestinal transit rate, gastrointestinal pH, etc. cannot be incorporated in the data interpretation. Each in vitro method has its distinct advantages and drawbacks. Based on the specific goal, one or more of these methods can be used as a screening tool for selecting compounds during the drug discovery process. The successful application of in vitro models to predict drug absorption across the intestinal mucosa depends on how closely the in vitro model mimics the characteristics of the in vivo intestinal epithelium. Although it is very difficult to develop a single in vitro system that can simulate all the conditions existing in the human intestine, various in vitro systems are used routinely as decision making tools in early drug discovery. 5.3.2.1 Animal Tissue-Based Methods It is extremely difficult to obtain viable human tissues for permeability studies on a regular basis. Since animal intestinal tissues are also made up of essentially the same kind of endothelial cells, permeability screening for drug discovery purposes is routinely carried out using various animal species. Excised animal tissue models have been used since the 1950s to explore the mechanism of absorption of nutrients from the intestine. Evidence on the uptake of glucose against a concentration gradient (Quastel, 1961) provided impetus for mechanistic studies involving excised tissues from the intestine of animals. However, the viability of the excised tissues is difficult to maintain since the tissues are devoid of direct blood supply and need constant oxygenation. Some of the more widely used methods for absorption and permeability studies are described below. Everted Gut Technique The everted gut technique has been in use from as early as the 1950s (Wilson and Wiseman, 1954) in the transport study of sugars and amino acids. Over the years modifications such as use of physiologically relevant tissue culture media, means to maintain constant oxygenation and improved incubation apparatus have all contributed to increased viability of ex vivo tissues leading to better predictability with everted tissue studies. This model is ideal for studying the absorption mechanism of drugs since both the passive and active transport can be studied. With the recent interest in the field P-glycoprotein activity in the gut, this model may be used to evaluate the role of efflux transporters in the intestinal absorption of drugs by comparing the transport kinetics of drug in the absence and presence of P-glycoprotein inhibitors or substrates. The everted gut model has an additional analytical advantage over other in vitro models because the sample volume on the serosal side is relatively small and drugs accumulate faster. Some of the disadvantages are the lack of active blood and nerve supply which can lead to a rapid loss of viability. In addition everting the intestinal tissue can lead to morphological damage causing misleading results.

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Ussing Chamber Transport studies across intestinal tissues from animals are also a widely used in vitro method to study drug absorption. This method involves the isolation of the intestinal tissues, cutting it into strips of appropriate size, clamping it on a suitable device and then the rate of drug transport across this tissue is measured. The utility of this in vitro device (Ussing Chamber) was first demonstrated by Ussing and Zerahn (1951). In this method, the permeability is measured based on the appearance of drug in the serosal side rather than the disappearance of drug in the mucosal side. The unique feature of this approach is that electric resistance of the membrane can be measured during the course of the experiment. The short circuit current across the membrane, as well as the resistance across the membrane, can be monitored. These parameters are routinely used as an indicator of the viability of the intestinal tissue during the transport studies with Ussing chamber. The apparent permeability coefficient (P) is estimated using the equation (Grass and Sweetana, 1989) dC V , P= AC0 dT where V is the volume of the receiver chamber, A is the exposed tissue surface area, C0 is the initial concentration drug in the donor chamber, and dC/dT is the change in drug concentration in the receiver with time. The Ussing chamber technique is an ideal method to study regional differences on the absorption of drugs by mounting intestinal tissues from various intestinal regions (Ungell et al., 1998). It is also possible to perform studies with human intestinal tissues thus providing a methodology to compare the permeability values across species. The amounts of drug required for the study are relatively small (mg quantity) and the collected samples are analytically clean which facilitates quantitative analysis. Apart from the disadvantages commonly associated with any in vitro studies, the drawbacks of this technique include: lack of blood and nerve supply, rapid loss of viability of the tissues during the experiment and changes in morphology and functionality of transporter proteins during the process of surgery and mounting of tissues. Isolated Membrane Vesicles Use of membrane vesicles isolated from numerous species (including humans) for transport characterization studies has been popular for some time now (Murer and Kinne, 1980; Hillgren et al., 1995; Sinko et al., 1995). Membrane vesicles prepared from intestinal tissues provide the flexibility of examining the interaction of drugs to a specific membrane of interest (e.g., brush-border membrane vs. basolateral membrane of enterocytes). Vesicles offer a unique opportunity to study the properties of drug and nutrient transport at the cellular level (brush-border as well as basolateral side). Membrane vesicle studies allow a complete manipulation of solute environment both inside and outside of the vesicle, thus making it an ideal system for mechanistic absorption studies. Studies with vesicles also

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facilitate isolation and identification of transporter proteins that are specifically expressed either on the brush-border or the basolateral side of the membranes. Compared to the other conventional in vitro techniques (Ussing chamber and intestinal perfusions), the vesicles studies are much better suited when availability of sufficient quantities of drug becomes difficult. Drug uptake study in membrane vesicle can be performed with a small amount of drug (mg quantity), and typically multiple experiments can be performed with vesicles prepared from a single laboratory animal. A unique advantage of this method is that vesicles can be cryopreserved and used for a long duration. Thus the vesicle system offer unique experimental capabilities not possible with more integrated methodologies. However, prepared vesicles are not pure and very often contain other membrane or organelle fragments. It is practically not feasible to isolate 100% pure brush-border membrane vesicles (or basolateral vesicles) without the contamination with the other type of vesicles. During the process of isolation of vesicles often leads to damage of the transporter proteins and enzymes. Since the volume of the vesicles is very small, they typically require a sensitive analytical method. 5.3.2.2 Cell-Based Methods Varieties of cell monolayer models that mimic in vivo intestinal epithelium in humans have been developed and currently enjoy widespread popularity. Unlike enterocytes, human immortalized (tumor) cells grow rapidly into confluent monolayers followed by a spontaneous differentiation providing an ideal system for transport studies. Table 5.2 lists a few of these cell models commonly used for permeability assessment in drug discovery and development. Caco-2 Cells and Others Caco-2 cells have been the most extensively characterized and useful cell model in the field of drug permeability and absorption for over a decade now (Arturrson, TABLE 5.2. Cell culture models commonly used for permeability assessment Cell Line

Species/origin

Special characteristics

Caco-2

Human colon adenocarcinoma

MDCK

Mardin-Darby canine kidney epithelial cells Pig kidney epithelial cells

Most well-established cell model, differentiates and expresses some relevant efflux transporters, expression of influx transporters variable (lab-to-lab) Polarized cells with low intrinsic expression of ABC transporters, ideal for transfections Polarized cells with very low intrinsic transporter expression, ideal for transfections Temperature-sensitive, ideal for paracellularly absorbed compounds (leakier pores) Similar to Caco-2 Contains mucus producing goblet cells Provides a size selective barrier for paracellularly transported compounds

LLC-PK1 2/4/A1 TC-7 HT-29 IEC-18

Rat fetal intestinal epithelial cells Caco-2 sub-clone Human colon Rat small intestine cell line

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1991; Artursson et al., 1996; Rubas et al., 1996; Hidalgo, 2001; Balimane and Chong, 2005a). Caco-2 cells, a human colon adenocarcinoma, undergo spontaneous enterocytic differentiation in culture. When they reach confluence on a semi-permeable porous filter, the cell polarity and tight junctions are well established. In the last 10–15 years, there has been a tremendous growth in the use of Caco-2 cells for mechanistic studies and as a rapid in vitro screening tool in support of drug discovery within the pharmaceutical industry. The predictability and utility of this model to rank order a large number of compounds in terms of absorption potential have been demonstrated conclusively by several investigators (Artursson and Karlsson, 1991; Rubas et al., 1993). The recent trend in many pharmaceutical research laboratories is to completely automate Caco-2 cell permeability screen using robotics. A fully automated Caco-2 cell system allows much greater throughput in the order of 500–2,000 compounds studies per month without a proportional increase in resources. The use of 24-well monolayer (cell surface area ∼0.33 cm2 ) coupled with the use of LC-MS/MS significantly reduced the amount of compound (no more than 50 µg) required to perform permeability experiment in this model. A more in-depth discussion on the Caco-2 cell permeability model along with its strengths and weaknesses is discussed later in the chapter. TC-7 is one of the sub-clones isolated from Caco-2 cells. Gres and colleagues (Gres et al., 1998) have reported comprehensive comparison of TC-7 to its parental Caco-2 cells. TC-7 clone had very similar cell morphology as seen in Caco-2 cells: presence of brush-border membrane and microvilli, and formation of tight junctions. Although permeability of mannitol and PEG-4000 was identical in both cell lines, TC-7 had a significantly higher transepithelial electrical resistance (TEER) value at 21 days in culture and beyond. Permeability values of passively absorbed drugs obtained in TC-7 clone correlated equally well as in parental Caco-2 cells to the extent of absorption in humans. 2/4/A1 cells is a modified cell line reported (Tavelin et al., 2003) to have been generated from fetal rat intestine. It is touted to better mimic the permeability of the human small intestine especially with regards to passive transcellular and paracellular drug transport. This immortalized cell line forms viable differentiated monolayers with tight junctions, brush-border membrane enzymes as well as the transporter proteins. Since the tight junctions in the Caco-2 cell line appear unrealistically tighter than the tight junctions in the endothelial cells in human intestine, the 2/4/A1 cells were proposed as a better model to study passively transported compounds via paracellular route. The TEER value in the 2/4/A1 cells reached a plateau of 25 cm2 compared to a plateau of 234 cm2 in the Caco-2 cells. The transport rate of poorly permeable compounds (e.g., mannitol and creatinine) in 2/4/A1 monolayers was comparable to that in the human jejunum, and was up to 300 times faster than that in the Caco-2 cell monolayers, suggesting that a cell line like 2/4/A1 will be more predictive for compounds that are absorbed via paracellular route.

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MDCK Cells MDCK is another cell line that is widely utilized for permeability assessment. These cells differentiate into columnar epithelial cells and form tight junctions (like Caco-2 cells) when cultured on semi-permeable membranes. The use of MDCK cell line as a model to evaluate the intestinal transport characteristics of compounds was first discussed in 1989 (Cho et al., 1989). More recently, Irvine and colleagues (Irvine et al., 1999) investigated the use of MDCK cells as a tool for assessing the membrane permeability properties of early drug discovery compounds. MDCK cells were grown on transwell-COL membrane culture inserts at high density and cultured for 3 days. Apparent permeability values of 55 compounds with known human absorption values were determined in MDCK cell system. For comparison purposes, the permeability of the same compounds was also determined using Caco-2 cells. The authors reported that the permeability obtained with MDCK cells correlated equally well (as with permeability determined in Caco-2 cells) to human absorption. Given the fact that Caco-2 cells are derived from human colon carcinoma cells whereas the MDCK cells are derived from dog kidney cells, it is very likely that the expression level of various transporters may be grossly different in these two cell lines. Species difference should therefore be kept in mind before using MDCK cells as a primary screening tool for permeability in early drug discovery. One major advantage of MDCK cells over Caco-2 cells is the shorter cultivation period (3 days vs. 3 weeks). A shorter cell culture time becomes a significant advantage considering reduced labor and reduced down time in case of cell contamination.

5.3.3

In Situ Methods

In situ perfusion of intestinal segments of rodents (rats or rabbits) is frequently used to study the permeability and absorption kinetics of drugs. The biggest advantage of the in situ system compared to the in vitro techniques discussed earlier is the presence of an intact blood and nerve supply in the experimental animals. Various modifications of the perfusion technique have been studied by different investigators: single pass perfusion (Komiya et al., 1980), recirculating perfusion (Van Rees et al., 1974), oscillating perfusion (Schurgers and DeBlaey, 1984), and the closed loop method (Doluisio et al., 1969). Lennernas and colleagues (Lennernas et al., 1997; Lennernas, 1998) have extended the perfusion studies to humans. This methodology is found to be highly accurate for predicting the permeability of passively transported compounds, however the use of a scaling factor has been recommended for predicting permeability of carrier-mediated compounds. In situ experiments for studying intestinal drug absorption involve perfusion of drug solution prepared in physiological buffer through isolated cannulated intestinal segments. Absorption is assessed based on the disappearance of drug from the intestinal lumen. Hydrodynamics of the flow of buffer through the intestinal segments can influence the absorption characteristics of the drugs due to the effect of unstirred water layer. In this technique the difference in the concentration of the

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inlet and outlet flow is used to calculate the permeability. The presence of an intact blood supply, nerve and clearance capabilities at the site of absorption lead to an excellent experimental system which mimics the in vivo condition. Also, the input of compounds can be closely controlled with respect to concentration, pH, flow rate, intestinal region, etc. In the single pass perfusion approach the net effective permeability (Peff ) is calculated as:

−Q in Ln CCoi Peff = , 2πRL where Ci and Co are the inlet and outlet concentrations at steady state of the compound in the perfusate, Q in is the inlet perfusion flow rate, and 2πRL is the mass transfer area available for absorption in the intestinal cylinder with length L and radius R. Despite its advantages, the use of single pass perfusion method is severely limited because this method relies on the disappearance of compound from the luminal side as an indication of absorption, but the rate of decrease of concentration in the perfusate does not always represent the rate of absorption of the drug into the systemic circulation (especially for compounds undergoing presystemic or luminal metabolism). The method is also limited because of its cost factor as it requires a large number of animals to get statistically significant absorption data. Relatively high amounts of test compounds are also required to perform studies (>10 mg), which is not feasible in early drug discovery. It was also demonstrated that the surgical manipulation of intestine combined with anesthesia caused a significant change in the blood flow to intestine and had a remarkable effect on absorption rate (Uhing and Kimura, 1995).

5.3.4

In Vivo Methods

Extrapolation of animal permeability/absorption data to humans should be performed with caution because of potential species differences. Based on the similarity of the composition of epithelial cell membranes of the mammals, and the fact that absorption is basically an interaction between the drug and the biological membrane, permeability across the gastrointestinal tract should be similar across the species. However, there are physiological factors such as pH, GI motility, transit time, and differential distribution of enzymes and transporters that can affect the absorption leading to species variability. Excellent reviews and discussions (Dressman, 1986; Kararli, 1995; Lin, 1995) discuss species similarity and differences in ADME, with an attempt to address the question on limitations in extrapolating data from animals to humans. Despite the fact that it is extremely resource intensive, in vivo evaluation of drug absorption in laboratory animals is a commonly used method to predict the extent of absorption of drug candidates in humans. Comparison of AUC (plasma concentration vs. time curve) values after intravenous, oral and intraportal (or intraperitoneal) administration can often indicate the absolute extent of in

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vivo absorption. More common practice for higher throughput in vivo absorption screening is that limited number of blood samples (3–4 time points up to 6 or 8 h) are collected after oral administration. As a result, a larger number of drug candidates can be ranked on the basis of drug concentration in the systemic circulation. It should be recognized that truncated pharmacokinetic studies (with reduced sampling times) are not optimal and the results obtained from such a study may not be accurate. However, the method is capable of differentiating well absorbed compounds from poorly bioavailable compounds.

5.3.5

In Silico Methods

Computational or virtual screening has received much attention in the last few years. In silico models which can accurately predict the membrane permeability of test drugs based on lipophilicity, hydrogen bonding capacity, molecular size, polar surface area, and quantum properties has the potential to specifically direct the chemical synthesis and therefore, revolutionize the drug discovery process. Such an in silico predictive model would minimize extremely time consuming steps of synthesis as well as experimental studies of thousands of test compounds. Lipinski et al. (1997) proposed an in silico computational method for qualitatively predicting the developability of compounds. The “rules of five” proposed by them predicted lower permeabilities for compounds with more than five H-bond donors, ten H-bond acceptors, with molecular weight greater than 500 and c Log P > 5. Using this completely empirical model, useful permeability predictions were achieved for closely related analog series of compounds. Quantitative structure property relationship (QSPR) has been recommended to predict human intestinal absorption without the need for actual compound synthesis. QSPR methods have been used to model physicochemical, chromatographic, and spectroscopic properties of compounds (Rubas et al., 1993; Artursson and Borchardt, 1997). Several computational methods have been described to predict the intestinal absorption parameters based on factors such as polar surface area, molecular surface area, dynamic surface area, etc. But most reports involve in silico modeling studies performed on compounds closely related in structure thus making the model ineffective when applied to a wider structurally diverse data set. It is desirable to have predictive QSPR models that covers a diverse set of compounds with respect to their properties (e.g., physicochemical and pharmacological) as well as chemical structures. Stenberg et al. (1999) compared the utility of three different predictive models for intestinal absorption. They demonstrated that molecular surface descriptors and descriptors derived from quantum mechanics were much more useful than the simple “rule of five” as the predictor of intestinal absorption. These in silico methods have a great potential as virtual screens in testing permeability of test drugs. The utility of polar molecular surface area (PSAd) as a predictor of intestinal absorption was demonstrated by Clark (1999). Similarly, the evaluation of the dynamic PSAd as a predictor of drug absorption was performed by Palm et al. (1998). PSAd of the compounds was calculated from all low energy

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conformations identified in molecular mechanics calculations in vacuum and in simulated chloroform and water environment. PSAd was determined to be a better predictor compared to the octanol–water partition coefficient or the experimentally obtained immobilized liposome chromatography retention time. Wessel et al. (1998) proposed an in silico method that used the molecular structural descriptors to predict the human intestinal absorption of drugs. Topological descriptors (based on 2D information of the compound), electronic descriptors (partial atomic charge and dipole moment), geometric descriptors (surface area, volume, etc.), and hybrid descriptors (combination of molecular surface area and partial atomic charge) were used for analysis. Based on the root-mean-square errors seen in the training set as well as the study set, it was demonstrated that there was a correlation between structure and intestinal absorption. The data set in the study included 86 structurally diverse compounds and the results confirm the potential of applying QSPR methods for estimating absorption. With further improvements in the choice of the descriptors, this in silico method can be used as a potential virtual screen in drug discovery processes. To date, one major critical impediment to a successful in silico modeling is the lack of a sufficiently large data base with reliable information. Also, the in silico methods, even at their best are not as reliable as real experimental data for predicting the permeability and absorption characteristics of compounds. However, in spite of the limitations, there are commercially available software packages (QMPRplus and Oraspotter) that incorporate the in silico methodology for predicting in vivo human absorption numbers. One draw-back of these modeling efforts is that most published work is based on small sets of permeability data (90%). But the permeability of these drugs when inappropriate pH was used is comparable to drugs that are not absorbed at all. Thus, it appears that an appropriate donor pH should be used depending on the physicochemical properties of test compounds. The degree of pH dependency is often very difficult to estimate because the pK a of a large number of drug candidates cannot be measured experimentally due to resources limitation. To minimize the occurrence of false negatives (i.e., classified as a poorly permeable when it is not), it becomes necessary to run the assay at two different pH condition (low of pH ∼5 and high pH ∼7.4) simulating the dynamic pH environment in the intestine to capture true intrinsic permeability. Because of a higher throughput potential, the PAMPA model can be used in the first tier pH dependent permeability study for similar chemotypes. Once an optimum pH is identified, all subsequent studies (including cell-based, tissue-based studies) can be carried out at that pH. However, it should be recognized that the permeability studies performed at multiple pH would negatively impact the throughput and cost involved.

5.4.4

PAMPA and Caco-2 Cell: Caveats

5.4.4.1 Transporter- and Paracellular-Mediated Absorption PAMPA is a high-throughput, non-cell-based permeability model that provides estimates of the passive transcellular permeability property. The lack of any functional drug transporters and paracellular pores in PAMPA makes it an inappropriate model for compounds that are absorbed via transporter- and pore-mediated processes. However, the lack of transporter- and pore-mediated permeability might be an advantage of the PAMPA model. Because the PAMPA provides an

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PAMPA permeability (nm/sec)

800

600

400

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0 −2

−1

1

2

3

4

5

Log D (pH 7.4)

F IGURE 5.5. Correlation of PAMPA permeability with lipophilicity. Log D (pH 7.4) value was calculated using ACD/Log D module

uncontaminated transcellular passive permeability data, it could be more useful in constructing the structure–permeability relationship at the chemistry bench. Lipophilicity (most commonly expressed as Log P or Log D values) plays a major role in passive diffusion. An adequate lipophilicity is required for a permeant to travel across the phospholipid membrane. However, as shown in the Fig. 5.5, the PAMPA permeability of 22 marketed drugs (listed in Table 5.3) did not demonstrate any correlation with lipophilicity alone. As expected, several other factors (e.g., polar surface area, molecular volume/flexibility, hydrogen bonding, etc.) in addition to lipophilicity are involved in dictating the overall passive permeability of any compound. Although pharmaceutically important drug transporters (e.g., PEPT1, OCT, OAT, etc.) are functionally expressed in Caco-2 cells (Sun et al., 2002; Anderle et al., 2004; Behrens et al., 2004), they are quantitatively under-expressed when compared to in vivo situation. For example, beta-lactam antibiotics (e.g., cephalexin, amoxicillin) and ACE inhibitors, that are known unequivocal substrates of dipeptide transporters, are poorly permeable across the Caco-2 cell monolayer despite the fact that they are completely absorbed in vivo (Chong et al., 1996). This model is likely to generate false negatives with drug candidates that are transported by carrier-mediated process. Caco-2 cells have tight junctions that are significantly tighter compared to human intestine, and it does not differentiate drugs that are absorbed primarily via paracellular pathway. The low molecular weight hydrophilic compounds (e.g., metformin, ranitidine, atenolol, furosemide, hydrochlorothiazide, etc.) showed poor permeability (i.e., equal or less than mannitol) in Caco-2 cells despite adequate absorption (greater

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than 50% of dose) in humans. Therefore, models such as PAMPA and Caco-2 cells can only serve as a one-way screen such that compounds with high permeability in these models are typically well absorbed, however, compounds with low permeability cannot be ruled out as poorly absorbed compounds in humans. 5.4.4.2 Incomplete Mass-Balance Due to Nonspecific Binding Nonspecific drug binding to plastic devices and cells during the permeability study is a common problem which often makes the data interpretation difficult. Permeability is calculated based on several factors: the amount of drugs appeared in the receiver compartment, the initial concentration in the donor compartment, and the surface area of the physical barrier (e.g., lipid bilayers and cell monolayer). When significant drug loss occurs during the incubation due to a nonspecific binding, two things occur (1) the concentration in the donor compartment (a driving force) is reduced and (2) the concentration in the receiver compartment is artifactually reduced. This will lead to an underestimation of permeability estimates, and potentially lead to false negatives. “Cacophilicty” or “membrane retention” has been used as a term to describe the drug binding to Caco-2 cell monolayer, and the significant binding results in an incomplete recovery. Incomplete recovery is particularly common with lipophilic drug candidates. A few approaches may be able to minimize the nonspecific binding. Rather than using the initial donor concentration, the final donor concentration at the termination of incubation can be used. Assumption is that the nonspecific binding occurs relatively quickly, therefore, the final donor concentration is a better estimate for the concentration gradient between two compartments. For certain drug candidates with poor recovery, the results can be significantly different depending on the calculation method used. Another approach involves addition of serum proteins (bovine serum albumin in case of Caco-2 cells) or surfactants (in case of PAMPA) to the receiver compartment to minimize nonspecific binding, therefore, improving the assay recovery and overall predictability of the model (Aungst et al., 2000; Krishna et al., 2001; Saha and Kou, 2002). 5.4.4.3 Inadequate Aqueous Solubility Drug candidates at the early discovery stage are often optimized in terms of SAR around potency and pharmacological activity, and the SAR generally lead to rather lipophilic and poorly soluble candidates (solubility in aqueous buffer 1%), the cell tight junction is easily compromised making the data interpretation difficult or impossible (Dimitrijevic et al., 2000; Rege et al., 2001, 2002). Consequently, for some discovery programs, the permeability data cannot be provided for a majority (>95%) of new compounds because they tend to come from a similar chemotype which may share a problematic

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PAMPA permeability (nm/sec)

500 10% DMSO 3% DMSO 1% DMSO

400

300

200

100

e

n

in fe af C

N

or

flo

tip

yr

xa

in

ci

e

ne lfa

Su

et am D

ex

sa

ha

la

zi

so

ro op et M

An

l lo

e id os op Et

ne

F IGURE 5.6. Effect of DMSO on the PAMPA permeability

pharmacophore in terms of aqueous solubility. PAMPA model has a significant advantage over Caco-2 cells in this regard. Figure 5.6 shows the effect of various concentrations of DMSO on the PAMPA permeability of some probe compounds (high and low Pc compounds). Contrary to a cell-based model, where higher than 1% DMSO would compromise the cell monolayer leading to unreliable results, the PAMPA permeability was consistent in the presence of DMSO up to 10%. Having higher cosolvent concentration not only increases the percentage of compounds that can be successfully studied in a permeability assay, but it also improves the mass-balance recovery. A higher cosolvent concentration is likely to minimize the physical loss (e.g., nonspecific binding to devise and membrane) during the permeability study. 5.4.4.4 Other Experimental Variability It is well documented that the Caco-2 cell permeability of the same set of drugs obtained from different laboratories varies significantly (Walter and Kissel, 1995; Ungell, 2004; Balimane and Chong, 2005a). There are a variety of factors that can influence the outcome. Minor differences in cell culture conditions (e.g., seeding density, feeding frequency and composition of the cell media, etc.), experimental protocol (e.g., initial concentration of drugs, composition of the permeability buffer, pH and monolayer washing steps, etc.) and age of the cells (e.g., passage number, culture duration and tightness of junction) can produce dramatic

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differences in the permeability values. In addition, the function of drug transporters expressed in the cell-based models can fluctuate significantly with difference in culture conditions (Walter and Kissel, 1995; Anderle et al., 2004). PAMPA permeability model, on the other hand, is a relatively rigid model less prone to interlaboratory variability problems. The lipid solution used to create the bilayers membrane is the only component of the model that can incorporate fluctuations in permeability value. Moreover, since PAMPA captures only the passive diffusive permeability and is devoid of any transporter proteins, other experimental factors play a negligible role.

5.5

P-gp Studies Using Caco-2 Cells

The bidirectional permeability assay, where the basolateral to apical (secretory direction, B to A) permeability is compared to the apical to basolateral (absorptive direction, A to B) permeability, is regarded as the gold standard in identifying P-gp substrates because it is functionally the most direct method of measuring efflux characteristics of drug candidates. Compounds with an efflux ratio (ratio of B to A/A to B) greater than 2–3 are typically considered as P-gp substrates. However, it is well known that efflux transporters other than P-gp (e.g., MRP2, BCRP, etc.) are also functionally expressed in the Caco-2 cells. Therefore, a simple bidirectional difference may not ascertain that the compounds being tested is indeed P-gp substrates. As a confirmatory study, a follow-up bidirectional experiment is routinely repeated in the presence of known inhibitors of P-gp, BCRP and MRP2. GF120918 at 2–4 µM is often used to selectively inhibit the P-gp-based efflux transport. However, some recent publications have reported that GF120918 interacts not only with P-gp but also with the BCRP (Maliepaard et al., 2001; Woehlecke et al., 2003). Similarly, MK-571 and FTC are used to selectively inhibit the MRP2 (Chen et al., 1999; Dantzig et al., 1999) and BCRP activity (Volk and Schneider, 2003; Zhang et al., 2004), respectively. Comparison of efflux ratio in the absence and presence of these specific inhibitors can delineate the potential role of the individual efflux transporter. Because it is a cell-based assay with a physical barrier (lipid bilayer), the test compounds must have adequate cell permeability for the bidirectional P-gp assay. One major drawback of the bidirectional permeability assay is that the P-gp substrates with insufficient transcellular permeability cannot be identified. Drugs such as famotidine and ranitidine are substrates for secretory transporter proteins (Lee et al., 2002) but often they fail to be detected as P-gp substrates due to their low passive permeability. Several efflux (e.g., P-gp, MRP2, and BCRP) and influx transporters (e.g., PEPT1) are expressed in Caco-2 cells under standard cell culture conditions. However, the functional expression of drug transporters in Caco-2 cells may vary significantly depending on the passage number and minor changes in culture conditions. For example, the efflux ratio of digoxin or sulfasalazine (both suggested to be efflux transporter substrate) varied drastically with passage number. It is interesting to note that often the lack of P-gp functional expression becomes

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Caco-2 cell permeability (nm/sec)

180 160 140 120

Sulfasalazine Mannitol Metoprolol

100 80 60 40 20

28 Pa

ss

ag

e

#

26 ag ss Pa

ag ss Pa

e

# e

#

24

22 # e ag ss Pa

Pa

ss

ag

e

#

20

F IGURE 5.7. Effect of Caco-2 cell passage number on the permeability of sulfasalazine, mannitol, and metoprolol

problematic sporadically. There was no identifiable trend with the change in passage number. Figure 5.7 demonstrates that the levels of P-gp expression in Caco-2 cells can vary significantly depending on the cell passage number. Mannitol (a hydrophilic paracellular marker) and metoprolol (a lipophilic transcellular marker) demonstrated consistent permeability values over the different passage numbers. The reproducibility for these two compounds confirms that the cell monolayers are not compromised. However, sulfasalazine, a P-gp substrate that typically has low A to B permeability value (120 nm/s) for only certain passage numbers. The variability in the levels of expression of P-gp was directly reflected by the variability in the permeability values of sulfasalazine. Similarly, the levels of P-gp expression have also been known to fluctuate widely amongst the wells even in a single passage study. Digoxin efflux ratio can vary from low of 8 to a high of 20 in different wells of the same passage number Caco-2 cells highlighting the variability in the expression of efflux transporter. Because of inconsistent P-gp functional expression in Caco-2 cells, the cell line which over-express efflux transporters might be a better in vitro tool to examine the drug–transporter interactions. The engineered cell lines selectively express the transporter of interest and facilitate interaction studies with a specific transporter in isolation. Bidirectional studies performed in these cell lines (over-expressing only one transporter) would specifically demonstrate the

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involvement of a single transporter without any interplay with other transporters. Follow-up studies can be conducted in the presence of selective inhibitor to further confirm the involvement of the transporter (viz: transporter phenotyping). MDCK and LLC-PK1 cell lines stably transfected with a specific efflux transporter (e.g., MDR1, mdr1, MRP2, and BCRP) may be used to tease out transporter interactions at early discovery stage. The functional expression of transporters in these cell lines appears to be more stable compared to Caco-2 cells.

5.5.1

Experimental Factors Effecting Efflux Ratio

Currently there is no universally accepted standard study protocol for conducting the cell based bidirectional permeability assay. Different laboratories perform these studies under different experimental conditions that often showed a large interlaboratory variability. Standardization of experimental conditions (e.g., pH, drug concentration and duration of incubation, etc.) can provide better consistency of the results and significantly minimize the occurrence of false negatives (i.e., unable to identify a true substrate). The use of an optimal substrate concentration is a key parameter for attaining accurate results from this model. Performance of bidirectional studies at high concentrations (50 µM or more) often lead to saturation of the efflux transporter and result in efflux ratio of unity even for well-known P-gp substrates. Figure 5.8 demonstrates the dramatic effect of using the lower (3 µM) concentration for improving the utility of this model. Classical 12

10

50 uM 3 uM

Efflux Ratio

8

6

4

2

0 Verapamil

Quinidine

BMS-1

BMS-2

F IGURE 5.8. Effect of substrate concentration (3 µM vs. 50 µM) on the efflux ratio (ratio of B to A/A to B) in the Caco-2 cell bidirectional study. The efflux ratio was calculated using the mean data. The coefficient of variation in the permeability data (both direction) is typically less than 25% amongst replicates

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P-gp substrates such as verapamil, quinidine and two internal research compounds are shown to have efflux ratio (ratio of B to A/A to B) of ∼1 when studied at 50 µM. However, when they were studied at lower concentration of 3 µM, they demonstrated unequivocal efflux characteristics with efflux ratio >2. The P-gp is saturable at high substrates concentrations and thus at 50 µM, the P-gp is knocked out functionally leading to false negative data. Using lower substrate concentrations (< 5 µM) could potentially minimize the saturation of the efflux transporters and can significantly improve the predictability of the assay. It should be recognized that lowering the substrate concentration imposes an analytical challenge, and one must make sure that the analytical technique is capable of measuring the sample concentrations with adequate accuracy. The preliminary screen at a low substrate concentration is an efficient approach to evaluate a large number of compounds and maintain sufficient throughput. However, “yes or no” classification may not be very useful because it is difficult to quantify (if possible) the clinical significance of the potential role of P-gp in the drug disposition. As a follow-up study, it is recommended that a range of substrate concentrations to be tested, and the estimated Km value can be related to in vivo relevant drug concentrations either in the intestine (during absorption) or systemic circulation (during distribution and excretion). One of the common limitations in conducting concentration dependency experiment is the lack of adequate aqueous solubility of new drug candidates (i.e., poor solubility does not allow a wide range of concentration to be tested). Another potential issue with cell-based models is that the integrity of tight junctions in the Caco-2 cell monolayers can be compromised (i.e., becomes leaky) when incubated with test compounds, and the extent of damage is typically concentration dependent. The permeability across the compromised monolayer is often much higher compared to the intact monolayers, and the permeability becomes artificially high in both direction. In that case, the efflux ratio often becomes unity, and the test compound is classified as a nonsubstrate even if it is a true substrate (again false negative). To detect cell damage during the incubation, one can measure TEER value before and after the study. But the change in the TEER value is not very sensitive to detect a minor cell damage. More definitive way to detect cell damage is to coincubate a paracellular route marker such as radiolabeled mannitol in the test run. It is also possible to see false positives. It appears that the basolateral membrane of Caco-2 cell monolayer is more sensitive than the apical membrane (i.e., the cell damage occurs more frequently in the B to A direction than the A to B direction). When this occurs, the B to A permeability is significantly greater than A to B permeability, and the test compound is classified as a P-gp substrate even if it is a nonsubstrate (false positives). Figure 5.9 shows that the cell damage due to BMS-X is concentration dependent because the mannitol permeability was significantly higher when incubated with 50 µM compared to 10 µM BMS-X, and it also illustrated that the only basolateral membrane was sensitive to BMS-X. When the substrate concentration was reduced to 10 µM, the B to A permeability was reduced and became similar to A to B permeability. Therefore, lowering the substrate concentration is a good way

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A to B

Caco-2 cell permeability (nm/sec)

B to A

120 100 80 60 40 20

X)

X)

S-

S-

BM

BM ni an M

M

an

ni

to

to

l(

l(

w

w

ith

ith

50

10

µM

µM

X SBM

BM

S-

X

(5

(1

µM

)

µM

)

F IGURE 5.9. Effect of drug concentrations on the integrity of Caco-2 cell monolayer

to minimize the potential cell damage, and consequently the incidence of both false negatives and false positives. Another experimental factor that needs to be fully optimized is the pH used in the bidirectional permeability studies. Optimization of the apical pH is not as straightforward as the substrate concentration. Based on the pKa of the test compounds, the change in apical pH can lead to dramatically different extent of ionized and unionized fractions in the apical compartment and consequently significant changes in the permeability value. Figure 5.10 demonstrates the effect apical pH on the efflux ratio observed for well-known P-gp substrates. Digoxin, a neutral P-gp substrate, had no ionization changes with pH and showed a uniform efflux ratio ∼10 at all three pH. However, saquinavir, a weak base, showed a significant change in the efflux ratio where the ratio was greater than 50 at an apical pH of 5.5 as compared to the ratio of ∼10 at pH 7.4. On the contrary, sulfasalazine (contains a carboxylic acid) demonstrated a higher efflux ratio at an apical pH of 7.4. The higher efflux ratio observed with saquinavir and sulfasalazine at an apical pH of 5.5 and 7.4, respectively, is primarily due to lower apical to basolateral permeabilities (i.e., smaller denominator in the ratio calculation) rather than the changes in the basolateral to apical permeability. Therefore, the apical pH needs to be optimized depending on the type of ionizable function group of the compounds of

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pH 6.5 pH 7.4

Efflux Ratio

50 40 30 20 10 0 Digoxin

Saquniavir

Sulfasalazine

F IGURE 5.10. Effect of apical pH (5.5, 6.5 and 7.4) on the efflux ratio of acidic, basic and neutral P-gp substrates in the Caco-2 cell bidirectional study. The efflux ratio was calculated using the mean data. The coefficient of variation in the permeability data (both direction) is typically less than 25% amongst replicates

interest. Prior information on the pKa and acidic/basic nature of the compound will be very useful in the P-gp substrate assay. Other experimental variables such as incubation duration, composition of transport buffer, presence/absence of serum, cell-culturing conditions, etc. need to be calibrated using a set of known P-gp substrates. Factors such as culturing conditions, composition of media, cell plate architecture (12-well vs. 24-well Transwell) all have a significant effect on the expression of P-gp and the interlaboratory variability is often significantly large. Therefore, a direct comparison of the results obtained from different laboratories should be done cautiously.

5.6

Conclusions

One of the most important challenges facing the pharmaceutical industry at present is to develop high-throughput, cost effective and highly predictive screening models of drug absorption that can be used during the decision making process early in drug discovery. Although physicochemical parameters such as lipophilicity, charge, molecular weight, etc. are often used as initial indicators of absorption, they are often not reliable because of their inability to incorporate the physiologic conditions. On the other hand, the conventional experimental methods for assessing the absorption characteristics are not streamlined to keep pace with the

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large number of compounds synthesized by combinatorial chemistry. The available experimental models have various pros and cons and their judicious use can increase the likelihood of progressing drugs with favorable oral absorption characteristics. Physicochemical and/or in silico methods that are rapid and require no usage of animal tissues should be used as the primary screening models in early drug discovery stage. Subsequently, automated high-throughput in vitro systems (cell culture-based or animal tissue-based) can be used in selecting and optimizing the chemical leads to identify potential drug candidates. Multiple complementary screening models involving in situ and in vivo methods should also be utilized simultaneously at this stage to minimize “false-positives” and “false-negatives” from getting into the development process. Caco-2 cells and PAMPA are two valuable research tools that are currently the method-of-choice for screening compounds for absorption and P-gp interaction potential. Despite the popularity and acceptability of PAMPA and Caco-2 cell models, it is important to recognize the caveats associated with these models to fully realize their potential. Standardization of experimental variables to develop a “uniform” methodology is an important first step prior to implementation of these models in high-throughput mode. Calibration of the models with appropriate reference probes (known marketed drugs as well as internal research compounds) is essential to maintain the validity of the model. These reference compounds should cover a broad structural and physicochemical space and should cover a range of solubility/permeability. Other probes to represent paracellular and efflux (P-gp) transport are also recommended. These reference compounds should be included as quality control set in every test run performed with unknown compounds. Like all other in vitro models, both PAMPA and Caco-2 cells have strengths and weaknesses. A thorough understanding of the rationale underlying the caveats (such as low recovery, solubility, etc.) associated with these models could certainly help putting the results in the proper perspectives. The results obtained from these assays should not be interpreted in isolation but should be assessed in conjunction with solubility and stability characteristics of the compounds. Finally, it is important to realize that simplistic in vitro models like PAMPA and Caco-2 cells are inadequate to represent the complicated absorption machinery in the human intestinal tract. Ultimately, it is the judicious use of these models in combination with in vivo studies that would enhance our ability to predict the drug disposition in humans and ensure that only drug candidates with a high-developability potential are moved forward into the development pipeline.

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6 Excipients as Absorption Enhancers Hans E. Junginger

6.1

Introduction

Excipients or auxiliary materials are used to formulate a delivery system for a drug to achieve optimal therapeutic effects. They should be able to deliver the drug at the right place at the right time and with the right dose with the optimal delivery characteristics. Basically, an excipient has to fulfill the same safety profile as a drug with the exception that it should not exert a therapeutic effect. Most excipients do comply with these requirements and do have the GRAS (generally regarded as safe, a system used by the US FDA) status when used in those amounts which are normally used to fabricate a drug delivery system. However, various substances that are commonly used as excipients do not show complete inertness, but may have additional effects on the tissues of the absorption sites which are not intended, because the excipients are primarily used for another purpose. Many surfactants are used in a drug delivery system either as a wetting agent or as a lubricant. Others are used as a solubilizer for poorly soluble drugs. Depending on their unique structure as amphiphilic compound combining hydrophilic and lipophilic characteristics those compounds also may interact with the tissue of the mucosal surfaces and change (transiently) their structure allowing for improved absorption of especially hydrophilic and high molecular weight drugs. In order to improve the bioavailability of poorly absorbable drugs the nuisance of these surfactant compounds has turned into their primary intended action as absorption enhancers. However, in many cases there is a direct relationship between absorption enhancing effect and toxicity of the used low molecular weight surfactants as transmucosal absorption enhancers, which excludes them for therapeutic use and hampers their commercialization especially if the poorly absorbable drug is meant for chronic use. According to Barry (1983), an ideal absorption enhancer should have the following desirable attributes: •

The absorbing enhancing action should be immediate and unidirectional, and the duration of the effect should be specific, predictable, and suitable.

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After removal of the material from the applied membrane, the tissue should immediately fully recover its normal barrier property. The enhancer should show no systemic and toxic effects. The enhancer should not irritate or damage the applied membrane surface. The enhancer should be physically compatible with a wide range of drugs and pharmaceutical excipients. The enhancer should be applicable for chronic use.

As evident from the above points, we are currently far from having an absorption enhancer available that fulfills all the requirements. Furthermore, no knowledge is available about the long-term application of such low molecular weight absorption enhancers. Although a lot of information is available in the literature about the efficacy and safety profile of most of the surfactants to be used as absorption enhancers, little knowledge is still available about the possible interference of these compounds with the inherent transporter systems of the cells. Such effects may become crucial when a biowaiver is granted for a drug substance that is predominantly actively absorbed via transporter systems and when the innovator product may use excipients that do not interfere with these transporter systems. When an excipient used in the multi source (generic) product does or when a different excipient used in the other formulation, these may account for strong differences in drug absorption. There have been many attempts to search for new and safer absorption enhancers especially with respect to improving absorption of hydrophilic compounds with high molecular weight such as peptides, insulin, calcitonin, etc. These have become available in the last two decades in sufficient and affordable amounts due to the progress in biotechnology and which are used for chronic therapy mostly by injection. The development of suitable alternative delivery systems (for the nasal, buccal, rectal, vaginal, ocular, and peroral route) (yet) have not kept pace with the availability of endogenous peptides because of the lack of suitable absorption enhancers for this class of substances. However, it turned out rather surprisingly that special polymers, which show mucoadhesive properties, also are able to act as safe penetration enhancers for improved drug absorption of especially hydrophilic (peptide) drug substances. This chapter aims to classify the existing types of absorption enhancers according to their mode of action, and to introduce the new categories of polymeric absorption enhancers for hydrophilic compounds.

6.2 Basic Mechanisms in Transcellular and Paracellular Transport Mammalian cell membranes separate cells from their environment and from one another. Cell membranes of the mucosal enterocyte linings consist of phospholipid bilayers in which proteins for signal transfer are incorporated.

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F IGURE 6.1. Transport routes across nasal respiration mucosa: (1) paracellular across tight junctions, (2) transcellular, and (3) transcytotic. Mucus secreting goblet cells (G), ciliated columnar cells (C), and tight junctions (Tj) are represented. Basal cells (B) are located on the basal lamina (Bl) adjacent to the lamina propria (Lp) with blood vessels. With permission from Junginger and Verhoef (1998)

These cell membranes are barriers to most polar compounds and also to macromolecules, but they are relatively permeable to water and small hydrophobic molecules. Methods to facilitate transport of molecules, either small or large, across epithelial cells can be categorized into two major groups: transcellular and paracellular transport (Hayashi et al., 1997, Fasano, 1998) as highlighted in Fig. 6.1.

6.2.1

Transcellular Transport

As reviewed before, basic mechanisms of transepithelial transport of drugs include passive transport of small molecules, active transport of ionic and polar compound, and endocytosis and transcytosis of macromolecules (Fig. 6.1). Small and nonionic molecules usually cross cell monolayers by passive transport. The rate at which a molecule diffuses across the lipid bilayer of cell membranes depends largely on the size of the molecule and its relative lipid solubility. In general, the smaller and more lipophilic the molecule is, the more rapidly it will diffuse across the bilayer. However, cell membranes are also permeable to some small watersoluble molecules such as ions, sugars, and amino acids (Elsenhans et al., 1983.) Passive transport is the movement of a solute along its concentration gradient. The passive transcellular transport of hydrophilic compounds, including macromolecules such as peptides, can be enhanced by interaction of the

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absorption-enhancing materials with both the phospholipid bilayer and the integrated proteins, thereby making the membrane more fluid and thus more permeable to both lipophilic and hydrophilic compounds.

6.2.2

Paracellular Transport

Paracellular transport is the transport of molecules around or between cells (Fig. 6.1). Tight junctions or similar interconnections exist between cells. At the level of tight junctions, cell membranes are brought into extremely close apposition, but are not fused, so as to occlude the extracellular space (Fig. 6.2). Because proteolytic activity is thought to be deficient in the paracellular space (in contrast to the presence of cytosolic enzymes in the transcellular space), the investigation of paracellular transport for hydrophilic compounds in general and for peptides and proteins in particular has recently become of great interest (Fasano, 1998). However, most of the commonly used absorption enhancers improve both the transcellular and paracellular pathways, albeit in different ratios (Junginger and Verhoef, 1998).

6.2.3

Mechanisms of Action of Absorption Enhancers

Absorption promoters (penetration enhancers) can influence the mucosa in different ways: • • •

By acting on the mucous layer By acting on the membrane components By acting on the tight junctions

F IGURE 6.2. Tight junctions are intercellular connections that hold epithelial cells together at their apical end (left). An enlargement of a tight junction is also presented (right). With permission from Junginger and Verhoef (1998)

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6.2.3.1 Action on the Mucus Layer The mucous layer covering the cell surface of the mucosa can be seen as an unstirred layer, acting as a barrier to the diffusion of drug molecules (Marriott and Gergory, 1990). The main components of the mucous layers include water (up to 95% by weight), mucin (generally no more than 5% by weight), inorganic salts (about 1% by weight), carbohydrates, and lipids (Marriott and Gregory, 1990). Mucin represents more than 80% of the organic components of mucus (Lichtenberger, 1992) and controls the gel-like structure (Marriott and Gregory, 1990). Mucins are O-linked glycoproteins (Strous and Dekker, 1992). From a polymer science viewpoint, mucins are block copolymers with branched and unbranched blocks. Both types of the blocks have protein backbone chains, but the branched blocks have highly branched oligosaccharide chains attached to them (Peppas and Huang, 2004). Ionic surfactants have been found to be able to reduce the mucus viscosity and elasticity (Martin et al., 1978). On the other side absorption of drugs across any mucosal tissue may involve interactions with the mucus gel overlying the tissue. Bhat et al. (1996) demonstrated that drug binding to the mucus glycoproteins is nonspecific in nature with similar types of binding forces and can reduce the amount of free drug available for absorption. In addition, mucoadhesive polymers are thought to interfere with the mucous layer, first by covering the mucous surface and then by interpenetration of the mucous network (Lehr et al., 1992c; Lehr et al., 1993; Peppas and Huang, 2004). As a consequence of both mechanisms, absorption enhancers are thought to reduce the barrier function of the mucus layer and increase drug permeability. On the other side, Schipper et al. (1999) could show that the binding of the mucoadhesive polymer chitosan (cf. Sect. 6.3.3.3) to the epithelial cell surface and subsequent absorption enhancing effect of the hydrophilic drug atenolol were significantly reduced in mucus producing and covered cell cultures (HT29-H cultures). When the mucus layer was removed prior to the addition of chitosan, the cell surface binding and absorption-enhancing effects of the chitosans were increased. It is suggested that the only modest absorption-enhancing effect of chitosan with mucus can be overcome by increasing the local concentrations of both chitosan and drug, i.e., through formulation of the chitosan into a particulate dosage form. 6.2.3.2 Action on Membrane Components As already mentioned, the membranes of epithelial cells contain phospholipids and proteins. The hydrophobic interaction between the acyl chains of lipid molecules results in the formation of a well-organized phospholipid bilayer. These ordered bilayers are poorly permeable to both macromolecules and highly polar low molecular weight compounds. Numerous studies have shown that absorption enhancers can increase the permeability of membranes by affecting biological membrane components such as proteins and lipids. In an early but very important review Swenson and Curatolo (1992) reported on methods for the enhancement of the oral absorption of polar drugs,

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including polar peptides and proteins. The enhancers reviewed are bile salts, anionic detergents, nonionic detergents, medium chain glycerides, salicylates, acyl amino acids, acyl carnitines, lysolecithin, ethylene diamine tetraacetic acid (EDTA), and various particulate systems. Also Anderberg and Artursson (1992) studied a series of surfactants registered in solid oral drug products. The effects of anionic sodium dodecyl sulfate (SDS) and nonionic (polysorbate 80 and polyoxyl 40 hydrogenated castor oil) surfactants as well as the effects of bile salts (sodium taurocholate, sodium taurodeoxycholate, and sodium taurodihydrofusidate (STDHF)) on epithelial permeability and integrity were studied using Caco-2 cell monolayers. It was observed that all surfactants demonstrated a concentration-dependent effect on the permeability of hydrophilic markers. However, the effects of anionic surfactants were more pronounced compared to those of nonionic absorption enhancers. Altered cell morphology and cell membrane damage were observed after exposure to SDS, STDHF, and polysorbate 80. It was also shown that the absorption enhancers increased the permeability of marker molecules via the paracellular and transcellular routes. The most likely mechanism by which low molecular weight absorption enhancers promote drug absorption is by solubilizing the phospholipids and membrane proteins, and thus increasing membrane permeability (Lichtenberg et al., 1983). In addition, surfactants, bile salts, and fatty acids influence both the transcellular and paracellular routes of absorption (Table 6.1). As most of the examples of the absorption enhancers given also possess (to some extent) surfactant-like properties, they might mix with the phospholipid bilayer of the membrane and thus be partly absorbed. This absorption could result not only in strong membrane damage, but also in toxic side-effects due to penetration interfering with cell organelles (Junginger and Verhoef, 1998). A special class of absorption enhancers for hydrophilic compounds are cyclodextrins, which are cyclic oligosaccharides of six, seven, or eight d-glycopyranose units, denoted as α-, β-, and γ- cyclodextrins, respectively. The three-dimensional ring structure of these compounds resembles a truncated cone, of which the internal cavity has slight hydrophobic properties and the outer surface is hydrophilic. Because of these structural features, cyclodextrins can form inclusion complexes with lipophilic drugs, thereby increasing their solubility in aqueous solutions (Uekama et al., 1982; Szejtli, 1988). Just recently Mannila and coworkers (2005) studied the effects of randomly methylated β-cyclodextrin on the sublingual bioavailabililty of various cannabinoids in rabbits and found increased uptake in the systemic circulation. Furthermore, cyclodextrins (particularly methylated β-cyclodextrins) can enhance the nasal absorption of peptide drugs such as insulin, although marked interspecies differences have been reported (Verhoef et al., 1994). The mechanism of action of methylated β-cyclodextrins as absorption enhancers for hydrophilic drugs is probably by transiently changing the mucosal permeability (by extraction and inclusion of membrane cholesterol) and opening of the tight junctions (Marttin et al., 1997; Junginger and Verhoef, 1998).

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TABLE 6.1. Classes of absorption enhancers and their mechanisms of action Class

Examples

Mechanism

Transport ways

Surfactants

Na-laurylsulfate Polyoxyethelyneg-laurylether Bile salts: Na-deoxycholate Na-glycocholate

Phospholipid acyl chain petrubation

Transcellular↑ Paracellular↑

Fatty acids Cyclodextrins

Chelators

Na-taurocholate Oleic acid Short fatty acids α-, β- and γ-cyclodextrin Methylated β-cyclodextrins EDTA Polycrylates

Positively charged polymers

Chitosan salts Trimethyl chitosan

Reduction mucus viscosity Peptidase inhibition Phospholipid acyl chain petrubation Inclusion of membrane compounds

Transcellular↑ Paracellular Transcellular↑ Paracellular↑

Complexation of Ca2+

Transcellular↑ Paracellular↑ Paracellular↑

Opening of tight junctions Ionic interactions with negatively charged groups of glycocalix

Paracellular↑

Potential Excipient Effect on Bioavailability The Biopharmaceutics Classification System (BCS) (Amidon et al., 1995) allows waivers of in vivo bioequivalence for rapidly dissolving immediate-release (IR) formulations of drugs with high solubility and high permeability. One potential issue in possibly extending BCS biowaivers to low-permeability drugs is the potential for excipients to modulate the intestinal permeability of the drug. Rege et al. (2001) investigated the effects of common excipients on Caco-2 transport of such low-permeability drugs. The effects of nine individual excipients (lactose monohydrate, hydroxypropyl methyl cellulose (HPMC), sodium lauryl sulfate (SLS), EDTA, Tween 80, docusate sodium (dioctyl sodium sulfosuccinate), propylene glycol, poly(ethylene glycol 400), and anhydrous cherry flavor) on the Caco-2 permeability were investigated using seven low-permeability compounds that differ in their physical properties. With the exception of SLS no excipients affected Caco-2 cell monolayer integrity. SLS moderately increased the permeability of almost all the drugs. Tween 80 significantly increased the apical-tobasolateral directed permeability of furosemide, cimetidine, and hydrochlorothiazide, presumably by inhibiting their active efflux, without affecting mannitol permeability. Additionally, docusate sodium moderately increased cimetidine permeability. Other excipients did not have a significant effect on the permeability of these drugs. In another study, Rege et al. (2002) investigated the transporter inhibition activity of three nonionic surfactant (Tween 80, Chromophor EL, and vitamin E

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TPGS) on P-glycoprotein, the human intestinal peptide transporter, and the monocarboxylic acid transporter in Caco-2 monolayers. Additionally they evaluated the role of membrane fluidity of protein kinase C in surfactant-induced transporter inhibition. All three surfactants inhibited P-glycoprotein (P-gp). Tween 80 and Cremophor EL increased apical-to-basolateral permeability and decreased basolateral-to-apical permeability of the P-gp substrate rhodamine 123. The effect of vitamin E TPGS was equally large, but essentially only reduced the basolateralto-apical permeability on rhodamine 123. These P-gp inhibition effects would appear to be related to these excipients’ modulation of membrane fluidity, where Tween 80 and Cremophor EL fluidized cell lipid bilayers, while vitamin E TPGS rigidized the bilayers. However, among the three surfactants, only Tween 80 inhibited the peptide transporter, as measured by glycyl sarcosine permeability. A common functional feature of these three surfactants was their ability to modulate fluidity, although the results indicated that even strong membrane fluidity modulation alone was not sufficient to reduce transporter activity. Protein kinase C inhibitor failed to affect rhodamine 123 and glycyl sarcosine permeability, suggesting that protein kinase C inhibition was not the mechanism of transporter inhibition. These results suggest that surfactants, which are absorption enhancers, can inhibit multiple transporters, but that changes in membrane fluidity may not be the generalized mechanism to reduce transporter activity. In a summary of a workshop report on BCS – Implementation Challenges and Extension Opportunities (Polli et al., 2004) it is stated: The BCS guidance indicates that excipients that are currently in US FDAapproved IR solid oral dosage forms will generally not affect the rate and extent of absorption of a highly soluble and highly permeable drug substance that is formulated in a rapidly dissolving IR product. Quantities of excipients should be consistent with intended function. The guidance further indicates that when new excipients or atypically large amounts of common excipients are used, there needs to be a documentation of the absence of an excipient effect. The FDA has recently made available to the public the Inactive Ingredients Database, which lists inactive ingredients in FDA-approved drug products. The Inactive Ingredients Database can be accessed at http://www.accessdata.fda.gov/scripts/cder/iig/index.cfm. This list is searchable by ingredient name. Each database cites for each route/dosage form containing the inactive ingredient. Given the consensus to extend potential biowaivers to include drugs whose fraction dose absorbed is less than 85%, with perhaps some lower limit (e.g., 40%), excipient effects were discussed as a potential concern. There was no consensus on this potential concern. There was general acknowledgement that most products only employ common excipients in typical quantities. There was some level of support for the expectation that common excipients in typical quantities do not modulate permeability or gastrointestinal transit of low permeability drugs. This viewpoint was countered by the growing understanding of the role of transporters in drug permeation and disposition, including transporter-mediated drug interactions. There was consensus that dose linearity extending sufficiently above

6. Excipients as Absorption Enhancers

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the highest dose strength is a basis to conclude that excipients in such studies do not represent a significant risk for the drug. Potential Excipient Effect on Tight Junctions The intercellular tight junction is one of the major barriers to the paracellular transport of macromolecules and polar compounds (Denker and Nigam, 1998). They have two physiological functions: first, they constitute the principal barrier to passive movement of fluid, electrolytes, macromolecules, and cells through the paracellular pathway (the “gate” function), and secondly, they contribute to transepithelial transport of compounds promoting epithelial cell polarity (Madara, 1987; Citi, 1992; Citi and Denisenko, 1995). Tight junction’s structure and permeability can be regulated by many potential physiological factors, including the concentration of cyclic AMP (cAMP) (Duffey et al., 1981), intercellular calcium concentration (Palant et al., 1983), and transient mucosal loads (Madara et al., 1986). Several studies have shown that one of the possible mechanisms of penetration enhancers is to loosen the tight junctions of epithelial membranes thereby increasing the paracellular transport of poorly absorbable drugs (Gonzales-Mariscal and Nava, 2005). Hence the issue of tight junction’s regulation by absorption enhancers appears to be crucial for macromolecular drug absorption. In a study by Thanou (2000), the tight junction’s membrane protein occludin was visualized by immunocytochemistry staining in the presence and absence of Trimethyl Chitosan 60 (TMC60) (degree of quaternization 60%) using confocal laser scanning microscopy (CLSM). Additionally, the effects of TMC60 on cytoskeletal F-actin were determined by visualization using CLSM. The transmembrane protein occludin displayed a disrupted pattern after incubation with 1.0% (w/v) TMC60, suggesting that the interaction of TMC60 with the tight junction’s protein is the major mechanism for opening of the tight junctions and subsequent increased paracellular permeability. These observations were quite similar to images of Caco-2 cells with 0.1% (w/v) chitosan, but the effect appeared to be stronger than for the reported 0.1% (w/v) chitosan. Chitosan treated cells showed a thickened pattern of occludin at the cell periphery and not a disrupted one, which might be due to the ten-fold difference in concentration or to an effect exclusively related to the quaternized derivative of chitosan, TMC60. Additionally it was observed that TMC60 provoked a redistribution of the cytoskeletal F-actin, a phenomenon that appeared to correlate well with the opening of epithelial tight junctions. Calcium depletion by chelating agent (e.g., EDTA, EGTA) has been reported to increase paracellular permeability. These agents induce general changes in the cell physiology such as disruption of actin filaments and adherence junctions, diminished cell adhesion, and activation of protein kinases (Citi, 1992). It was proposed that EGTA provokes alterations on the tight junctions, being a consequence of its effects on Ca2+ dependent adhesion molecules (which are concentrated in adherence junctions), through a contraction of the junction-associated microfilament cytoskeleton (Citi and Denisenko, 1995). It has been demonstrated that serosal

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rather than apical Ca2+ levels play a more important role in this process (Collares et al., 1994). Basolateral Ca2+ levels vary and a particular chelation enhancer cannot accomplish full depletion of the calcium ions from the adherence junction to provoke the paracellular widening. Therefore, the approach of using chelating agent as permeation enhancers leads to variable results, even in controllable in vitro conditions like the Caco-2 cell system (LeCluise and Sutton, 1997). Functional polymers such as polyacrylic acid derivatives and chitosan (derivatives) appear to be a valuable alternative solution to increase exclusively the paracellular permeation and absorption of hydrophilic drugs. Being high molecular weight and hydrophilic polymers it is assumed that their intrinsic absorption and related to this their toxicity is minimal and they are not expected to show systemic adverse side effects (Junginger and Verhoef, 1998; Thanou et al., 2001c). These functional polymers will be discussed in the next sections of this chapter.

6.3 6.3.1

Mucoadhesive Polymers as Absorption Enhancers Theories of Mucoadhesion

Various theories have been worked out in the last decades that explain the mechanisms with which mucoadhesives adhere to the mucous layer. The theories of mucoadhesion are primarily based on the classical theories of metallic and polymer adhesion. Four main theories exist that describe the possible mechanisms of mucoadhesion: the electronic, the adsorption, the wetting, and the diffusion theory. •

The electronic theory assumes that transfer of electrons occurs between the mucus and the mucoadhesive due to differences in their electronic structures (Derjaguin et al., 1977, 1994). The electron transfer between the mucus and the mucoadhesive polymer leads to the formation of a double layer of electrical charges at the interface of the mucus and the mucoadhesive with the result of attraction forces inside the double layer. The adsorption theory is based on the attraction forces between the mucus and the mucoadhesive. The attraction is achieved via molecular bonding caused by secondary forces such as hydrogen and van der Waals bonds (Kinloch, 1979, 1980; Gu et al., 1988; Mikos and Peppas, 1989; Chickering and Mathiowitz, 1999). The resulting attractive forces are considerably larger than the forces described by the electronic theory. The wetting theory correlates the surface tension of the mucus and the mucoadhesive with the ability of the mucoadhesive to swell and spread on the mucus layer and indicates that interfacial energy plays an important role in mucoadhesion (Good and Girrfalco, 1960; Helfland and Tagami, 1972; Kaelble and Moacanin, 1977; Peppas and Buri, 1985). By calculating the interfacial energy from the individual spreading coefficients of the mucus and mucoadhesive or by calculating a combined spreading coefficient, good predictions about the mucoadhesive performance can be obtained (Lehr et al., 1992a, 1993). The

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wetting theory has the most impact on the mechanism of mucoadhesion since spreading of the mucoadhesive over the mucus (and vice versa) is a prerequisite for the validity of all other theories. The diffusion theory was proposed first by Voyutskii (1963) and assumes that both the mucoadhesive surface and the mucoadhesive polymer come in the first step in contact to each other. In a second step it is postulated that both the mucin polymers and the mucoadhesive polymer chains interpenetrate each other with subsequent physical entanglement and hydrogen bonds. The interpenetration has to be sufficiently deep in order to become substantial and is dependent on the molecular weight, degree of crosslinking, chain length, flexibility, and spatial conformation of the polymers (Kinloch, 1980; Park and Robinson, 1985; Mikos and Peppas, 1986; Ponchel et al., 1987; Duchˆene et al., 1988; Peppas and Stahlin, 1996). Jabbari et al. (1993) and Peppas and Huang (2004) were the first to introduce the interdiffusion theory in mucoadhesion. It was proposed that in an aqueous environment the free polymers have enough mobility to diffuse. After intimate contact of the mucus and the mucoadhesive carrier, the free polymer chains, which are initially in the mucus or mucoadhesive parts, may diffuse across the interface due to a chemical potential gradient. After a period of time, the diffused chains form effective interaction sites in the interfacial region. Desai et al. (1992) experimentally estimated the diffusion coefficients of certain proteins in the porcine mucus on the order of 10−7 cm2 /s. The diffusion coefficients of free mucins were about 10−8 cm2 /s in mucus while aggregated mucins have diffusion coefficients of 10−11 to 10−12 cm2 /s (Bansil et al., 1995). In addition to their low diffusion coefficients, the dynamics of polymer chain diffusion across the interface is rather complex (Wool, 1995). First visualization studies (plastic sections of freeze substituted samples) showed the mucoadhesive interface as an irregular borderline with many coves and invaginations, but were sharp rather than hazy. While with light microscopy mucus glycoproteins could be identified unambiguously by specific histochemical reactions, there was no evidence for intermixing using plastic sections of freeze substituted samples. Hence, the interpenetration depth at the mucus/polymer interface may not be in the micron scale but in the nanorange. (Lehr et al., 1992c). Though direct observations of free chain interpenetration in the interface between mucus and mucoadhesives are not possible, recent experimental observations support the interdiffusion contribution to adhesion. Jabbari and coworkers (1993) proved mucin interpenetration at the poly(acrylic acid)/mucin interface using ATR-FTIR spectroscopy. Their results showed clearly that the concentration of mucin inside the PAA gel increases with time. Sahlin and Peppas (1996) used near-field FTIR microscopy to study the free PEG chains diffusion across PAA hydrogel. The diffusion process was confirmed and the diffusion coefficients were on the order of 10−8 to 10−9 cm2 /s.

None of these theories give a complete description of the mechanisms involved in mucoadhesion. The total phenomenon of mucoadhesion most probably is a combination of all these theories. Some investigators divide the mucoadhesion process

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into sequential phases, each of which is associated with a different mucoadhesion mechanism (Lee et al., 2000; Solomonidou et al., 2001; Dodou et al., 2005): First, the polymer gets wet and swells (wetting theory). Then, noncovalent (physical) bonds are created within the mucus–polymer interface (electronic and absorption theory). Finally, the polymer and protein chains interpenetrate (diffusion theory) and entangle together to form subsequently noncovalent (physical) and covalent (chemical) bonds (electronic and adsorption theory). It may become clear that the mechanisms of mucoadhesion are of utmost importance for the effectiveness of mucoadhesive polymers, which are intended to act as absorption enhancers for improved drug absorption. There are two important aspects to consider: first the residence time of a mucoadhesive (particulate) drug delivery system after attachment to the mucus according to the mechanisms discussed above for prolonged drug delivery, and second the interpenetration of the mucoadhesive polymers into the mucus layers covering the absorptive mucosal tissues, and their interactions with the sugar residues of the glycocalix in order to elicit a response reaction which results in the transient opening of the tight junctions and allows a paracellular transport of the hydrophilic drug molecules along this route. These aspects will be discussed in the following sections.

6.3.2

Material Properties of Mucoadhesives

Mucoadhesives are characterized by material properties that contribute to good adhesiveness according to one or more theories of mucoadhesion. Such material properties are the ability to swell, their ability to form molecular bonds with the mucus layer, and their spatial conformation due to the entanglement of chains. The creation of molecular bonds and the entanglement of chains result in changes in their rheological behavior of the mucoadhesive polymers. The rheological properties of mucoadhesives can therefore be used as an indication of the extent of molecular bonding and spatial conformation. The cohesiveness of mucoadhesives contributes indirectly to their adhesive ability, since it deals with the internal strength of the mucoadhesive. Dodou et al. (2005) have discussed and revisited these properties of mucoadhesive polymer: •

Swelling

The ability of mucoadhesive polymers to swell is a prerequisite for mucoadhesion since it concerns wetting, uncoiling, and spreading of the polymer over the mucus (wetting theory). This spreading process, controlled by the interfacial properties of the mucus and mucoadhesive, allows intimate contact at the mucus–mucoadhesive interface, thus governing the formation of bonds (Lehr et al., 1992a, 1993). Overhydrating of the polymer, however, may result in a slippery mucilage, deteriorating mucoadhesion (Mortazavi and Smart, 1993). Furthermore, swelling is a key-parameter for the environment-sensitive drug delivery (Qiu and Perk, 2001), where controlled drug release can be obtained by a reversible volume change of an environmental-sensitive polymer with controlled swelling–deswelling properties (Gutowska et al., 1997).

6. Excipients as Absorption Enhancers •

151

Molecular bonding

The presence of suitable molecular groups in the mucoadhesives leads to the formation of covalent bonds (e.g., disulfide bonds), as well as noncovalent bonds (e.g., ionic, hydrogen, and van der Waals bonds) with the mucus layer. These molecular bonds contribute considerably to good adhesion, according to the electronic and the adsorption theory. The advantage of covalent bonds may be that they are stronger than the noncovalent bonds, which may result in higher mucoadhesive forces (Bernkop-Schn¨urch and Steiniger, 2000). However, covalent bonds require time to be created, whereas noncovalent bonds are formed immediately as soon as the mucus and the mucoadhesive polymer come into contact. The delay time that is required for covalent bonding does not play an impeding role for such drug delivery systems, in which maintaining the delivery system at a particular location for an extended period of time (about 3 h in gastrointestinal delivery) is advantageous (Lee et al., 2000; Junginger et al., 2002), whereas a desired longer residence time at the intestinal gut surface is most likely not possible as the turnover time of mucus is estimated to be in the order of 47–270 min in the rat (Lehr et al., 1991). •

Spatial conformation

The interpenetration rate of the mucus–mucoadhesive chains depends on the diffusion coefficient and the chemical potential gradient of the interacting macromolecules (Huang et al., 2000). The flexibility and mobility of the mucoadhesive chains as well as the expanded form of the mucoadhesive network control the effective chain length which can penetrate into the mucus (Lee et al., 2000; Peppas and Huang, 2004). In this way spatial conformation is critical for the interpenetration of mucus–mucoadhesive chains. •

Rheological properties

The chain entanglement and the molecular bonding that occur between the mucus and the mucoadhesive lead to changes in the rheological behavior of the two materials (Rossi et al., 2001). Since changes in the rheological properties reflect the degree of interaction between mucus and mucoadhesive, rheological methods constitute a common way to evaluate the strength of mucoadhesion. Mucoadhesive systems with a high elastic component showed good mucoadhesiveness (Tamburic and Craig, 1997). Moreover, a high viscosity and viscoelasticity of the mucus–mucoadhesive system indicates improved cohesiveness and resistance to deformation (Madsen et al., 1998). A number of authors (Huang et al., 2000; Madsen et al., 1998; Rossi et al., 1994; Caramella et al., 1994) found experimentally that the viscosity of the mucus–mucoadhesive system can be larger as the sum of the separate viscosities. This phenomenon is called “rheological synergism.” High rheological synergism indicates extensive chain entanglement (diffusion theory) and thus good mucoadhesiveness. •

Cohesiveness

Mucoadhesives exhibit high adhesiveness at their interface with the mucus layer, but should exhibit sufficient cohesiveness as well in order to prevent internal

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fracture of the (swollen) mucoadhesive polymers. Solid forms of mucoadhesives show in general satisfying cohesiveness. Another aspect of the correlation between cohesiveness and mucoadhesive ability has been pointed out by H¨agerstr¨om et al. (2000). Together with his coworkers he investigated the mucoadhesiveness of common polymers to several kinds of mucins. The results show that a too high interaction between the polymer and the mucins led to weakening instead of strengthening of the internal gel structure, since the increased interaction at the interface disturbed the internal cohesive structure of the polymer network. In a recent investigation, Accili and collaborators (2004) showed that the polyacrylates Carbopol 974P and Pharmacoat 606 showed different mucoadhesive properties depending on which type of mucus (sublingual, esophageal, and duodenal bovine) they were brought in contact to. The significantly different behavior of the two polymers was correlated with the desquamation layer thickness and the differential sialic acid and fucose expositions in the targeted mucosae. A number of solid mucoadhesive dosage forms for drug delivery in the gastrointestinal tract, such as tablets, micro- and nanoparticles, granules, pellets, and capsules have been already studied in vitro and in vivo in 1992 (Duchˆene and Ponchel, 1992). They showed satisfying mucoadhesive properties, although a correlation between in vitro and in vivo performance of the delivery systems cannot always be made. Until today no reliable mucoadhesive drug delivery system for intestinal application is available. This is mainly due to the fact that the mucoadhesive polymers have to first swell in order to obtain their mucoadhesive properties. Second, the particulate or single unit dose delivery systems have to get attached to the mucous linings of the gut, which is a randomly occurring process during which most of the delivery systems already come in contact with soluble mucus (fragments), amply available in the gut fluids, and which adhere also to the mucoadhesive surface of the drug delivery systems, hence deactivating the mucoadhesive properties of the drug delivery systems before they are able to reach the mucosal surface of the gut. In this case, most of the drug is delivered into the intestinal liquids rather than to the mucosal gut surface and most of the (peptide) drug is degraded and lost for drug absorption.

6.3.3

Classes of Mucoadhesive Polymers

6.3.3.1 Polyacrylates The term polyacrylates includes synthetic, high molecular weight polymers of acrylic acid (polyacrylic acid or PAA) (Fig. 6.3a) that are also known as Carbomers. They are either linear or (weakly) crosslinked (either by allyl sucrose (Carbomers) or divinyl glycol (Polycarbophils)) polymers that are broadly applied in pharmaceutical and cosmetic industry (mostly as excipient for controlled drug release for oral dosage forms and as stabilizers for gels). Crosslinked Carbomers, manufactured by the Performance Materials Segment of the BF Goodrich company under the commercial name Carbopols and Polycarbophils (PCPs), are also used as mucoadhesive platforms for drug delivery. Carbopols and PCPs have

6. Excipients as Absorption Enhancers

(a)

(b)

O

O

HO

HOCH2

OH

(c)

ONa

CH2

O CHCH2

n

O OH

CH2

(d)

O

O O

CH2OH OH

O

NH

HO O

O

n

OCH3

O

HOCH2

O

153

n

CH2OH

NH2

ONa

(e)

(f )

SH

SH

O CONH2+

SH

COOH O

HOCH2

NH

HO

HOCH2

O

NH

O

O O CHCH2

n

OH

NH2

CH2OH

NH

HO O

n

O

O O OH

NH2

n

OH2CH

F IGURE 6.3. Structural formulas of mucoadhesives: (a) poly(acrylic acid) (PAA), (b) sodium carboxymethylcellulose (NaCMC), (c) chitosan and carboxylic methylester derivative, (d) thiolated polymer of PAA, (e) chitosan–thioglycolic acid (TGA) conjugate, and (f) chitosan-4-thio-butyl-amidine (chitosan-TBA) conjugates. The dashed boxes indicate the side groups that are responsible for mucoadhesiveness. With permission from Dodou et al. (2005)

received extensive review and toxicological evaluation. The PCPs and calcium PCPs are classified as category 1 GRAS materials (Goodrich, 2002). Polyacrylates interact with mucus by hydrogen and van der Waals bonds, created between the carboxylic groups of the polyacrylates and the sulfate and sialic acid residues of mucin glycoproteins (Dodou et al., 2005). However, polyacrylates do possess also properties as absorption enhancers for the paracellular absorption of hydrophilic compounds as peptides and are additionally able to inhibit the activities of enzymes present in the intestinal fluid (Lueßen et al., 1996a). Polyacrylates as Absorption Enhancers The absorption across rat intestinal tissue of the model peptide drug 9desglycinamide, 8- L-arginine vasopressin (DGAVP) from bioadhesive formulations was studies by Lehr et al. (1992b) in vitro, in a chronically isolated intestinal loop in situ and after intraduodenal administration in vivo. Only the Polycarbophil suspensions of the drug could show significant increases of bioavailabilities in all three models, whereas a controlled release bioadhesive drug delivery system consisting of microspheres of poly(2-hydroxyethyl methacrylate) with a mucoadhesive Polycarbophil coating was practically ineffective, because its mucoadhesive coating was deactivated by soluble mucins before reaching the intestinal mucosa.

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A prolongation of the absorption phase in vitro and in the chronically isolated loop in situ suggested that the polymer was able to protect the peptide from proteolytic degradation. In another study, Lueßen and coworkers (1997) compared the absorption enhancing effects of polycarbophil, chitosan, and chitosan glutamate and found that all three mucoadhesive polymers were potent enhancers of the model peptide drug DGAVP using Caco-2 cell layers and the vertically perfused intestinal loop model of the rat. However, the observed comparable transport effect of polycarbophil in the intestinal loop model was mainly ascribed to the protection of DGAVP against proteolytic degradation in the intestinal lumen, which allows for sufficient concentration and thus transport of the peptide drug when a polycarbophil induced paracellular transport is less pronounced. Enzyme Inhibitory Effects of Polyacrylates Lueßen and coworkers (1996a) have studied the potency of mucoadhesive excipients to inhibit intestinal proteases. Among the different mucoadhesive polymers investigated, uniquely the poly(acrylates) polycarbophil and carbomer 934P were able to inhibit the activities of trypsin, α-chymotrypsin, carboxypeptidase A, and cytosolic leucine aminopeptidase. However, they failed to inhibit microsomal leucine aminopeptidase and pyroglutamyl aminopeptidase. Carbomer was found to be more efficient to reduce proteolytic activity than polycarbophil (most probably due to various flexibilities of the polymer chains). The authors also could demonstrate the pronounced binding properties of polycarbophil and carbomer for bivalent cations such as zinc and calcium, being a major reason for the observed inhibitory effect. These polymers were able to deprive Ca2+ and Zn2+ , respectively, from the enzyme structures, thereby inactivating their activities. Carboxypeptidase A and α-chymotrypsin activities were observed to be reversible upon addition of Zn2+ and Ca2+ ions, respectively. Table 6.2 shows some of the results of luminal enzyme inhibition by polyacrylates. However, the poly(acrylic acid) derivatives polycarbophil and carbomer showed rather weak inhibitory effects on enzymes of the intestinal brush border cell membranes responsible for DGAVP and metkephamid degradation (Lueßen et al., 1996b). In another study, Lueßen and coworkers (1996c) compared the enhanced intestinal absorption of carbomer, the neutralized carbomer–sodium salt (NaC934P), and chitosan hydrochloride for the peptide drug buserelin and concluded that the higher bioavailability with chitosan hydrochloride compared to carbomer and NaC934P is an indication that for buserelin the intestinal transmucosal transport enhancing effect of the polymer plays a more dominant role than the protection against proteases such as α-chymotrypsin. It is emphasized again that the decrease of enzyme activity is time dependent (i.e., after 10–20 min maximum deactivation is achieved). Although enzyme inhibition by these polyacrylates is not an absorption-enhancing effect per se, especially in the intestinal peptide and protein absorption, it is a more than favorable effect to increase the amount of peptide drug absorbed (Lueßen et al., 1996b, 1997).

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R R TABLE 6.2. Effects of Polycarbophil (PCP) and Carbomer 934P (C934P) on protease activities (Lueßen et al., 1995, 1996a)

Enzyme

Polymer

Trypsin

PCP

C934P

α-Chymotrypsin

PCP C934P

Carboxypeptidase A

PCP C934P

Leucine Aminopeptidase M Leucine Aminopeptidase C

PCP C934P PCP C934P

Pyroglutamyl Aminopeptidase

PCP C934P

Concentration (%)

Inhibitiona

0.35 0.25 0.15 0.25 0.15 0.1 0.5 0.25 0.5 0.25 0.1 0.05 0.1 0.05 0.5 0.5 0.5 0.25 0.5 0.25 0.5 0.5

+ ± − ++ + ± ± − + − + ± ++ + − − ++ + ++ + − −

a ++, strong inhibition; +, inhibition; ±, slightly reduced enzyme activity; −, no

inhibition

Removal of endogeneous Ca2+ from the intestinal epithelial cells by the formation of poly(acrylic acid)-Ca2+ complexes loosens the cellular barrier, especially by triggering the (reversible) opening of the tight junctions. This effect has been demonstrated by Borchardt et al. (1996) by flux studies of mannitol and dextran fluorescently labeled fluorescein isothiocyanate (FITC-dextran, MW 4,400 Da) across intestinal Caco-2 cell monolayers after incubation with poly(acrylic acid) derivatives and by visualization studies using CLSM. It should be noted that the observed reversibility in tight junction opening was gradual only because of the high viscosity and adhesive character of the polymer solutions to the cell layer. Carbopol 934P was also used by Thanou and collaborators (2001b) for the enhancement of the intestinal absorption of low molecular weight heparin (LMWH) in rats and pigs. LMWH is a polyanion and does not interact with polycarbophil 934P. To both animal species LMWH was administered intraduodenally and the antiXa levels were measured. Both studies showed a remarkably enhanced LMWH uptake after about 1 h and the effect for providing sufficient antithrombotic effect lasted for both animal species about 7 h showing that this polyacrylate may be a good absorption enhancer for LMWH, provided a good delivery system can be developed based on polycarbophil 934P.

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6.3.3.2 Chitosan Application, Mechanism and Safety Aspects Chitosan (poly[β-(1-4)-2-amino-2-deoxy-D-glucopyranose]) is a cationic polysaccharide comprising copolymers of glucosamine and N-acetylglucosamine (Fig. 6.3c). Nowadays chitosan is available in different molecular weight (polymers 500,000–50,000 Da, oligomers 2,000 Da), viscosity grades, and degree of deacetylation (40–98%). It is next to cellulose the most abundant polysaccharide in nature. Chitosan is insoluble at neutral and alkaline pH values, whereas it forms salts with inorganic and organic acids such as glutamic acid, hydrochloric acid, lactic acid, and acetic acid. Chitosan is generally regarded as biocompatible, slowly biodegradable natural origin polymer (Hirano and Noishiki, 1985; Chandy and Sharma, 1990). It is widely used in the food industry as a food additive and as a weight loss product. Chitosans have found a number of applications as biomaterials in tissue engineering and in controlled drug release systems for various routes of delivery (Dodane and Vilivalam, 1998; Illum, 1998; Suh and Matthew, 2000). Chitosan polymers are also used as a safe excipient for a number of pharmaceutical applications (e.g., excipient in granules and tablets, gels and microspheres) (Baldrick, 2000). Chitosan has been included in the European Pharmacopoeia since 2002. The bioadhesive properties were first described by Lehr et al. (1992d) demonstrating that chitosan in the swollen state is an excellent mucoadhesive at porcine intestinal mucosa and is also suitable for repeated adhesion. The authors also reported that chitosan underwent minimal swelling in artificial intestinal fluids due to its poor aqueous solubility at neutral pH values, proposing that substitution of the free-NH2 groups with short alkyl chains would change the solubility and hence the mucoadhesion profile. The strong mucoadhesive properties of chitosan are due to the formation of hydrogen and ionic bonds between the positively charged amino groups of chitosan and the negatively charged sialic acid residues of mucin glycoproteins (Rossi et al., 2000). Illum et al. (1994) described that chitosan solutions at 0.5% (w/v) concentrations are highly effective at increasing the absorption of insulin across nasal mucosa in rats and sheep. The mechanism of action of chitosan was suggested to be a combination of bioadhesion and a transient widening of the tight junctions in the membrane. The influence of chitosan’s degree of deacetylation and MW was also investigated on the permeability of Caco-2 cell intestinal monolayers. Schipper and coworkers (1996) studied the effect of chitosan solutions at pH 5.5 on the permeability of the nonabsorbable paracellular marker [14 C]mannitol and intracellular dehydrogenase activity. It was found that chitosans with a high degree of deacetylation were effective as absorption enhancers at low and high molecular weight, and also showed clear dose-dependent toxicity, whereas chitosans of low degree of deacetylation were effective at only high molecular weight and showed low toxicity. The effects of chitosans of both low and high molecular weight and degree of deacetylation were further investigated by the same authors with respect to their ability to bind at epithelial Caco-2 cell monolayers. Both chitosans

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appeared to bind tightly to the epithelium, inducing a redistribution of F-actin (change from a filamentous to a globular structure) and the tight junction’s zonula occludens-1 protein. No intracellular uptake of chitosan could be observed. It was also shown that these effects were mediated by chitosan’s cationic charges, since addition of the highly anionic heparin to the test solution inhibited the absorption enhancing effect (Schipper et al., 1997). Kerec and coworkers (2005) just recently investigated the role of Ca2+ on the permeability effect of chitosan on the isolated pig urinary bladder. Their results show that when calcium ions were applied together with chitosan to the luminal surface of the urinary bladder, they decrease the permeability of the model drug moxifloxacin in a concentration dependent way. These experiments show that Ca2+ ions are of no benefit to absorption enhancement when simultaneously given to both chitosans and polyacrylates (Lueßen et al., 1996a). Whereas for most absorption enhancers studied the cytotoxicity profile was evident, chitosan gave contradictory results regarding safety (Carreno-Gomez and Duncan, 1997). Dodane et al. (1999) investigated the effect of chitosan (degree of deacetylation 80%) solutions at pH 6.0–6.5 on the structure and function of Caco-2 cell monolayers. Using a series of microscopic techniques, the authors were able to show that chitosan had a transient effect on the tight junction’s permeability and that viability of the cells was not affected. However, chitosan treatment slightly perturbed the plasma membrane, but this effect was reversible. In a preliminary study, Chae and coworkers (2005) investigated the molecular weight (MW) dependent Caco-2 cell layer transport phenomena (in vitro) and the intestinal absorption patterns after oral administration (in rats in vivo) of water soluble chitosans. The absorption of chitosans was significantly influenced by its MW. As the MW increases, the absorption decreases. The absorption both in vitro and in vivo of a chitosan with a MW of 3.8 kDa was about 25 times higher in comparison to a high MW chitosan (230 kDa). On the other side, the chitosans showed concentration- and MW-dependent cytotoxic effects: the chitosan oligosaccharides (MW < 10 kDa) showed negligible cytotoxic effects on the Caco-2 cells whereas the high MW chitosans were more toxic in this experimental setting. However, the abundant use of chitosans in the food industry and the use of chitosan as excipient for peroral drug delivery systems prove that also chitosan with a high molecular weight can be regarded as safe. Chitosan as Absorption Enhancer of Hydrophilic Macromolecular Drugs Illum and coworkers (1994) reported at first that chitosan is able to promote the transmucosal absorption of small polar molecules as well as peptide and protein drugs across nasal epithelia. Immediately afterward Artursson and collaborators (1994) reported that chitosan can increase the paracellular permeability of [14 C]mannitol (a marker for paracellular routes) across Caco-2 intestinal epithelia. Chitosan gels were first tested in vivo for their ability to increase the intestinal absorption by Lueßen and coworkers in 1996c. The absorption enhancement of the peptide analog buserelin was studied after intraduodenal coadministration with chitosan (pH 6.7) in rats. Chitosan substantially increased the bioavailability of the

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peptide (5.1%) in comparison to control (no polymer) or Carbopol 934P containing formulations. Borchardt and his team (1996) investigated chitosan glutamate solutions at pH 7.4 for their effect in increasing the paracellular permeability of [14 C]mannitol and fluorescently labeled dextran (MW 4,400 Da) in vitro in Caco-2 cells. No effect on the permeability of the monolayer could be observed, indicating that at neutral pH value chitosan is not effective as absorption enhancer. The pH dependency of chitosan’s effect on epithelial permeability was further investigated by Kotz´e and coworkers (1998). Two chitosan salts (hydrochloride and glutamate) were evaluated for their ability to enhance the transport of [14 C]mannitol across Caco-2 cell monolayers at two pH values, 6.2 and 7.4. At low pH both chitosans showed a pronounced effect on the permeability of the marker, leading to 25- (glutamate salt) and 36-fold (hydrochloride salt) enhancement. However, at pH 7.4 both chitosans failed to increase the permeability, due to their insolubility for use as absorption enhancer in more basic environment such as in the large intestine. These results made quite clear that chitosan (salts) cannot be used as absorption enhancers for in vivo studies when the drug should be released in the jejunum because of the insolubility and hence ineffectiveness at pH values higher than 6.5. 6.3.3.3 N,N,N,-Trimethyl Chitosan Hydrochloride (TMC) Synthesis and Characterization of TMC Sieval et al. (1998) and Kotz´e and collaborators (1998) based on the method of Domard et al. (1986) synthesized TMC. TMC is a partially quaternized derivative of chitosan, which is prepared by reductive methylation of chitosan with methyl iodide in a strong basic environment at an elevated temperature. The degree of quaternization can be altered by increasing the number of reaction steps by repeating them or by increasing the reaction time. TMC proved to be a derivative of chitosan with superior solubility and basicity, even at low degrees of quaternization, compared to chitosan salts. This quaternized chitosan shows much higher aqueous solubility than chitosan in a much broader pH and concentration range. The reason for this improved solubility is the substitution of the primary amine with methyl groups and the prevention of hydrogen bond formation between the amine and the hydroxylic groups of the chitosan backbone. The absolute molecular weights, radius, and polydispersity of a range of TMC polymers with different degrees of quaternization (22.1, 36.3, 48.0, and 59.2%) were determined with size exclusion chromatography and multiangle laser light scattering (MALLS). The absolute molecular weight of the TMC polymers decreased with an increase in the degree of quaternization. The respective molecular weights measured for each of the polymers were 2.02×105, 1.95×105, 1.66 × 105, and 1.43 × 105 g/mole. It should be noted that the molecular weight of the polymer chain increases during the reductive methylation process due to the addition of the methyl groups to the amino group of the repeating monomer. However, a net decrease in the absolute molecular weight is observed due to degradation of the polymer chain caused by exposure to the specific reaction condition during the synthesis (Snyman et al., 2002). Polnok and coworkers (2004)

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investigated the influence of the methylation process on the degree of quaternization on N-trimethyl chitosan chloride. 1 H-Nuclear magnetic resonance spectra showed that the degree of quaternization was higher when using sodium hydroxide as base compared to dimethyl amino pyridine. The degrees of quaternization as well as O-methylation of TMC increased with the number of reaction steps. The mucoadhesive properties of TMC with different degrees of quaternization, ranging between 22 and 49%, were investigated by the group of Snyman et al. (2002). TMC was found to have a lower intrinsic mucoadhesivity compared to the chitosan salts, chitosan hydrochloride and chitosan glutamate, but if compared to the reference polymer, pectin, TMC possesses superior mucoadhesive properties. The decrease in the mucoadhesion of TMC compared to the chitosan salts was explained by a change in the conformation of the TMC polymer due to interaction between the fixed positive charges on the quaternary amino group, which possibly also decreases the flexibility of the polymer backbone. The interpenetration into the mucus layer by the polymer is influenced by a decrease in flexibility resulting in a subsequent decrease in mucoadhesivity (Snyman et al., 2003). N-Trimethyl Chitosan as Absorption Enhancer of Peptide Drugs TMC was first investigated for permeation enhancing properties and toxicity by Kotz´e and coworkers (1997, 1999), using the Caco-2 cells as a model for intestinal epithelium. Initially a trimethylated chitosan having a degree of trimethylation of 12% (dimethylation 80%) was tested. This polymer (1.5–2.5%, w/v; pH 6.7) caused large increases in the transport rate of [14C]mannitol (32- to 60-fold), fluorescently labeled dextran 4,400 (167- to 373-fold), and the peptide drug buserelin (28- to 73-fold). CLSM confirmed that TMC opens the tight junctions of intestinal epithelial cells to allow increased transport of hydrophilic compounds along the paracellular transport pathway. No intracellular transport of the fluorescent marker could be observed (Kotz´e et al., 1999). Chitosan HCl and TMCs of different degrees of trimethylation were tested by Kotz´e and collaborators (1999) for enhancing the permeability of [14 C]mannitol in Caco-2 intestinal epithelia at a pH value of 7.2. Chitosan HCl failed to increase the permeability of these monolayers and so did TMC with a degree of methylation of 12.8%. However, TMC with a degree of trimethylation of 60% increased significantly the [14 C]mannitol permeability across Caco-2 intestinal monolayers, indicating that a threshold value at the charge density of the polymer is necessary to trigger the opening of the tight junctions at neutral values. TMC polymers were further investigated by Thanou et al. (1999, 2000a) to see if they provoke cell membrane damage on Caco-2 cell monolayers during enhancement of the transport of hydrophilic macromolecules. Using cell membrane impermeable fluorescent probes and CLSM, it was visualized that TMC polymers widen the paracellular pathways without cell damage. From such visualization studies it also appears that the mechanism of opening the tight junctions is similar to that of protonated chitosan (Thanou et al., 2001c). Because of the absence of significant toxicity, TMC polymers (particularly with a high degree

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of trimethylation) are expected to be safe absorption enhancers for improved transmucosal delivery of peptide drugs (Thanou et al., 1999). The effects of TMC60 (degree of trimethylation 60%) polymers were subsequently studied in vivo in rats, using the peptide drug buserelin (pH = 6.8) and octreotide (pH = 8.2) (Thanou et al., 2000b, 2000c). Buserelin formulations with or without TMC60 (pH 7.2) were compared with chitosan dispersions at neutral pH values after intraduodenal administration in rats. A remarkable increase in buserelin serum concentrations was observed after coadministration of the peptide with TMC60, whereas buserelin alone was poorly absorbed. In the presence of TMC60 buserelin was rapidly absorbed from the intestine having tmax at 40 min, whereas chitosan dispersions (at pH 7.2) showed a slight increase in buserelin absorption compared to the control. Chitosan did not manage to increase the buserelin concentrations to the levels achieved with TMC60. The absolute bioavailability of buserelin after coadministration with 1.0% TMC60 was 13.0%. Similar to the buserelin studies (Thanou et al., 2000b), octreotide absorption after intrajejunal administration was substantially increased, resulting in peptide absolute bioavailability of 16%. Octreotide was also administered to juvenile pigs with or without TMC60 at a pH of 7.4. The solutions were administered intrajejunally through an in-dwelling fistula that was inserted one week prior to the octreotide. Intrajejunal administration of 10 mg of octreotide, coadministered with 5 and 10% (w/v) TMC60, resulted in a 7.7- and 14.5-fold increase in octreotide absorption with absolute bioavailabilities of 13.9 ± 1.3% and 24.8 ± 1.8%, respectively (Fig. 6.4) (Thanou et al., 2001a). It is stated by the authors that a gel was obtained with the 10% (w/v) concentration of the polymer. This high concentration of the TMC60 polymer was chosen to counteract the dilution of the 20 ml administration volume by the luminal fluids and mucus of the intestinal tract and to ensure that substantial amounts of both peptide and enhancer could reach the absorptive site of the intestinal mucosa (Thanou et al., 2001a). Although the results show very high bioavailabilities (also taking into account the small absorptive area which is created by only widening of the tight junctions), the impracticality of administering such high concentrations in a solid dosage form cannot be overlooked as concentrations of 1–2 g of the polymer have to be administered in an attempt to obtain the same results (Van der Merwe et al., 2004a). In order to overcome these problems a completely new and different approach has been chosen by Dorkoosh and coworkers (2002). The platform of their delivery systems consists of superporous hydrogels (SPH) and superporous hydrogels composite (SPHC). These hydrogels can swell very rapidly and have the capacity to take up between 100 and 200 times of intestinal liquid of their original volume. Arriving in the intestine those SPHs swell quickly and bring the delivery systems (small tablet in which the drug is incorporated) which is attached to the outside of the SPH platform in direct contact to the absorbing surface. TMC at the outside of the small tablet will interfere at the interface between swollen SPH and intestinal wall as a polymeric penetration enhancer widening locally the tight junctions

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F IGURE 6.4. Plasma octreotide concentration (mean ± SE) versus time curves after intrajejunal administration in pigs (10 mg/20 ml/pig) with the polymers chitosan HCl [CS1.5, 1.5% (w/v), pH 5.5; n = 6] and TMC [TMC 10, 10% (w/v); pH 7.4; n = 6, and TMC, 5% (w/v); pH 7.4; n = 3] or without any polymer [OA 10, octreotide in 0.9% NaCl; pH 7.4; n = 5]. With permission from Thanou et al. (2001a)

to allow for paracellular absorption of the peptide drug. In an in vivo study with pigs the achieved absolute bioavailabilities of octreotide were between 8.7 ± 2.4% and 16.1 ± 3.3% depending on the type of delivery system used. The value of 16.1±3.3% was achieved with TMC60 as absorption enhancer. After the peptide’s release from the dosage form the SPH platforms get overhydrated and are easily broken down by the peristaltic forces of the gut. Scintigraphic studies in human have shown the good performance of these oral peptide drug delivery systems with prolonged residence times in the gut. Incorporating the SPHc delivery platforms in enteric coated gelatin capsules of size 000 lead to various stomach transit times (2–6 h in pigs) and 1.5–3 h in human volunteers (Dorkoosh et al., 2004). Capsules of smaller size (00) may reduce the variability in gastric transit times. 6.3.3.4 Monocarboxymethyl Chitosan An usual approach to increase chitosan’s solubility at neutral pH values is the substitution of the primary amine. Whereas N-substitution with alkyl groups (i.e. −CH3 groups) can increase the aqueous solubility without affecting its cationic character, substitution with moieties bearing carboxyl groups can yield polymers with polyampholytic properties (Muzzarelli et al., 1982). Monocarboxymethylated chitosan (MCC) was synthesized and further evaluated as potential absorption enhancer (Thanou et al., 2001d). This chitosan derivative (degree of substitution 87–90%) has polyampholytic (zwitterionic) character, which allows the formation of clear gels or solutions (dependent on the

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concentration of the polymer) even in the presence of polyanionic compounds like heparins at neutral and alkaline pH values, whereas it aggregates at acidic pH. Chitosan and the quaternized derivative TMC form complexes with polyanions that precipitate out of the solution. In contrast, MCC appeared to be compatible with polyanions. Two viscosity grades MCC (high and low) were initially investigated to see if they are able to increase the permeation of LMWH (4,500 Da) across Caco-2 intestinal cell monolayers. However, the MCC concentrations necessary to open the tight junctions were several times higher than that of TMC60 at neutral pH value. Low viscosity MCC induced higher transport of LMW when compared with the high viscosity derivative. Cell viability tests at the end of the experiments showed that this type of polymer had no damaging effect on cell membranes, whereas recovery of the transepithelial electrical resistance values to initial levels indicated the functional integrity of the monolayer. The mechanism by which polyampholytic chitosans interacts with the tight junctions is not clear yet. For in vivo studies, LMWH was administered intraduodenally with or without MCC to rats. Three percent (w/v) low viscosity MCC significantly increased the intestinal absorption of LMWH, reaching the therapeutic anticoagulant blood levels of LMWH for at least 5 h determined by measuring anti-Xa levels (Thanou et al., 2001d). 6.3.3.5 Thiolated Polymers Thiolated Polymers of Polyacrylates and Cellulose Derivatives Thiolated polymers are synthesized by immobilizing thiol groups on polyacrylates or cellulose derivatives (Fig. 6.3d) by the group of Bernkop-Schn¨urch (Bernkop-Schn¨urch and Steiniger, 2000; Bernkop-Schn¨urch et al., 2000; Leitner et al., 2003a; Clausen and and Bernkop-Schn¨urch, 2000). The main purpose of introducing free thiol groups into polymers, which already have mucoadhesive properties, is to further strongly increase the strength of their mucoadhesiveness due to the chemical reaction of the thiol groups of the mucins and the thiol groups of the thiolated polymers by forming stable covalent disulfide bridges. With this elegant approach the mucoadhesiveness of such polymers and additionally their cohesiveness could be strongly increased. However, because of the turnover time of mucus, which has been estimated in the isolated intestinal loop of the rat by Lehr et al. (1991) to be in the order of 47–270 min, the residence time of these thiolated polymers and the delivery systems made of such thiolated polymer will be restricted to this time interval. A reasonable assumption is that the polymers will stick to the mucus at the time of 3 h after peroral application. Bernkop-Schn¨urch and coworkers (2000; Bernkop-Schn¨urch et al.2000) linked L -cysteine covalently to PCP (Fig. 6.3d) mediated by a carbodiimide. The resulting thiolated polymers displayed 100 ± 8 and 1, 280 ± 84 µmol thiol groups per gram, respectively. In aqueous solutions these modified polymers were capable of forming inter and/or intramolecular disulfide bonds. Due to the formation of disulfide

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bonds within the thiol-containing polymers, the stability of matrix tablets could be strongly improved. Whereas tablets based on the corresponding unmodified polymer disintegrated within 2 h, the swollen carrier matrices of thiolated NaCMC and PCP remained stable for 6.2 h and for more than 48 h, respectively. With the model drug rifampicin controlled release characteristics of these thiolated matrix tablets could be demonstrated. Tensile studies carried out with the unmodified and thiolated polymers at pH 3, 5, and 7, respectively, revealed that only if the polymer displays a pH value of 5, the total work of adhesion could be improved significantly due to the covalent attachment of thiol groups. The permeation enhancing effect of thiolated polycarbophil on intestinal mucosa from guinea pigs showed weak enhancement ratios (1.1–1.5) in comparison to control tests. Thiolated Polymers of Chitosan With the same aim as described in the last paragraph, chitosan has been chemically modified by covalent binding of sulfur containing moieties. To date, different thiolated chitosan have been synthesized: chitosan–thioglycolic acid conjugate (Fig. 6.3e), chitosan–cysteine conjugates (Bernkop-Schn¨urch and Hopf, 2001; Kast and Bernkop-Schn¨urch, 2001; Hornof et al., 2003), chitosan–cystein conjugates (Bernkop-Schn¨urch et al., 1999), and chitosan-4-thio-butyl-amide (chitosanTBA) conjugates (Fig. 6.3f) (Bernkop-Schn¨urch et al., 2003). These thiolated chitosans have numerous advantageous features in comparison to unmodified chitosan, such as significantly improved mucoadhesive properties and permeation enhancing properties. The strong cohesive properties of thiolated chitosans make them highly suitable excipients for controlled drug release dosage forms (Bernkop-Schn¨urch et al., 2003; Kast and Bernkop-Schn¨urch, 2001). Moreover, solutions of thiolated chitosans display in situ gelling properties at physiological pH values, which make them suitable for novel application systems to the eye (Bernkop-Schn¨urch et al., 2004). The improved mucoadhesive properties of thiolated chitosans were explained by the formation of covalent bonds between thiol groups of the polymer and cysteine-rich subdomains of glycoporteins in the mucus layer (Leitner et al., 2003b). These covalent bonds are supposed to be stronger than noncovalent bonds, such as ionic interactions of chitosan with nonionic substructures as sialic acid moieties of the mucus layer. This theory was supported by the results of tensile studies with tablets of thiolated chitosan, which demonstrated a positive correlation between the degree of modification with thiol bearing moieties and the adhesive properties of the polymer (Kast and Bernkop-Schn¨urch, 2001; Roldo et al., 2004). These findings were confirmed by another in vitro mucoadhesion system, where the time of adhesion of tablets on intestinal mucosa was determined. The contact time of the thiolated chitosan derivatives increased with increasing amounts of immobilized thiol groups (Kast and Bernkop-Schn¨urch, 2001; Bernkop-Schn¨urch et al., 2003). With chitosan–thioglycolic acid conjugates a 5to 10-fold increase in mucoadhesion in comparison to unmodified chitosan was achieved.

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6.3.3.6 Solid Dosage Form Design Based on TMC and Thiolated Polymers and Their In Vivo Evaluation As in the sections described above about TMC, this chitosan derivative has only been administered as a gel formulation or solution. However, the impracticability of administering a polymer solution intraduodenally with the peptide dispersed or dissolved in it, as well as the fact that most peptides are unstable in the presence of an aqueous milieu, has led to the need for a solid oral dosage form in which TMC can be administered together with peptide drugs. To optimally make use of the absorption enhancing properties of TMC in a solid dosage form, the polymer should be able to dissolve rapidly and then be allowed to spread over a wide area of the epithelium in the small intestine. The opening of the tight junctions is a time-dependent process and it is therefore necessary that most of the TMC should be released from the dosage form prior to the release of the peptide drug. The site at which the peptide is released should coincide with the side where the TMC is opening the paracellular pathway for maximum paracellular absorption of the peptide drug (Van der Merwe et al., 2004a). Minitablets with a diameter of 2–3 mm and granule formulation were developed as solid oral dosage form for the delivery of TMC and the peptide drug desmopressin (1-(3-mercaptopropionic acid)-8-D-arginine vasopressin monoacetate; DDAVP). Both the developed minitablet and granule formulations showed an initial burst release of TMC with a delayed release for DDAVP. Maximum release of TMC was in the order of 50% for all formulations, which is acceptable considering the high molecular weight of the polymer. Domestic pigs were used for the evaluation of the developed minitablet and granule formulations. However, the somatostatin analogue, octreotide, was used in this study and therefore the formulations were slightly adapted to give the same release profiles (Van der Merwe et al., 2004b). The delivery systems were filled in 000 capsules and enteric coated and applied with a special designed applicator into the stomach of the pigs. Statistical analysis showed no significant difference between the absolute bioavailabilities for the different formulations administered via the peroral route. The average bioavailabilities for the negative control, minitablet formulation, granule formulation, and TMC/octreotide solution were, respectively, 0.9 ± 0.5%, 1.0 ± 1.5%, 1.4 ± 0.5%, and 0.5 ± 0.2%. The combination of the gelatin, the enteric coating, and the sticky properties of TMC might have resulted in a delay of release of both the octreotide and the TMC, resulting in the unsatisfactory absorption enhancement with the polymer. A similar low bioavailability with the peptide drug antide has been the result of an in vivo study of Bernkop-Schn¨urch and coworkers (2005). Antide and the permeation mediator glutathione were embedded in the thiolated polymer chitosan4-thio-butylamidine conjugate and compressed to tablets. Because it turned out that antide was strongly degraded in the small intestine by elastase, a stomach targeted delivery system was designed. The absolute and relative bioavailability

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after oral application of the tablet delivery system to pigs were 1.1% and 3.2%, respectively. In an earlier study, Guggi and coworkers (2002, 2003) developed a solid dosage for the peroral delivery of salmon calcitonin to rats. Different drug carrier matrices, comprising chitosan-4-thio-butyl-amide (chitosan-TBA) conjugate as substantial polymeric excipient and containing equal amounts of salmon calcitonin and optionally the permeation mediator reduced glutathione (Bernkop-Schn¨urch and Scerbe-Saiko, 1998) were developed. In order to avoid an enzymatic degradation of the peptide drug in the gastrointestinal tract chitosan inhibitor conjugates were also added. All compounds were homogenized and directly compressed to tablets. To enteric-coated tablets targeted to the small intestine, a chitosan-BBI conjugate (Bowman-Birk inhibitor) (Guggi and Bernkop-Schn¨urch, 2003) and a chitosan-elastatinal conjugate were added (Bernkop-Schn¨urch and Scerbe-Saiko, 1998). The different tablets were given orally to rats and the plasma calcium levels were monitored as a function of time. Pharmacological efficacy was calculated on the basis of the area under the reduction in plasma calcium levels of the oral matrix tablets versus i.v. injection. No significant effects were measured when calcitonin was given as a solution orally and also when chitosan was used as the main tablet ingredient due to its insolubility in pH values above 6.5. Only with tablets containing chitosan TBA conjugate as the main tablet excipient a moderate decrease of the calcium level of more than 5% for several hours have been reached. The increased absorption of the peptide, when embedded in a thiolated chitosan matrix, occurs due to the properties of this compound: the high stability and cohesiveness can provide a sustained release of the peptide, while the mucoadhesive features should lead to a prolonged residence time of the dosage form on the site of absorption (which still has to be demonstrated). Moreover, the combination of thiolated chitosan with the permeation mediator reduced glutothione, seems to have an impact on the bioresponse of orally given calcitonin. The significantly higher pharmacological efficacy of thiolated chitosan tablets containing glutathione in comparison to corresponding tablets without glutathione indicates that glutathione contributes additionally to the drug absorption enhancing process. As pointed out before, it is extremely difficult to develop a peptide drug delivery system for peroral application intended to trigger paracellular uptake of the drug by locally widening of the tight junctions due to the deactivation of the mucoadhesive polymers by soluble mucins in the intestinal liquids before the delivery system reaches the mucosal epithelial tissue and could exert its polymer–mucus (and even preferred directly with the epithelial tissue sialic and sulfate groups) interactions. With small animals as mice and rats, some success with small particulate delivery systems has been achieved due to the short distances between delivery system and gut wall. However, translating the efficacy of such delivery systems to the gut of the pig or human being with much wider diameter of the gut lumen has not been successfully solved so far.

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6.4

Conclusions

Most of the pharmaceutical excipients, which when used in appropriate amounts for the manufacturing of common drug delivery systems, are inert and do not show interactions with absorbing tissues. Some excipients such as low molecular weight surfactants may not be as neutral in their interactions with the mucosal membranes. In most cases, their intrinsic properties to form at a defined concentration micelles is used to solubilize poorly soluble drugs. When those solubilisates come in contact with the mucosal tissues of the gut they may interact with the phospholipid bilayer of this membrane and cause some perturbations of the phospholipid bilayer structure and form also micelles with those components or solubilize membrane proteins. Dependent on their concentration, this may lead locally to toxic effects. It is well known that the absorption enhancing effect is directly related to the surfactant concentration. STDHF seemed to be a promising absorption enhancer for the nasal application of insulin; however, chronic toxicity studies with ciliated chicken membranes showed that this compound was too toxic for chronic use and was therefore withdrawn from the market. Cyclodextrins show basically the same effect as low molecular mass permeation enhancers: they are predominantly used for the solubility increase for poorly soluble drugs such as estrogens, progesterone, testosterone, and hydrocortisone (Duchˆene et al., 1999). There seem to be also some species differences: whereas, e.g., β-cyclodextrins showed very promising results in the nasal absorption of insulin of rats and sheep, no significant improvement could be obtained in the human nose. It can be concluded that until today low molecular weight surfactants and also cyclodextrins play a minor role as absorption enhancer for hydrophilic drugs. With the advent of new biotechnological techniques endogenous compounds like insulin have become available at affordable prices. However, until, today the development of alternative dosage forms (for the nasal, buccal, peroral, rectal, vaginal, and ocular route) for the administration of those class III drugs (high solubility/low permeability) according to the BCS (Amidon et al., 1995) could not keep pace with this development of endogenous peptides. Multifunctional high molecular weight polymers as polyacrylates and chitosan with its various derivatives show promising properties as specific penetration enhancers for the paracellular route of absorption of hydrophilic molecules with high enhancing potency of reversibly opening of the tight junctions and practically with no toxicity when used in normal doses. However, the physical properties (poor solubility and high viscosity and easy saturation of the mucoadhesive properties by soluble mucins in the intestinal liquids) make it very difficult to develop suitable dosage forms which are able to quickly swell in the intestinal gut fluids, to develop mucoadhesive properties, and finally reach the mucous linings of the (human) gut in a still mucoadhesive form. After adhesion to the gut mucus and widening of the tight junctions the peptide drug should be released in the desired controlled manner. Hence, the development of such dosage forms is still in its infancy, but there are promising perspectives that such delivery systems will be successfully developed further on and will see the light of the market.

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References Accili, D., Menghi, G., Bonacucina, G., Di Martino, P., and Palmieri, G. F. (2004) Mucoadhesion dependence of pharmaceutical polymers on mucosa characteristics. Eur. J. Pharm. Sci. 22: 225–234. Amidon, G. L., Lennern¨as, H., Shah, V. P., and Crison, J. R. (1995) A theoretical basis for a biopharmaceutics drug classification. The correlation of in vitro product dissolution and in vivo bioavailability. Pharm. Res. 12: 413–420. Anderberg, E. K. and Artursson, P. (1992) Epithelial transport of drugs in cell culture. VII. Effects of pharmaceutical surfactant excipients and bile acids on transepithelial permeability in monolayers of human intestinal epithelia. J. Pharm. Sci. 81: 879–887. Artursson, P., Lindmark, T., Davis, S. S., and Illum, L. (1994) Effect of chitosan on the permeability of monolayers of intestinal epithelial cells (Caco-2). Pharm. Res. 11: 1358– 1361. Baldrick, P. (2000) Pharmaceutical excipient development: the need for preclinical guidance. Regul. Toxicol. Pharmacol. 32: 210–218. Bansil, R., Stanley, E., and LaMont, J. T. (1995) Mucin biophysics. Ann. Rev. Physiol. 57: 635–657. Barry, B. W. (1983) Percutaneous absorption. In: Barry, B. W. (ed.). Dermatological Preparations, Marcel Dekker, New York, pp. 127–233. Bernkop-Schn¨urch, A. and Hopf, T. E. (2001) Synthesis and in vitro evaluation of chitosanthioglycolic acid conjugates. Sci. Pharm. 69: 109–118. Bernkop-Schn¨urch, A. and Scerbe-Saiko, A. (1998) Synthesis and in vitro evaluation of chitosan–EDTA–protease-inhibitor conjugates which might be useful in oral delivery of peptides and proteins. Pharm. Res. 15: 263–269. Bernkop-Schn¨urch, A. and Steiniger, S. (2000) Synthesis and characterization of mucoadhesive thiolated polymers. Int. J. Pharm. 194: 239–247. Bernkop-Schn¨urch, A., Brandt, U. M., and Clausen, A. E. (1999) Synthesis and in vitro evaluation of chitosan–cysteine conjugates. Sci. Pharm. 67: 196–206. Bernkop-Schn¨urch, A., Scholler, S., and Biebel, R. G. (2000) Development of controlled drug release systems based on thiolated polymers. J. Control. Release 66: 39–48. Bernkop-Schn¨urch, A., Hornof, M., and Zoidl, T. (2003) Thiolated polymers – thiomers: modification of chitosan with 2-iminothiolane. Int. J. Pharm. 260: 229–237. Bernkop-Schn¨urch, A., Hornof, M., and Guggi, D. (2004) Thiolated chitosans. Eur. J. Pharm. Biopharm. 57: 9–17. Bernkop-Schn¨urch, A., Pinter, Y., Guggi, D., Kahlbacher, H., Sch¨offmann, G., Schuh, M., Schmerold, I., Del Curto, M. D., D’Antonio, M., Esposito, P., and Huck, C. (2005) The use of thiolated polymers as carrier matrix in oral peptide delivery – proof of concept. J. Control. Release 106: 26–33. BF Goodrich Company. (2002) Carbopol, Noveon, Nomenclature and Chemistry (product information). Bulletins 3 and 12, USA. Bhat, P. G., Flanagan, D. R., and Donovan, M. D. (1996) Drug binding to gastric mucus glycoproteins. Int. J. Pharm. 134: 15–25. Borchardt, G., Lueßen, H. L., de Boer, A. G., Verhoef, J. C., Lehr, C.-M., and Junginger, H. E. (1996) The potential of mucoadhesive polymers in enhancing intestinal peptide drug absorption. III. Effects of chitosan glutamate and carbomer on the epithelial tight junctions in vitro. J. Control. Release 39: 131–138. Caramella, C., Bonferoni, M. C., Rossi, S., and Ferrari, F. (1994) Rheological and tensile test for the assessment of polymer–mucin interaction. Eur. J. Pharm. Biopharm. 40: 213–217.

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7 Intestinal Transporters in Drug Absorption Rajinder K. Bhardwaj, Dea R. Herrera-Ruiz, Yan Xu, Stephen M. Carl, Thomas J. Cook, Nicholi Vorsa, and Gregory T. Knipp

7.1

Introduction

Effective drug therapy relies on the interplay between the pharmacokinetics and pharmacodynamics (PK/PD) of the agent upon administration. During the initial stages of drug discovery, numerous studies are performed to assess the pharmacological effectiveness of new chemical entities (NCEs) to select a lead compound(s) that offers the greatest promise for therapeutic efficacy. While the ability of a drug to bind to a therapeutic target is critical to its clinical success, the ultimate effectiveness is also a function of its ability to reach the therapeutic target in sufficient concentrations to mitigate or treat the ailment. Therefore, the pharmacokinetics of any NCE must also be evaluated early in the drug discovery stages to enhance the rational selection of a lead compound from the many NCEs that are screened, based on not only biological activity but also potential in vivo bioavailability. Bioavailability is defined by the US FDA as “the rate and extent to which the active ingredient or active moiety is absorbed from a drug product and becomes available at the site of action” (21 CFR 320.1(a)). The overall bioavailability is largely determined by the absorption, distribution, metabolism, and excretion (ADME) of selected compounds in targeted patient populations. While ADME involves transport/permeability processes across cellular barriers in numerous tissues, we will restrict our discussion to intestinal absorption (absorptive influx) and excretion (secretory efflux). The gastrointestinal (GI) tract varies greatly in morphological characteristics from relatively no folding in the esophagus to high degrees of folding (villi) in the small intestine (Tortora and Grabowski, 1993). The small intestinal villous epithelium is the primary mediator and barrier to GI absorption of orally administered drugs and nutrients into systemic circulation. The primary cells mediating drug absorption across the intestinal villous epithelium are the polarized columnar enterocytes, which are distinguished by the presence of apical membrane microvilli. The villous structure and the enterocyte microvilli provide a significant increase in the intestinal absorptive surface area (Tortora and Grabowski, 1993); however,

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it is the compound’s physicochemical properties that dictate the route and extent of absorption. Paracellular and transcellular diffusion are the two routes of GI permeation (Adson et al., 1995; Knipp et al., 1997; Sorensen et al., 1997). Paracellular absorption occurs via diffusion of dissolved solute between cells through the tight junctional complex and tortuous pathway in the intercellular spacing (Adson et al., 1995; Knipp et al., 1997). The paracellular pathway is quite restrictive depending on the pore size and charge of the tight junctions as well as the cell barrier’s porosity. There are several physicochemical characteristics of a drug that favor paracellular diffusion including charge, hydrophilicity, shape/conformation, size, and molecular weight (Adson et al., 1995; Knipp et al., 1997). The transcellular route is comprised of several potential parallel pathways for drug permeation including passive transcellular diffusion, ion channels, facilitated diffusion, active transport, and endocytosis (Oh and Amidon, 1999). A more comprehensive discussion on the characteristics of each transcellular route of permeation is provided by Oh and Amidon (1999). Passive transcellular diffusion has traditionally been viewed as the most desirable route for GI drug absorption. The degree of passive transcellular permeation of a compound is also largely dependant on those physicochemical properties mentioned above, including the degree of ionization, lipophilicity, molecular weight, and shape/conformation. In the past, the pH-partition hypothesis, first postulated in the mid- to early 1900s, was used as a model for predicting the absorption and/or disposition of a drug across biological membranes based on the lipid to aqueous partition coefficients as a function of molecular ionization. Jacobs (1940) initially linked biological permeability and accumulation to pH, demonstrating a correlation between absorption and an electrolyte’s degree of dissociation. In fact, much of the early work was based on the observation that the rate of drug absorption is related to the drug’s degree of dissociation in solution, where drugs that exhibit a higher lipophilic versus ionic character will diffuse much more readily across biological membranes (Hogben et al., 1959). Given this clear correlation, researchers postulated that the physical barriers to drug absorption must be lipoidic in nature. The pH-partition hypothesis was then mathematically described under sink conditions based on the fraction of unionized drug in solution using Fick’s first law of diffusion, assuming that aqueous boundary layer does not affect the transport process: Fick’s first law of diffusion Dm S K p dM = (Cd − Cr ), dt hm

(7.1)

under sink conditions Dm S K p C d dM = = P SCd , dt hm

P=

Dm K p fun , hm

(7.2)

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where dM/dt is the flux of material per time, Dm the membrane diffusion coefficient, S the cross-sectional surface area, K p the partition coefficient, h m the membrane thickness, Cd the concentration of donor chamber, Cr the concentration of receiver chamber, Cd −Cr the concentration gradient at time t, P the permeability, and fun is the fraction unionized. Based on the pH-partition hypothesis, an in vitro method was widely utilized to predict drug absorption by measuring a compound’s ability to partition (log P) between a fairly immiscible lipophilic solvent (octanol) and water or buffers at different pHs (Leo et al., 1971). While this method does provide adequate predictive power within series of compounds, its broad utility is often limited by observed deviations between in vitro and in vivo permeability. These deviations were observed in early literature, but a correlative explanation was often incorrectly proposed to rationalize a fit to the pH-partition hypothesis (Shore et al., 1957). In its most basic sense, under the partitioning model absolute bioavailability should increase linearly with increasing lipophilicity (log P) due to the lipophilic nature of biological membranes, where permeation is a function of diffusion. Expansion of the model realizes that highly hydrophilic compounds (log P < 1–2) would not be absorbed transcellularly due to their polar/ionic character, just as highly hydrophobic compounds (log P > 4–5) would accumulate in the interior aliphatic portion of the cellular membrane due to their lipophilicity. For example, an absorption model that is depended solely on partitioning would result in a parabolic relationship between the fraction absorbed (log 1/concentration) and lipophilicity (log P), which would only occur in unusual cases in vivo (Higuchi and Davis, 1970). Numerous deviations in the predictive power of log P values for assessing the permeability of a compound have been observed, and therefore, other predictive tools were advanced to better address these inconsistencies. One of the more recognized tools, “The Rule of Five” was proposed by Lipinski et al. (1997, 2000, 2001) to estimate the permeability of compounds in silico based on molecular descriptors at the early stages of drug discovery. Lipinski’s Rule of Five states: “. . ..poor absorption or permeability is more likely when there are more than 5 H-bond donors, 10 H-bond acceptors, the molecular weight is greater than 500 and the calculated log P is greater than 5.” (Lipinski et al., 1997). The rule of five has found broad utility to predict the developability and optimization of NCEs in industry. However, several confounding factors, most importantly the role of drug transporters, have acted to limit the predictability and applicability of this and many analogous techniques. The complex nature of membrane physiology and the lack of predictive absorption methodologies is better understood when one considers the numerous roles elucidated for different drug transporters in mediating transcellular influx and efflux of xenobiotics. There are numerous classes of transporter proteins that have been identified to date, each with different, sometimes overlapping, substrate specificity, capacity and affinity, as well as specific tissue, cellular and temporal expression patterns. Transporter proteins are integral proteins that function via

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either a facilitated diffusion, or active, energy-dependent mechanisms to mediate transcellular flux of xenobiotics and nutrients (Oh and Amidon 1999). Not surprisingly, a compound’s physicochemical properties greatly influence its interactions with transporters and lipophilic character (i.e., partitioning) plays a major role in determining these interactions. As such, not only is there great overlap in substrate selectivity of many transporters, but there is also variability in uptake due to lipophilicity and solubility differences and resulting membrane interactions. Much of this variability may also be due to interactions with other potential substrates present in biological fluids, stearic influences of transporter binding, possible membrane interactions, such as changes in fluidity, the rate of transport (Vmax ), or even competitive binding of substrates to other transporters. Thus researchers have developed various in vitro and in vivo models to delineate the role of individual transporter activities in mediating xenobiotic uptake (Stewart et al., 1997; Ekins et al., 2000, 2005; Kimura et al., 2002; van de Waterbeemd, 2002; Harrison et al., 2004; Kassel, 2004; Sun et al., 2004). A number of cell models have been used to evaluate the intestinal permeability of drugs, with the Caco-2 cell model, derived from colorectal adenocarcinoma, being the most widely used. Caco-2 cells exhibit much of the barrier functionality of the normal endogenous intestinal epithelium. Under the right conditions, Caco-2 cells not only grow in a tight-knit monolayer and exhibit tight junctions, but they express many of the same receptors and transporters of the intestinal epithelium. The simplicity of this model makes in vitro permeability and uptake measurements relatively straightforward and conducive to automation. However, despite the obvious benefits of in vitro drug transport studies using cell lines, there are also many disadvantages and hurdles to overcome when using cell line models. For instance, even though transepithelial electrical resistance (TEER) can be determined to verify the integrity of the monolayer, leakiness is always of concern when performing these studies and can be a source of artifacts. Additionally, cellular energy requirements dictate the endogenous expression of a multitude of transporters, many with overlapping substrate specificities. This not only provides a source of variation, but also makes it difficult to assess the transport properties due to a single transporter. Moreover, culture conditions can greatly affect the genetic regulation of a multitude of functional proteins, resulting in intraand interlaboratory variations and error. In short, assessing drug transport function via various cell line models can be a useful tool, however, it also presents many technological hurdles, and the techniques employed do raise some issues of applicability to the physiologic model. In addition to those limitations mentioned above, cell lines do not adequately address the issue of transcriptional and translational variations within any particular patient population. Single nucleotide polymorphisms (SNPs) and the regulatory mechanisms associated with these genetic variations have broadened the pharmacogenomics field quite extensively. The biochemical architecture of the human intestine, including drug transporters and their variants, must be viewed in the light of evolution of mammalian nutritional requirements. For the majority of mammalian evolution, the intestine served as the primary gatekeeper that

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compounds must traverse when entering the mammalian organism. As such, the in vivo model of drug absorption and permeation with respect to drug transporters is a function of all those transporter proteins expressed at any given time and location within the vicinity of the drug product as it moves along the intestine, working in concert to facilitate the acquisition of nutrients. While useful for permeability prediction and screening of large databases of compounds, cell culture models fall short of simulating the actual in vivo conditions and should be rationalized in context. Due to the innate limitations in studying drug permeability in vitro, the pharmaceutical industry has been ever evolving in its quest to further understand and enhance the intestinal absorption of pharmaceuticals. Current approaches include formulation design to either exploit, or inhibit transporter function through the use of various excipients. Surfactants, for example, are well known to alter membrane fluidity, thus altering potential substrate interactions and transporter interactions. Another approach to maximize bioavailability is to tailor NCE design to improve substrate affinity for a particular transporter. A classic example utilizing this approach is the addition of a peptidic valine moiety to the antiviral acyclovir to produce valacyclovir. While the free compound does absorb well through the GI tract, the valyl addition increases the compound’s transporter affinity for the oligopeptide transporter, PepT1, thus drastically increasing its oral bioavailability, as will be discussed in greater detail later. A fundamental understanding of drug transporters is essential when analyzing a drug’s PK/PD behavior after oral administration. Difficulties in characterizing the intestinal transepithelial transport of drugs underscores the need for a complete understanding of the biophysical and biochemical barriers that are present in the GI tract. This chapter aims to delineate the role of several selected transporter families likely involved in the intestinal bioavailability of orally administered drugs. These transporter families have been selected due to the breadth of literature demonstrating their respective importance in intestinal drug absorption. The chapter will highlight the interplay of the molecular and functional characteristics for each of the different transporters and summarized with respect to the impact of these characteristics on altered bioavailability and pharmacokinetics.

7.2

ATP Binding Cassette Transporters

Several members of the ATP binding cassette (ABC) transporter protein superfamily have been shown to impart multidrug resistance by virtue of their ability to efflux xenobiotics from the cytoplasm and across the cellular membrane in an energy-dependent, polarized manner. These various ABC transporter superfamily isoforms constitute a broad array of substrate specificities from endogenous fatty acid metabolites to synthetic therapeutics. A common characteristic shared by ABC transporters is the presence of nucleotide binding domain(s) (NBDs),

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A. in

NH2

COOH

NBD2

NBD1

NH2

out

B. in

COOH

NBD2

NBD1 out

C. in NH2

NBD

COOH

F IGURE 7.1. Transmembrane arrangement of drug transporter proteins. (A) is the schematic of P-gp (MDR1), MDR3, MRP4, MRP5, and MRP8 that have 12 TM (transmembrane) regions and two NBDs (nucleotide binding domains), (B) shows the MRP transporters (MRP1, MRP2, MRP3 and MRP6, and MRP7) that has five extra TM regions toward the N terminus. (C) depicts the BCRP that has six TM regions and one NBD also called as a half transporter

which enable these integral membrane proteins to hydrolyze ATP to drive efflux (Fig. 7.1) (Higgins, 1991). The cloning of the human genome, coupled with various genomic and functional studies, has revealed 49 human ABC transporter isoforms that are separated into seven distinct subfamilies based on their sequence homology (ABCA to ABCG). A complete description concerning the various ABC transporter subfamilies can be found at the Web site http://www.gene.ucl.ac.uk/nomenclature/ genefamily/abc.html. Among these subfamilies, the ABCB and ABCC subfamilies contain the most widely investigated transporters influencing human intestinal absorption. Specifically, P-glycoprotein (ABCB1, P-gp) and multidrug resistanceassociated proteins (ABCC, MRPX) have not only been shown to be expressed along the GI tract, but due to their cellular localizations and broad substrate specificities, appear to be the primary efflux pumps determining xenobiotic absorption (Table 7.1). The ABCG2 isoform, also known as the breast cancer resistance protein (BCRP), has also been demonstrated to affect the intestinal absorption of various therapeutic agents. The molecular and functional characteristics of these isoforms on the intestinal absorption of drugs are discussed more comprehensively below.

ABCC1 ABCC2 ABCC3

ABCC4 ABCC5 ABCC6

DJS cMRP

cMOAT2 EST90757 MLP2 MOAT-D MOAT-B

SMRP, MOAT-C

EST349056, MLP1 MXR ABCP

Peptide transporter 1 (PEPT1)

Multidrug resistance related proteins 4 (MRP4) Multidrug resistance related proteins 5 (MRP5) Multidrug resistance related proteins 6 (MRP6) Breast cancer resistance protein

Multidrug resistance related proteins 1 (MRP1) Multidrug resistance related proteins 2 (MRP2) Multidrug resistance related proteins 3 (MRP3)

Sister of P-gp (sP-gp)

SLC15A1

ABCG2

ABCB11

ABCB1

CD243 GP170 ABC20 ABC16 PFIC-2 PGY4 GS-X

P-glycoprotein (P-gp)

Approved gene symbol

Other names

General name

ATP-binding cassette, sub-family C (CFTR/MRP), member 4 ATP-binding cassette, sub-family C (CFTR/MRP), member 5 ATP-binding cassette, sub-family C (CFTR/MRP), member 6 ATP-binding cassette, sub-family G (WHITE), member 2 Solute carrier family 15 (oligopeptide transporter), member 1

ATP-binding cassette, sub-family B (MDR/TAP), member 1 ATP-binding cassette, sub-family B (MDR/TAP), member 11 ATP-binding cassette, sub-family C (CFTR/MRP), member 1 ATP-binding cassette, sub-family C (CFTR/MRP), member 2 ATP-binding cassette, sub-family C (CFTR/MRP), member 3

Gene name

13q33–q34

4q22–q23

16p13.1

3q27

13q31

17q21

16p13.1

2q24

7q21.1

Location

(continued)

NM 005073

AF103796

AF104942 NM 005688 X95715

U66682

Y17151 NM 020038

U63970

L05628

M14758 NM 000927 AF091582

Sequence accession IDs

TABLE 7.1. Classification details of the important influx and efflux transporters present in the human small intestine. The table has been summarized from the information obtained from HUGO (Human Genome Organization) gene nomenclature committee’s Web site (http://www.gene.ucl.ac.uk/nomenclature/)

7. Intestinal Transporters in Drug Absorption 181

SLCO1B1O ATP-C LST-1

SLC21A6

SLC22A1

Organic Cation transpoter (OCT1) Organic anion transporting polypeptide (OATP1B1)

SLC28A2

SLC16A1

SPNT1 HCNT2 HsT17153

Monocarboxylate transporter (MCT1)

Concentrative nucleoside transporter (CNT2)

SLC15A3

hPTR3 SLC28A1

SLC15A4

PTR4

Peptide histidine transporter 1 (PHT1) Peptide histidine transporter 1 (PHT2) Concentrative nucleoside transporter (CNT1)

Solute carrier family 15, member 4 Solute carrier family 15, member 3 Solute carrier family 28 (sodium-coupled nucleoside transporter), member 1 Solute carrier family 28 (sodium-coupled nucleoside transporter), member 2 Solute carrier family 16 (monocarboxylic acid transporters), member 1 Solute carrier family 22 (organic cation transporter), member 1 Solute carrier organic anion transporter family, member 1B1

Gene name

TABLE 7.1. (Continued) Approved gene symbol

Other names

General name

12p12

6q26

1p12

15q15

15q25–26

11q12.2

12q24.32

Location

NM 006446

U77086

BC026317 NM 003051

U84392 NM 004212

AY038999 NM 145648 AB020598 NM 016582 U62967

Sequence accession IDs

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183

P-Glycoprotein (P-gp; ABCB1)

Influencing drug transport in mammalian tumor cells, P-glycoprotein (P-gp) was originally identified as a xenobiotic efflux pump by Juliano and Ling (1976) providing a mechanism for multidrug resistance. Subsequent studies confirmed that P-gp was a 170–180 kDa, ATP-dependent transmembrane glycoprotein, which is formed by the posttranslational glycosylation of a 140 kDa pro-P-gp protein (Kramer et al., 1995). Topological analyses of P-gp showed that the protein comprised four major domains; two membrane-bound domains, each with six transmembrane segments and two cytosolic ATP binding motifs, commonly known as Walker A and B NBDs that bind and hydrolyze ATP. P-gp consists of 1,280 amino acid residues and exhibits a large degree of homology between the carboxy and amino terminal halves (Leveille-Webster and Arias, 1995). Each half consists of both a hydrophobic and hydrophilic NBD, containing approximately 300 amino acids. Xenobiotics bind to separate sites on P-gp, demonstrating that different drugs and/or the different NBDs can each independently regulate P-gp function (Leveille-Webster and Arias, 1995). The gene responsible for encoding P-gp belongs to the ABCB family of ABC transporters, and is commonly known as the Multidrug Resistance 1 (MDR1) gene. There are 11 members of the ABCB family; however, the discussion here is restricted to the two commonly considered MDR gene families in humans MDR1 (ABCB1) and MDR2/3 (ABCB4) (Lincke et al., 1991; Germann et al., 1993). The human genome nomenclature details and chromosome location are provided in Table 7.1 and at the Web site http://www.gene.ucl.ac.uk/nomenclature/genefamily/ abc.html. It is worth noting that the homologous MDR3 and Bile-Salt Exporting Protein (ABCB11; sister of P-gp) are also important efflux transporters of ABCB subfamily and mainly involved in the active transport of bile salts across the hepatocyte canalicular membrane (Meier and Stieger, 2002). Both MDR3 and sister of P-gp demonstrated low expression and variable ethnicity-based expression in the human small and large intestines, which suggests that they play a minimal role in mediating oral absorption (Wang et al., 2004). In rodents, the gene responsible for the primary MDR isoforms’ expression are depicted by lower case letters as mdr1 (a and b) and mdr2 (Torok et al., 1999). The MDR1 gene product in humans and the mdr1a and mdr1b gene products in rodents confer resistance by effluxing xenobiotics from the cytosolic compartments in cells (Higgins, 1992). The MDR2 and mdr2 genes encode a protein primarily expressed in the bile canalicular membrane that is engaged in the transport of phosphatidylcholine into the liver bile canaliculi (Thiebaut et al., 1987; Smit et al., 1993). 7.2.1.1 The Expression of P-gp Immunohistological studies with human small intestinal samples indicate that P-gp is localized to the apical brush-border membrane of the intestinal epithelium (Thiebaut et al., 1987). Due to the localized expression of P-gp at the microvillous tip of enterocytes (Terao et al., 1996), P-gp will limit the absorption of compounds

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by directly effluxing them back into the intestinal lumen. Interestingly, the level of P-gp expression increases from proximal to distal regions of the intestine (Mouly and Paine, 2003). P-gp expression has also been demonstrated in kidney, adrenal gland, liver, colon, and lungs (Fojo et al., 1987; Gatmaitan and Arias, 1993). Additionally, P-gp expression in endothelial cells is lining the blood–tissue barrier that includes the brain capillaries (Thiebaut et al., 1989), implicating a protective functional role for P-gp through the active efflux of xenobiotics from the endothelial cytoplasm into the capillary lumen, as confirmed by Joly et al. (1995). P-gp is also expressed in the apical membrane of the placental syncytial trophoblasts, which faces the maternal blood compartment (Sugawara et al., 1988), and thus forms a functional barrier between the maternal and fetal blood circulations. Some peripheral blood mononuclear cells, such as cytotoxic T lymphocytes and natural killer cells, also express P-gp, suggesting involvement in cell-mediated cytotoxicity. Moreover, P-gp is expressed and functions in human hematopoietic stem cells, indicating it may contribute to the established chemoresistance of these cells (Chaudhary and Roninson, 1991; Drenou et al., 1993). Low-level P-gp expression is also found in prostate, skin, spleen, heart, skeletal muscle, stomach, and ovary (Fojo et al., 1987; Gatmaitan and Arias, 1993). 7.2.1.2 The Regulation of P-gp Expression Expectedly, P-gp expression can be modulated by various factors, such as xenobiotics, environmental stress, differentiating agents, and hormones under cell culture conditions. In the rat liver, P-gp expression was increased after acute treatment by chemical carcinogens including 2-acetylaminofluorene and aflatoxin B1 (Burt and Thorgeirsson, 1988), suggesting xenobiotic-mediated transcriptional induction. Furthermore, cholestasis or carbon tetrachloride intoxication has also resulted in increased P-gp expression in the liver of rodents and nonhuman primates (Schrenk et al., 1993). In addition to anticancer drugs, other xenobiotics, including protein kinase C agonists and chemical carcinogens, have been demonstrated to induce in vitro P-gp expression in several human carcinoma cell lines (Chaudhary and Roninson, 1993). Steroid hormones have also been shown to increase P-gp expression levels, as estradiol treatment has resulted in increased efflux of rhodamine 123 in rat pituitary cells expressing P-gp (Jancis et al., 1993). Moreover, concomitant rifampin therapy (600 mg/day for 10 days, p.o.) has also resulted in significant reduction in the area under the plasma concentration time curve (AUC) of oral digoxin (single-dose, 1 mg oral and 1 mg intravenous), a known P-gp substrate. Furthermore, rifampin treatment resulted in a threefold increase in intestinal P-gp levels that correlated with a decrease in the AUC of orally administered digoxin (Greiner et al., 1999). Differentiating agents such as retinoic acid and sodium butyrate also upregulate P-gp expression in human neuroblastoma and colon carcinoma cells, respectively (Bates et al., 1989; Mickley et al., 1989). Environmental stresses, such as heat, shock, arsenite, and cadmium chloride treatment have also been shown to affect in vitro P-gp expression (Chin et al., 1990).

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7.2.1.3 P-gp Mediated Drug Transport To explain the mechanism by which P-gp actively effluxes xenobiotics, two hypotheses have been postulated: the “hydrophobic vacuum cleaner” (HVC) and the “flippase model” (FM). The HVC model suggests P-gp clears substrates before they enter the cytoplasm (Higgins and Gottesman, 1992; Gottesman and Pastan, 1993). As such P-gp forms a hydrophilic pathway, and the drugs are transported from the cytosol to the extracellular media through the middle of a pore. Alternatively, the FM proposes that P-gp interacts with the xenobiotics as they enter through the lipid membrane and “flips” the drug from the inner leaflet to the outer leaflet and back into the extracellular media. Evidence also supports the presence of at least two allosterically coupled drug-binding sites (Ferry et al., 1992; Martin et al., 1997), although it is not clear if these sites facilitate drug efflux by separate mechanisms, it does suggest that they may convey broader P-gp substrate affinity. Cornwell et al. (1986) demonstrated the role of P-gp in the specific and saturable binding of vinblastine in membrane vesicles from highly multidrugresistant human KB carcinoma cell lines. The binding of vinblastine to P-gp is competitively inhibited by vincristine and daunorubicin, suggesting these compounds share the same binding site. Competitive binding studies using colchicine and actinomycin D revealed a lack of competition for the vinblastine-binding site, further supporting the findings of Ferry et al. (1992) and Martin et al. (1997) that P-gp has multiple drug binding domains (Cornwell et al., 1986; Akiyama et al., 1988). Shapiro et al. (1999) demonstrated that progesterone was not effluxed by P-gp, although it was shown to bind P-gp and block the efflux of other substrates. This study further supports the possibility of additional potential binding sites, or high affinity of endogenous progesterone to one of the two binding sites suggested above. There is still confusion about whether these studies have identified the same two binding sites or, in fact there are additional binding sites present in P-gp. As such, considerable research is required to elucidate the functional mechanisms of P-gp active efflux, as well as determine the structural moieties responsible for imparting such broad substrate specificity. 7.2.1.4 The Substrate Specificity of P-gp Numerous studies have demonstrated that P-gp possesses broad substrate specificity, with a preference for hydrophobic, amphipathic molecules containing a planar ring system ranging in size from 200 to 1,900 Da. P-gp is also involved in the transport of neutral compounds such as digoxin and cyclosporine A, negatively charged carboxyl groups such as those found on atorvastine and fexofenadine, and hydrophilic drugs such as methotrexate (Sharom, 1997). The degree of hydrogen bonding and partitioning into the lipid membrane has been determined to be a rate-limiting step for substrate interactions with P-gp (Seelig and Landwojtowicz, 2000). A representative list of substrates/inhibitors is listed in Table 7.2 and includes anticancer agents, antibiotics, antivirals, calcium channel blockers, and immunosuppressive agents.

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TABLE 7.2. Partial list of compounds that have shown to interact with P-glycoprotein Group

List of drugs

Anticancer drugs

Doxorubicin, daunorubicin, vinblastine, vincristine, actinomycin D, paclitaxel, teniposide, etoposide Cyclosporin A, FK506, valinomycin, gramicidine Lovastatin, atorvastatin, pravastatin, simvastatin Fexofenadine, terfenadine Loperamide, antiemetics, domperidone, ondansetron Ivermectin, itraconazole, dactinomycin, ketoconazole, erythromycin, valinomycin, grepafloxacin, actinomycin D Ritonavir, amprenavir, saquinavir, indinavir, nelfinavir

Immunosuppressive Lipid lowering agents Anti-Histaminic Antidiarrheal agents Antibiotic HIV protease inhibitors Steroids Cardiac drugs Analgesics Beta-Adrenoceptor antagonists Food/Herbal Constitutents Pharmaceutical excipients

Aldosterone, hydrocortisone, cortisol, corticosterone, dexamethasone, dopamine antagonist, domperidone Digoxin, digitoxin, quinidine Morphine, asimadoline, fentanyl Bunitrolol, carvedilol, celiprolol, talinolol Piperine, quercetin, naringin, curcumin, bergamottin, kaempferol, rutin Cremophor EL, Tween 20, Tween 80, Nonidet P-40, Acacia, polyethylene glycols, Triton-X 100, pluronic block copolymers, Brij 30 & 35, solutol HS 15, poloxamers, 1-[(3-cholamidopropyl)dimethyla amino]-1-propanesulfonate (CHAPS)

The table has been modified from the information reported by Fisher et al. (1996), Sikic (1997), Zuylen et al. (2000), and Kunta and Sinko (2004)

It is important to note that the substrate specificity of P-gp may also vary across populations due to genetic polymorphisms. For example, Kurata et al. (2002) demonstrated a significant difference in oral digoxin bioavailability between two allelically diverged MDR1 populations, which resulted in population specific absorption and/or distribution outcomes. A recent comprehensive review of the clinical implications of P-gp polymorphisms is suggested for additional information on this subject (Ieiri et al., 2004). While the observed absorption of numerous drugs may be affected by P-gp affinity, there are certain drugs that can also be used to inhibit P-gp activity (Krishna and Mayer, 2000). The P-gp inhibitors include several calcium channel blockers, immunosuppressive agents, and other well-characterized compounds such as SDZ, PSC 833, LY335979, and GF120918 (Table 7.2) (Hyafil et al., 1993; Schinkel and Jonker, 2003). Drug–drug interactions resulting from concomitant administration of P-gp substrates and/or inhibitors may result in an increase in the absorption of one or more of these agents, potentially leading to toxic side effects by virtue of increased plasma levels that rise above the minimum toxic concentration. There are numerous clinical examples of these effects being observed and reported in the literature. For example, a significant therapeutic increase in the plasma levels of digoxin was observed upon coadministration with valspodar (PSC833) in healthy patients. The increase in AUC of this narrow therapeutic

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index compound leads to the conclusion that the digoxin dose should be decreased by 50% and clinical toxicity of digoxin should be monitored upon concomitant administration of these two agents (Kovarik et al., 1999). In contrast, drug–drug interactions can also result in a lowering of the plasma levels of a therapeutic agent, where the coadministered drug induces P-gp expression that subsequently results in decreased plasma levels due to reduced absorption and increased intestinal clearance. One compelling example of this phenomenon was observed in a study with rifampin and fexofenadine (Hamman et al., 2001), where rifampin induced the intestinal expression of P-gp resulting in increased oral clearance and reduced bioavailability of fexofenadine. Additionally, common pharmaceutical excipients such as hydrophilic cyclodextrin (2,6-di-O-methyl-β-cyclodextrin) (Arima et al., 2001), cosolvents (poly(ethylene)glycol (PEG) 400), and surfactants (Tween 80, Cremophor EL) (Nerurkar et al., 1996; 1997; Hugger et al., 2002) have also been shown to inhibit P-gp activity. Given the obvious adverse effects this may have on drug bioavailability and overall therapy in general, excipient selection should be an important factor to be considered in rational formulation design. Surfactants or cosolvents have also been shown to indirectly influence P-gp by inducing changes in cellular membrane fluidity. Changes in membrane fluidity can alter the microenvironment of the apically oriented TM domain, and subsequently alter substrate recognition, binding, and efflux by P-gp (Ferte, 2002). Dietary constituents and phytochemicals also affect drug absorption and disposition through drug–nutrient P-gp interactions, potentially impacting the clinical pharmacokinetics of therapeutic agents (Walter-Sack and Klotz, 1996). Recently, it has been estimated in the US that approximately 30–50% of the population used a dietary supplement that can alter the absorption of drugs through mechanisms like competitive inhibition or transporter induction (Kauffman et al., 2002). The flavonoids, particularly the flavonols, flavanones, flavones as well as coumarins, and other ingredients present in fruits, vegetables, and herbs have been found to modulate the activity of P-gp function and may cause detrimental effects on drug pharmacokinetics (Izzo, 2005). One of the most recognized interactions is the ingestion of grapefruit juice and/or St John’s Wort which results in the change of the pharmacokinetic profiles of cyclosporine A and digoxin due to inhibition or induction of P-gp mediated transcellular intestinal epithelial absorption (Bailey et al., 1998; D¨urr et al., 2000). In a representative case, the oral coadministration of grapefruit juice with talinolol (10 mg/kg) resulted in an increased talinolol Cmax , AUC and a reduced tmax without significantly affecting the terminal talinolol halflife (Spahn-Langguth and Langguth, 2001). This study indicated that grapefruit juice acted to inhibit intestinal P-gp mediated efflux and resulted in enhanced talinolol bioavailability. In a separate study, 1 g of black pepper administered in soup along with phenytoin significantly increased the human plasma levels of phenytoin (Velpandian et al., 2001). Piperine, an active constituent of black pepper, is an inhibitor of P-gp and CYP3A4 (Bhardwaj et al., 2002). Orange juice components, such as methoxyflavones, increase vinblastine uptake by Caco-2 cells, possibly by interacting with P-gp (Honda et al., 2004). Therefore concomitant intake of herbal

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extracts or fruits/foods and nutraceuticals may modulate the pharmacokinetic profile of the therapeutic index of drugs, in particular for those agents exhibiting a narrow therapeutic index, resulting in an altered clinical response.

7.2.2 Multidrug Resistance-Associated Protein Family (MRP; ABCC) The human MRP gene encodes an MRP polypeptide with an apparent mass of 170 kDa, which is posttranslationally converted to a 190 kDa form by the addition of N-linked complex oligosaccharides (Almquist et al., 1995). To date, nine members within the MRP family have been identified, delineated as MRP1 to MRP6 (ABCC1 to ABCC6) and MRP7 to MRP9 (ABCC10 to ABCC12) (Belinsky et al., 1998; Bera et al., 2001, 2002; Kubota et al., 2001; Kruh and Belinsky, 2003). Membrane topology of MRP1 identified three transmembrane spanning domains (two are of 6 and the third is of 5 helices) (Fig. 7.1) (Bakos et al., 1996; Hipfner et al., 1997). Additionally, MRP1 has two NBDs and two intracellular loops with the first linker segment located between two transmembrane domains (TMD) whereas the second is between the transmembrane and a nucleotide binding domains. MRP4 (ABCC4), MRP5 (ABCC5), MRP8 (ABCC11), and MRP9 (ABCC12) lack the five helices of the third membrane spanning domain, but possess intracellular loops whereas MRP2 (ABCC2), MRP3 (ABCC3), MRP6 (ABCC6), and MRP7 (ABCC10) resemble MRP1 (Bera et al., 2001, 2002; Hopper et al., 2001; Yabuuchi et al., 2001; Kruh and Belinsky, 2003). The human genome nomenclature details and chromosomal localization of important intestinal MRP transporters are listed in Table 7.1. 7.2.2.1 The Expression of MRPs MRP1 was first cloned by Cole et al. (1992) from a multidrug-resistant human lung cancer cell line. MRP1 is highly expressed in the intestine, where it is localized in the basolateral membrane of intestinal epithelial cells and is involved in the absorptive efflux of its substrates into blood (Peng et al., 1999). MRP1 expression has also been demonstrated in brain, liver, lung, kidney, and testis (Flens et al., 1996; Zhang et al., 2000; Cherrington et al., 2002). Comparatively, MRP2 was first cloned from human liver as a canalicular multispecific organic anion transporter (cMOAT) (Paulusma et al., 1996) and was found to be expressed on the apical membrane of the intestine, liver, and kidney tubules (Schaub et al., 1997; Fromm et al., 2000; Scheffer et al., 2002). Furthermore, MRP2 expression was localized in the brush-border membrane of villi in rabbit small intestine with expression decreasing from the villous tip to the crypt (Van Aubel et al., 2000). Moreover, MRP2 expression was shown to vary substantially along the human GI tract, with higher expression levels found in small intestine and minimal expression observed in colonic segments (Zimmermann et al., 2005). MRP3 expression is also evident in the basolateral membrane of the small intestine, as well as the liver, colon, lung, spleen, and kidney (Kool et al., 1997;

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Scheffer et al., 2002). Rost et al. (2002) identified MRP3 mRNA expression throughout the rat intestine by RT-PCR, with lower expression observed in the duodenum and jejunum and markedly increased expression observed in the ileum and colon. MRP4 is expressed in several tissues including jejunum, kidney, brain, lung, and gall bladder (Kool et al., 1997; Zhang et al., 2000; Taipalensuu et al., 2001; Van Aubel et al., 2002). MRP5 expression is found to be in colon, liver, kidney, skeletal muscle, and brain (Kool et al., 1997; McAleer et al., 1999; Zhang et al., 2000), while MRP6 is highly expressed in the kidney and liver, with low expression in several other tissues, including duodenum, colon, brain, and salivary gland (Zhang et al., 2000). The functional activity and expression of MRP7, MRP8, and MRP9 have not been as well characterized, although they are gaining increasing attention due to their involvement in conveying multidrug resistance (Bera et al. 2002; Kruh and Belinsky, 2003; Chen et al., 2003, 2005; Hopper-Borge et al., 2004). The comparative mRNA expression of MRPs 1–5 and P-gp studied in ten healthy human intestinal tract biopsy samples (Zimmermann et al., 2005) demonstrated a rank order expression of MRP3 MDR1 > MRP2 > MRP5 > MRP4 > MRP1 and MDR1 > MRP3 MRP1 in the duodenum and the terminal ileum, respectively. The comparative rank order of mRNA expression remains consistent throughout the colon (ascending, transverse, descending, and sigmoid colon) with MRP3 MDR1 > MRP4 ∼ = MRP5 > MRP1 MRP2. Total RNA analysis by Taipalensuu et al. (2001) showed a ranking of BCRP ∼ = MRP2 > MDR1 ∼ = MRP3 ∼ ∼ ∼ = MRP6 = MRP5 = MRP1 > MRP4 > MDR3, as determined by quantitative polymerase chain reaction in jejunal biopsies from 13 healthy human subjects. Direct comparison between the expression of BCRP, MDR1, MDR3, and MRP1–6 found that BCRP exhibited a 100-fold lower transcript level in Caco-2 cells compared with jejunum (Taipalensuu et al., 2001). Furthermore, based upon the ratio of MRP:18S rRNA expression, the rank order for MRP expression in Caco-2 cells was MRP2 ≥ MRP6 > MRP4 ≥ MRP3 > MRP1 = MRP5 (Prime-Chapman et al., 2004). Our laboratory also observed a variable intestinal expression of P-gp, MDR3, S-P-gp, LRP, MRP1–4 in tissue slides and intestinal protein lysates obtained from normal adult small and large intestinal epithelium of Chinese and Caucasian donors (Wang et al., 2004). Furthermore, the expressions of P-gp, MDR3, LRP, MRP1, and MRP2 were found to be higher in the small intestine of Chinese when compared to Caucasian samples, suggesting distinct expression profiles of these transporters (Wang et al., 2004). Interestingly, there has been concern over the variable expression of these isoforms in traditional cell screening models (e.g., Caco-2 cells), as compared with normal physiological expression observed in patients. Given the widespread use of these screening models, these differences in transporter expression may not properly translate to the actual physiological condition resulting in selection of lead candidates based on erroneous data. Finally, immunofluorescent staining of Caco-2 cells revealed the apical and basolateral localizations of MRP2 and MRP3, respectively, whereas MRP1 was not observed on either membrane (Prime-Chapman et al., 2004).

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7.2.2.2 The Regulation of MRP Isoform Expression Nuclear receptors that regulate transcription of MRP2 proteins include pregnane X receptor (PXR, NR1I2), farnesoid X receptor (FXR, NR1H4), and the constitutive androstane receptor (CAR, NR1I3) (Tanaka et al., 1999; Kast et al., 2002). Data indicate that steroid-based compounds, such as bile acids, cholesterol, hormones, as well as other drugs, including rifampin, dexamethasone, and phenobarbital, all affect the above nuclear receptors. For example, the mRNA expression of MRP2 was up regulated in the presence of rifampin in the rhesus monkey liver, human small intestine, and primary cultures of human hepatocytes (Kauffmann et al., 1998; Fromm et al., 2000; Dussault et al., 2001). Moreover, rats treated with known PXR ligands, such as the antiglucocorticoid/antiprogestin RU486 and the antifungal clotrimazole, showed upregulation of MRP2 gene expression, suggesting a role of PXR ligands in regulation of MRP2 expression (Courtois et al., 1999). Naturally occurring chenodeoxycholic acid, synthetic GW4064, and known FXR ligands have induced MRP2 mRNA expression in both human and rat hepatocytes. Finally, the CAR agonist, phenobarbital also induced MRP2 mRNA expression (Kast et al., 2002). Intestinal MRP3 mRNA has been shown to increase in a dose- and timedependent manner in Caco-2 cells after treatment with a series of bile salts, chenodeoxycholic acid, taurochenodeoxycholic acid, taurocholic acid, and taurolithocholic acid (Inokuchi et al., 2001). Additionally, the glucocorticoids, dexamethasone, and hydrocortisone have also been shown to transcriptionally upregulate human MRP3 mRNA in a non-small-cell lung cancer cell line, which was correlated to an increase in MRP3 protein (Pulaski et al., 2005). Recently, the involvement of various transcriptionally mediated pathways aryl hydrocarbon receptor (AhR), PXR, CAR, peroxisome proliferator-activated receptor α (PPARα), and nuclear factor-E2-related factor 2 (Nrf2) were studied on the induction of MRPs (Maher et al., 2005). The mRNA expression of MRP2, 3, 5, and 6 were shown to be induced using AhR ligands [2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD),polychlorinated biphenyl126 (PCB126),and β-naphthoflavone],whilethe CAR activator 1,4-bis[2-(3,5-dichloropyridyloxy)]benzene (TCPOBOP) induced MRP2, 3, 4, 6, and 7 mRNA expression. In addition, Nrf2 activators (butylated hydroxyanisole, oltipraz, and ethoxyquin) induced MRP2–6, further supporting the relevance of transcription factors with respect to MRP regulation (Maher et al., 2005). 7.2.2.3 The Substrate Specificity of MRP’s The substrate specificity and function of MRP family members have been extensively reviewed by Kruh and Belinsky (2003), and will only be briefly discussed here. MRP1 is involved in the transport of anionic drugs (e.g., methotrexate), drug or metabolite conjugates (glutathione, glucuronate, or sulfate), leukotriene C4 (LTC4), 2,4-dinitrophenyl-S-glutathione(DNP-SG), bilirubin glucuronides, estradiol-17-glucuronide, and dianionic bile salts (Leier et al., 1994; Jedlitschky

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TABLE 7.3. Partial list of substrates that have shown to interact with Multidrug ResistanceAssociated protein Transporter

Drugs/xenobiotics

Physiological substrates

MRP1

Vinca alkaloids, epipodophyllotoxins, anthracyclines, camptothecin, MTX

MRP2

Vinca alkaloids, anthracylines, vincristine, etoposide camptothecin, MTX, ochratoxin A, p-Aminohippurate Etoposide, MTX 6-MP, MTX, PMEA 6-MP, PMEA Anthracycline, etoposide ? 5-FU, ddC, PMEA ?

LTC4, estrogen glucuronide, leukotriene E4, S-Glutathionyl prostaglandin A2, bilirubin (monoglucuronosyl, bisglucuronosyl) Bilirubin glucuronide DNP-SG, S-Glutathionyl ethacrynic acid, 17β-glucuronosyl estradiol

MRP3 MRP4 MRP5 MRP6 MRP7 MRP8 MRP9

Glycocholic acid Cyclic nucleotides, DHEAS Cyclic nucleotides ? 17β-estradiol 17-(β-D-glucuronide)? Cyclic nucleotides ?

LTC4, cysteinyl leukotriene; MTX, methotrexate; 6-MP, 6-mercaptopurine; PMEA, 9-(2phosphonylmethoxyethyl)adenine; 5-FU, 50-fluorouracil; ddC, zalcitabine; DHEAS, dehydroepiandrosterone sulfate; DNP-SG, S-glutathionyl 2,4-dinitrobenzene. Table modified from the information reported by Konig et al. (1999) and Kruh and Belinsky (2003)

et al., 1996; Nies et al., 1998; Hooijberg et al., 1999). Representative substrates of MRP1 are listed in Table 7.3 and include anthracyclines, vinca alkaloids, pipodophyllotoxins, and camptothecins (Cole et al., 1994; Konig et al., 1999; Kruh and Belinsky, 2003). Several compounds are also known to be either direct or indirect inhibitors of MRP1. Verapamil and its analogs, in the presence of reduced glutathione (GSH) or its nonreducing S-methyl derivative, inhibited LTC4 transport in membrane vesicles prepared from MRP1-transfected cells. Verapamil itself was not transported by MRP1 in either intact cells or membrane vesicles, suggesting that verapamil modulates MRP1 activity through enhancing MRP1 mediated GSH transport (Loe et al., 2000a,b). Leslie et al. (2003) demonstrated a similar effect on MRP1 mediated GSH transport in the presence of bioflavonoids, possibly through an interaction(s) with one of the several multiple flavonoid binding sites (Trompier et al., 2003). Similarly, MRP2 transports leukotrienes C4 , D4 , and E4 and various glutathione conjugates, including oxidized glutathione, 2,4-dinitrophenyl-S-glutathione, bromosulfophthalein glutathione, as well as those conjugates of heavy metals including arsenic and cadmium (Jedlitschky et al., 1997; Madon et al., 1997; Suzuki and Sugiyama, 1998, 1999). The glucuronide conjugates of bilirubin, estradiol, acetaminophen, grepafloxacin, triiodo-L-thyronine, and SN-38 have also been demonstrated to be MRP2 substrates (Suzuki and Sugiyama, 1998, 1999). Additionally, MRP2 also transports glucuronide and sulfate conjugates of several bile salts, a range of unconjugated organic anions such as methotrexate, reduced

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folates, bromosulfophthalein, irinotecan and its metabolite SN-38, pravastatin, ceftriaxone, temocaprilat, ampicillin as well as Fluo-3 and p-aminohippurate (Konig et al., 1999; Suzuki and Sugiyama, 1998, 1999; Kusuhara and Sugiyama, 2002). Keppler et al. (1997) have suggested the transport efficiency (Vmax /K m ) of MRP2 substrates in the rank order of leukotriene C4 > leukotrieneD4 > S(2,4-dinitrophenyl)-glutathione > monoglucuronosyl bilirubin > estradiol-17βD -glucuronide > taurolithocholate sulfate > oxidized glutathione. The overlapping substrate specificity of MRP2 with P-gp, coupled with their intestinal and cellular colocalization to the apical membrane, suggests a concerted function between these two transporters that would comprise a significant barrier to the intestinal absorption of many xenobiotics. Interestingly, grepafloxacin uptake was observed to be directly influenced by the combined effect of P-gp and MRP2. However, the secretory efflux of grepafloxacin was shown to be predominantly a function of MRP2 activity as opposed to P-gp, both in vitro and in vivo, suggesting that in spite of some overlapping substrate specificity, MRP members may also act independently as a functional barrier to bioavailability (Naruhashi et al., 2002). Recently, MRP2 has also been shown to be involved in the efflux of a tobaccospecific carcinogen, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, and the food carcinogen PhIP, reducing their oral bioavailabilities, thus protecting against foodderived carcinogenesis (Dietrich et al., 2001; Leslie et al., 2001). In addition, bioavailability of fungal toxin ochratoxin A and the tea flavonoid epicatechin were inhibited by MK571, an antagonist of MRP2, suggesting these as MRP2 substrates (Leier et al., 2000; Vaidyanathan and Walle, 2001). Similar to both MRP1 and MRP2, MRP3 also has the capacity to transport organic anionic drugs and glucuronate-conjugated drugs; however, MRP3 exhibits a reduced capacity for GSH conjugates (Hirohashi et al., 1999), as well as a wide range of bile salts such as glycocholate, taurolithocholate-3-sulfate, and taurochenodeoxycholate-3-sulfate (Hirohashi et al., 2000; Zeng et al., 2000; Zelcer et al., 2001). In addition MRP3, in contrast to MRP1 and MRP2, does not require glutathione for mediating the transport of natural products (Zelcer et al., 2001). Substrates for MRP4 include folic acid, folinic acid (leucovorin), and methotrexate (Chen et al., 2002), as well as cAMP, cGMP, estradiol-17β-D-glucuronide, bile acids (Chen et al., 2001; Lai and Tan, 2002; Van Aubel et al., 2002; Zelcer et al., 2003), and thiopurines (Wielinga et al. 2002). Recent literature also indicates that MRP4 is involved in the efflux of camptothecins, as shown in human MRP4 stably transfected HepG2 cells (Tian et al., 2005). MRP5 has an affinity for nucleotide-based substrates including anticancer thiopurine and thioguanine drugs, as well as the anti-HIV drug 9-(2-phosphonylmethoxyethyl)adenine (Wijnholds et al., 2000; Wielinga et al. 2002; Reid et al., 2003). Interestingly, organic anion such as benzbromarone and sulfinpyrazone inhibit MRP5, suggesting an affinity for anionic phosphate/phosphonate moieties (Wijnholds et al., 2000). With respect to the remaining MRP family members, MRP6 was shown

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to be involved in the transport of different natural cytotoxic agents such as etoposide, doxorubicin, and cisplatin in MRP6-transfected Chinese hamster ovary (CHO) cells (Belinsky et al. 2002), while MRP7 is also a lipophilic anion efflux transporter with an affinity for docotaxel and 17β-estradiol-(17β-D-glucuronide) (Chen et al., 2003; Hopper-Borge et al., 2004). MRP8 has been demonstrated to mediate the efflux of cyclic nucleotides (Guo et al., 2003).

7.2.3

Breast Cancer Resistance Protein (BCRP; ABCG2)

The ABC transporter BCRP was first cloned from a doxorubicin-resistant MCF7 breast cancer cell line (MCF-7/AdrVp) by Doyle et al. (1998). Subsequently, other groups cloned the BCRP cDNA sequence from other sources and designated the gene either MXR (mitoxantrone resistance protein) or ABCP (placental ABC protein) (Allikmets et al., 1998; Maliepaard et al., 1999). Since structural and sequence homology revealed that BCRP belongs to the ABCG gene subfamily, the Human Genome Nomenclature Committee conferred the official designation as ABCG2 (Table 7.1). BCRP consists of six putative TMD involved in drug binding and efflux, as well as a single amino-terminal cytosolic NBD that functions as an ABC involved in ATP hydrolysis. BCRP has been suggested to be a half-transporter that may function as a homodimer or tetramer bridged by disulfide bonds (Xu et al., 2004). Study of BCRP tissue localization shows that it is expressed in the small intestine, colon, liver, placental syncytiotrophoblasts, and ovary (Maliepaard et al., 2001). Further, a monoclonal antibody directed against BCRP used for immunohistochemical staining revealed prominent expression at the apical membrane of the small intestinal, colonic epithelia, and in hepatocyte canalicular membranes (Scheffer et al., 2000; Maliepaard et al., 2001). Comparatively, BCRP mRNA expression was found to be higher than all other ABC transporters measured (MDR1, MDR3, MRP1–6) in human jejunal mucosa biopsies obtained from 13 healthy volunteers (Taipalensuu et al., 2001). Moreover, with villin-normalized data expression levels between Caco-2 cells versus jejunal samples varied less than 2.5-fold for ABC transporter genes, whereas Caco-2 cells exhibited a 100fold lower transcript copy number in contrast to the human jejunum for BCRP (Taipalensuu et al., 2001). Furthermore, later passage (p56) Caco-2 cells showed higher expression levels of BCRP in comparison to earlier passage (p33) cells, suggesting culturing conditions or epigenetic factors also influence BCRP expression (Taipalensuu et al., 2001). Immunofluorescence studies demonstrated the polarized apical membrane expression of BCRP in Caco-2 cells (Xia et al., 2005). BCRP mRNA expression quantified by quantitative real-time PCR along the GI tract of 14 healthy subjects demonstrated expression to be maximal in the duodenum and continuously decreasing toward the rectum (terminal ileum 93.7%, ascending colon 75.8%, transverse colon 66.6%, descending colon 62.8%, and sigmoid colon 50.1% compared to duodenum, respectively) (Gutmann et al., 2005).

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Interestingly, gender-based variation was also observed for BCRP expression, although the biological relevance of these data has not yet been elucidated. BCRP transports estrone 3-sulfate and 17β-estradiol-3-sulfate, while the parent steroids are not substrates (Imai et al., 2003). BCRP also transports the active glucuronide conjugate metabolite of SN-38 (SN-38-glucuronide) with an approximately sevenfold lower affinity than the parent SN-38 itself, suggesting the involvement of BCRP in transporting glucuronide conjugated compounds (Nakatomi et al., 2001). BCRP also mediates the secretion of other clinically important camptothecans, such as topotecan. Jonker et al. (2000) observed a more than sixfold decrease in topotecan plasma concentrations upon concomitant administration of GF120918 (an inhibitor of both P-gp and BCRP), in P-gp deficient mice. This study shows that BCRP mediates apically directed drug transport, which would reduce drug bioavailability. As such, the coadministration of an effective BCRP inhibitor may increase the oral bioavailability of topotecan. Interestingly, fumitremorgin C, an extract of Aspergillus fumigatus, selectively inhibits BCRP with no overlapping affinity for P-gp, as demonstrated by reversing resistance mediated by MCF-7 cells transfected with the BCRP gene (Rabindran et al., 1998; Ozvegy et al., 2001). These observations suggest that the oral absorption of therapeutic compounds specifically designed to be substrates of BCRP would be enhanced. A more comprehensive summary of various BCRP substrates can be found in Table 7.4. TABLE 7.4. Partial list of compounds shown to interact with breast cancer resistance proteins Substrates

Inhibitors

Anti-tumor drugs Mitoxantrone Methotrexate, Camptothecins (SN-38, topotecan) CI1033 NB 506 J-107088 Flavopiridol

GF120918 XR9576 VX-710 Diethylstilbestrol Taxanes Falvonoids Imatinib Gefitinib

Endogenous substrates Estrone 3-sulfate 17 β-estradiol sulfate 17β-Estradiol 17-(β-D-glucuronide Folic acid Protoporphyrin IX

Fumitremorgin C Tamoxifen HIV protease Inhibitors Novobiocin UCN-01

Fluorescent dyes BODIPY-Prazosin Hoechst 33342 BBR 3390 The table has been modified from the information published by Sarkadi et al. (2004), Leslie et al. (2005) and Staud and Pavek (2005)

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195

Solute Carrier Transporters

The solute carrier (SLC) transporter superfamily is comprised of integral membrane proteins that mediate substrate transport across cell surface or cellular organelle, such as the golgi apparatus or synaptic vesicle membranes, by either a facilitated diffusion or an active transport mechanism. The function of SLC transporters may also be coupled to the cotransport of a counterion down its electrochemical gradient (e.g., Na+ , H+ , and Cl− ). The members of the SLC superfamily are involved in the transport of a wide range of substrates including amino acids, peptides, sugars, vitamins, bile acids, neurotransmitters, and xenobiotics. There are several reviews on the SLC family members identified across species that the reader is directed for more information (Saier et al., 1999; Saier, 2000; Hediger et al., 2004). The Human Genome Organization (HUGO) Nomenclature Committee Database provides a comprehensive list of SLC transporter genes which are subdivided into 43 distinct subfamilies consisting of 319 separate isoforms (Hediger et al., 2004; see http://www.gene.ucl.ac.uk/nomenclature/). The SLC superfamily includes many pharmacokinetically important transporters such as proton dependent oligopeptide transporters (SLC15A family), organic cation transporters (SLC22A family), organic anion transporting polypeptides (SLC21A family), nucleoside transporters (SLC28, 29A family), and the monocarboxylate transporters (SLC16A family). While many other SLC subfamilies exist, these subfamilies are thought to significantly influence the bioavailability of their respective substrates and are relevant to the observed PK/PD profiles for many existing drugs and are increasingly being utilized for NCE screening in the drug discovery phase. The classifications and molecular characteristics of several important intestinal SLC transporters are described below.

7.3.1

Proton/Oligopeptide Transporters (POT; SLC15A)

With the elucidation of the human genome, and advances in molecular biology and cloning, it should be of no surprise that peptidomimetic drugs are increasingly utilized as therapeutic agents for the treatment of numerous disorders. Peptidelike agents have a broad range of clinical applications in the treatment of many disorders including AIDS, hypertension, and cancer. The currently known peptide transporters include the Peptide Transporters 1 and 2, PepT1 (SLC15A1) and PepT2 (SLC15A2); the Peptide/Histidine Transporters 1 and 2, PHT1 (SLC15A4) and PHT2 (SLC15A3); and the Intestinal Peptide Transporter PT1 (CDH17). PT1 is the only protein identified which is not classified as a member of the proton oligopeptide transporter (SLC15) family, also known as the proton-coupled oligopeptide transporter (POT) superfamily (Fei et al., 1998; Meredith and Boyd, 2000). In fact, PT1 is considered a member of the cadherin family. PepT2 expression has not been shown in the GI tract and thus it will not be discussed in this chapter.

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Several comprehensive reviews can be found describing the common characteristics of the oligopeptide transporter proteins (Graul and Sad´ee, 1997; Nussberger et al., 1997a; Yang et al., 1999; Meredith and Boyd, 2000; Rubio-Aliaga and Daniel, 2002; Herrera-Ruiz and Knipp, 2003). Members of the POT superfamily are predicted to contain 12 predicted transmembrane α-helical spans, with a majority of the proteins having intracellulary localized N- and C-termini (Covitz et al., 1998; Lee, 2000; Herrera-Ruiz and Knipp, 2003). Two characteristic protein signatures of the POT family members have been identified, known as the PTR2 family signatures (Steiner et al., 1995): (1) [GA] – [GAS] – [LIVMFYWA] – [LIVM] – [GAS] – D – x – [LIVMFYWT] – [LIVMFYW] – G – x(3) – [TAV] – [IV] – x(3) – [GSTAV] – x – [LIVMF] – x(3) – [GA] and (2) [FYT] – x(2) – [LMFY] – [FYV] – [LIVMFYWA] – x – [IVG] – N – [LIVMAG] – G – [GSA] – [LIMF], and a third consensus proposed has been proposed by Fei et al. (1998) (GTGGIKPXV). Saier et al. (1999) have proposed three different signature sequences associated with the POT superfamily based on their phylogenetic analysis. The cloned human PepT1 cDNA sequence encodes a 708 amino acid protein with an estimated molecular weight of 79 kDa, and an isoelectrical point of 8.6 (Liang et al., 1995). PepT1 has shown expression in several animal species (Fei et al., 1994, 2000; Saito et al., 1995; Chen et al., 1999; Pan et al., 2001; Klang et al., 2005; Van et al., 2005), each exhibiting high homology with other species. PepT1 protein expression has been demonstrated in the human small intestine (Liang et al., 1995; Herrera-Ruiz et al., 2001) and was localized on the apical plasma membrane of enterocytes in rats (Ogihara et al., 1999). Other studies have demonstrated that PepT1 isoforms are localized intracellularly in lysosomes (Zhou et al., 2000 Sun et al., 2001). Apical expression of PepT1 has been established in both prenatal and mature animals (Shen et al., 2001; Rome et al., 2002). Furthermore, PepT1 cellular localization has been demonstrated to vary with the stage of animal development. Hussain et al. (2002) revealed that PepT1 is exclusively expressed in the apical membrane of enterocytes from both prenatal and mature animals; however, immunolocalization studies showed that immediately after birth, PepT1 was also expressed intracellularly in the basal cytoplasm, as well as the basolateral membrane of the intestinal epithelium. Recently, two putative human peptide/histidine (hPHT) transporters have been identified with expression observed in several human tissues (Botka et al., 2000; Knipp and Herrera-Ruiz, 2004). The hPHT1 mRNA sequence is approximately 2.7 kb long, encoding a translated 577 amino acid protein with an estimated molecular weight of 62 kDa, and a predicted pI of 9.2. Four N-linked glycosylation sites were predicted, along with several protein phosphorylation sites (Herrera-Ruiz and Knipp, 2003). Human PHT2 has not widely been studied, and little is known about its biological significance. PHT2 was first isolated from the human placenta and has an open reading frame of 1.7 kb, encoding a protein with 581 amino acids with an estimated molecular mass of 64.6 kDa. Only the rat isoform of the PHT2 protein has been partially evaluated (Sakata et al., 2001). Three N-linked glycosylation sites on the rPHT2 protein are predicted and protein phosphorylation

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sites (PKA and PKC) were identified (Herrera-Ruiz and Knipp, 2003). Orthologous expression in rat and mouse has been reported for both PHT1 and PHT2 (Yamashita et al., 1997; Botka et al., 2000; Sakata et al., 2001). Both hPHT1 and hPHT2 have shown expression along the entire GI tract, especially in the small intestine and colon (Herrera-Ruiz et al., 2001), an important difference in relation with hPepT1. Expression of hPHT1 has been shown in the plasma membrane of intestinal tissue segments (Bhardwaj et al., 2005a). Studies have demonstrated that PHT1 and PHT2 are expressed in the human and rat GI tracts and in Caco-2 cells (Herrera-Ruiz et al., 2001). Intracellular localization rPHT2 expression has also been demonstrated intracellular localization in lysosomes, autophagosomes, and vacuoles of HEK-293T and baby hamster kidney (BHK) cells (Sakata et al., 2001). The human Intestinal Peptide Transporter 1 (HPT1) has a cDNA coding region of 2.5 kb long, encoding a 120 kDa protein comprising 832 amino acids. While HPT1 is related to the cadherin family of proteins (Dantzig et al., 1994), it has demonstrated peptide and cephalosporin transport activity (Yang, 1998). Since it is not a POT family member, it will not be discussed further. 7.3.1.1 Peptide Transporter Mediated Transport Transepithelial peptide transporters use a proton gradient and membrane potential to provide the necessary driving force for substrate translocation (Daniel, 1996; Adibi, 1997; Nussberger et al., 1997a,b). The required proton gradient is generated through the activity of an electroneutral proton/cation exchanger, the Na+ /H+ antiporter (Meredith and Boyd, 2000; Theis et al., 2001). Peptide or peptidomimetic molecule uptake is commensurate with the translocation of a proton into epithelial cells, making substrate uptake strongly dependent on the extracellular pH, where a pH of 4.5–6.5, depending on the net charge of the substrate, is optimal for transport activity (Temple et al., 1995, 1996; Amasheh et al., 1997; Balimane and Sinko, 2000; Kottra et al., 2002). The optimal extracellular pH for both PepT1 and PHT1 has been estimated to be 6.0 and 5.0, respectively. In addition, this model suggests that peptides which are not appreciably degraded intracellularly are transported out of the cells by an as-yet unidentified basolateral peptide transporters which have lower affinities than the PepT-like transporters (Terada et al., 1999, 2000b; Irie et al., 2001, 2004). It has been established that all PepT1 substrates share the same substratebinding site due to the fact that uptake strictly conforms to the Michaelis–Menten equation and exhibits the competitive inhibition regardless of substrate charge (Wenzel et al., 1996; Mackenzie et al. 1996; Sawada et al., 1999). Proton coupling occurs in the H+ -binding site of PepT1, where a H+ is bound prior to anionic or neutral substrate uptake but is not required for cationic substrates (Nussberger et al., 1997b). This model assumes that anionic substrates cannot access the substrate-binding site lacking a H+ , and that the protonated substrate-binding site can accept only negatively charged substrates. Recently, a computational model of the H+-coupled substrate transport of neutral and charged molecules has been published (Irie et al., 2005). The authors established a PepT1 mechanistic model

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demonstrating the normally observed bell shaped uptake versus pH shaped curves for different charged substrates based on two novel main hypotheses: (1) H+ binds to not only the H+ -binding site, but also the substrate-binding site; and (2) H+ at the substrate-binding site inhibits the interaction of neutral and cationic substrates, but is necessary for that of anionic substrates. Studies have shown the importance of certain amino acid residues in the transport activity of PepT-like proteins (Fei et al., 1997; Bolger et al., 1998; Yeung et al., 1998; Chen et al., 2000; Meredith, 2004). Studies utilizing PepT1/PepT2 chimeras suggest that the first half of the transporters (first 400 residues) contain substrate-binding domain segments as well as defining other functional properties (D¨oring et al., 1996, 2002; Terada et al., 2000a; Kulkarni, 2003a,b). The significance of the PepT chimeras with respect to these transporters is unknown since similar studies have not been conducted for PHT1/PHT2 peptide transporters. 7.3.1.2 The Substrate Specificity of Peptide Transporters The PepT-Like Transporters Studies elucidating the substrate specificity of PepT1 indicate that this protein transports almost all possible dipeptides, tripeptides, as well as numerous peptidomimetics and that their respective sizes or molecular weights were not significant factors (Leibach and Ganapathy, 1996). Analysis of the binding and transport characteristics of PepT1 have led to the development of several molecular models attempting to establish a PepT-substrate template (Swaan and Tukker, 1997; Bailey et al., 1999; Zhang et al., 2002a,b; Gebauer et al., 2003; Biegel et al., 2005). Brandsch et al. (2004) summarized the substrate structural characteristics required for high PepT1 affinity (100 µM) to α Blocker Prazosin was much higher when compared to hOCT1 (1.8 µM) and hOCT3 (13 µM) (Hayer-Zillgen et al., 2002). Members of the OCTN subfamily have differential abilities to interact with a variety of organic cation drugs, as well as carnitine (Koepsell and Endou, 2004; You, 2004). For example, TEA is a substrate for rat, mouse, and human OCTN1 (Tamai et al., 1997, Wu et al., 2000a) and OCTN2 (Wu et al., 1998, 1999; Tamai et al., 1998; Friedrich et al., 2003), but not for OCTN3, at least for mouse isoform (Tamai et al., 2000b). Rat OCTN1 and hOCTN1 exhibit a very low affinity for

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carnitine (Tamai et al., 1997; Wu et al., 2000a), but mOCTN1 mediates significant transport of carnitine (Tamai et al., 2000b), suggesting a species difference of substrate specificity. In addition, OCTN2 from all species (Wu et al., 1998; Tamai et al., 2000b; Friedrich et al., 2003), and mOCTN3 (Tamai et al., 2000b) exhibit medium and very high affinity for carnitine, respectively. 7.3.3.2 Organic Cation Transporter Mediated Transport Several common transport properties have been identified for all OCTs and independent from subtype or species. OCTs translocate organic cations and other compounds in an electrogenic manner, which has been shown for the rat isoforms rOCT1, rOCT2, and rOCT3 (Busch et al., 1996; Nagel et al., 1997; Kekuda et al., 1998; Okuda et al., 1999), and for the human transporters hOCT1 and hOCT2 (Busch et al., 1998; Gorboulev et al., 1997). In addition, OCTs medicated transport is independent from Na+ and H+ ions (Busch et al., 1996; Gorboulev et al., 1997; Kekuda et al., 1998). Driving force for substrate transport is provided by the substrate concentration gradient and the membrane potential (Busch et al., 1996, 1998; Gorboulev et al., 1997; Kekuda et al., 1998; Okuda et al., 1999). As such, OCTs are able to translocate substrates across the plasma membrane in either direction (Busch et al., 1996, 1998; Nagel et al., 1997; Kekuda et al., 1998). In contrast to OCTs, OCTN mediated transport mechanism depends largely on the isoform and substrate tested. Human and rat OCTN1 work as H+ /organic cation antiporters that mediate transport of tetraethylammonium (TEA) and other organic cations (Tamai et al., 1997; Wu et al., 1998). However, mOCTN1 mediate carnitine transport in a Na+ /dependent manner (Tamai et al., 2000b). OCTN2 is a Na+ /carnitine cotransporter with a high affinity for carnitine (Tamai et al., 1998, 2001; Wu et al., 1999; Wagner et al., 2000). It can also function alternatively as a polyspecific cation uniporter in a Na+ -independent manner (Tamai et al., 1998, 2001; Wu et al., 1999; Wagner et al., 2000). In the presence of Na+ , hOCTN2 could transport short-chain acyl esters of carnitine (Ohashi et al., 1999) as well as zwitterions, e.g., cephaloridine (Ganapathy et al., 2000). Furthermore, OCTN2 mediated transport is electrogenic and pH-dependent (Wagner et al., 2000). OCTN3 was found only in mice (Tamai et al., 2000b). In contrast to OCTN2 from different species, mOCTN3 transports carnitine independently from Na+ and demonstrates less affinity for organic cation compared to OCTN1 and OCTN2 (Tamai et al., 2000b). 7.3.3.3 The Expression of Organic Cation Transporters The tissue distribution and membrane localization of the OC family of proteins have been studied using different approaches. In each case, it has been demonstrated that the respective isoforms have different tissue expression patterns that can vary with species (Koepsell et al., 2003; You, 2004). In general, OCT isoforms are mainly expressed in the liver or kidney, and may also be found, to a less extent, in the heart, skeletal muscle, placenta, and small intestine. hOCT1 is mainly expressed in the liver, whereas hOCT2 is mainly found in the kidney

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(Gorboulev et al., 1997). hOCTN1 and hOCTN2 are both abundantly expressed in the kidney, skeletal muscle, placenta, prostate, and heart (Tamai et al., 1997, 1998; Wu et al., 1999), with hOCTN2 also being expressed at low level in the liver (Tamai et al., 1998). Although hOCTN1 is expressed strongly in the kidney (Tamai et al., 1997), rOCTN1 is present principally in the liver (Wu et al., 2000a). For a more detailed description of the tissue distribution of OCT and OCTN isoforms, the reader is directed to the reviews by Koepsell et al. (2003) and You (2004). The following sections will focus on the expression of OCTs and OCTNs in the GI tract. Tissue expression of OCT1 isoforms indicates that rat OCT1 (Grundemann et al., 1994, Zhang et al., 1997a) and OCT3 (Kekuda et al., 1998) mRNA are expressed in the small intestine at a relatively low and high level, respectively. With respect to the human variants, hOCT1 is expressed to a much lower extent in the human small intestine (Zhang et al., 1997b), while hOCT3 expression in the human small intestine has not been confirmed. Although rOCT2 is predominantly expressed in the rat kidney (Okuda et al., 1996), hOCT2 is also expressed in the human small intestine, as detected by RT-PCR (Gorboulev et al., 1997). A recent study using Oct1−/− mice further suggests the basolateral localization of mOCT1 in the mouse intestine (Fig. 7.2) (Jonker et al., 2001; Jonker and Schinkel, 2004). However, direct immunolocalization data are not currently available. The mRNA expression of the rOCTN1 (Wu et al., 2000a) and hOCTN2 (Tamai et al., 1998; Wu et al., 1998) were also detected in intestinal enterocytes, the latter one being very weak. To date, no further studies on the intestinal protein expression of OCTNs have been reported. 7.3.3.4 The Regulation of Organic Cation Transporters The expression and function of OCT family members have been suggested to be regulated via subtype, species, and tissue-specific parameters. The short-term regulation of basolateral and apical OCTs in different experimental systems is well documented (Ciarimboli and Schlatter, 2005). For rOCT1, hOCT1, hOCT2, and hOCT3, regulation has been associated with phosphorylation/dephosphorylation of the transporter, which can result in changes in the substrate affinity (Ciarimboli and Schlatter, 2005). For example, after stable transfection in HEK293 cells, rOCT1-mediated organic cation transport was stimulated by protein kinase C (PKC), PKA, and endogenous tyrosine kinase activation (Mehrens et al., 2000). Furthermore, it was determined that at least one of these phosphorylation sites is PKC dependent. PKC mediated phosphorylation in these sites leads to a conformational change at the substrate binding site, and thus results in alteration of the substrate transport including TEA and quinine (Mehrens et al., 2000). Therefore, the potential phosphorylation sites may play an important role in the regulation of transporter activity, especially the S286 residue, which is conserved in almost all OCTs (Ciarimboli and Schlatter, 2005). A gender dependent difference was observed when it was shown that renal expression of rOCT2 was significantly increased in male versus female rats (Urakami et al., 1999, 2000). Furthermore, these studies demonstrated that

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testosterone increased rOCT2, while estradiol resulted in a moderate decrease in the expression of rOCT2 (Urakami et al., 1999, 2000), suggesting the role of sex hormone regulation. In contrast, there was no gender difference observed in the renal expression of rOCT1 or rOCT3. In a separate ontogenic study, it was determined that rat renal OCT1, OCTN1, and OCTN2 mRNA levels increased gradually from infants to adults (Slitt et al., 2002). Since OCT1 and OCTN1/2 mediate the renal clearance of organic cations, substrates of these isoforms may be excreted more slowly in infants and children in contrast to adults (Slitt et al., 2002). Similarly, hOCTN1 mRNA was strongly expressed in the fetus when contrasted to the adult liver (Tamai et al., 1997), which indicates that the potential increased fetal hepatic toxicity. Clearly, the current data suggest that the genderand age-dependent effects on the expression of OCT isoforms need to be considered when evaluating the overall pharmacokinetics and potential toxicity of OCT substrates that have a narrow therapeutic index. Similar to the OATP/Oatp family, SNPs have been identified for several isoforms of human OCT and OCTN family (Koepsell and Endou, 2004). Some are associated with decreased transporter activity. In a population of 57 Caucasians, 25 SNPs within the hOCT1 gene were detected and further analyzed (Kerb et al., 2002). Eight SNPs resulted in single amino acid substitutions. Out of these, three SNPs (Arg61Cys, Cys88Arg, and Gly401Ser) affected in vitro organic cation transport. Uptake of MPP+ by Arg61Cys variant, the most frequent mutant (16%), was dramatically decreased (50%) compared to wild-type hOCT1. A follow-up study by Shu et al. (2003) screened 15 protein-altering variants of hOCT1 in Xenopus oocytes. It was demonstrated that predicted mutations occurring in the evolutionarily conserved regions of the gene had a more significant impact on the hOCT1 function. In a separate investigation on a heterogeneous collection of 247 patients, 28 hOCT2 variants were identified with eight (four occurring at a frequency ≥1% in ethnic populations) causing a nonsynonomous amino acid change (Leabman et al., 2002). Interestingly, the pharmacogenomic analysis of hOCT3 revealed no polymorphisms that resulted in an amino acid composition change (Lazar et al., 2003). The net impact of these OCT isoform variants in mediating the intestinal absorption of xenobiotics has yet to be determined. OCTs polymorphism may also contribute to the toxicity of drugs. Influence of OCT1 on a severe drug side effect, lactic acidosis, was observed in Oct1−/− mice treated with the biguanide analog, phenformin and metformin. Biguanides are substrates of OCTs, and can accumulate in the plasma due to reduced hepatic clearance caused by OCT transport (Wang et al., 2003; Jonker and Schinkel, 2004). Biguanides exhibit their antidiabetic effects partially through the inhibition of the mitochondrial respiration, leading to reduced glucose, which subsequently leads to an accumulation of plasma lactic acid (Owen et al., 2000; Wang et al., 2003). Deletion of the mOCT1 isoform was demonstrated to decrease the excretion of biguanides and promote inhibition of the mitochondrial respiration, i.e., lactic acidosis (Wang et al., 2003; Jonker and Schinkel, 2004). It is hypothesized that hOCT1 is largely responsible for the potentially fatal lactic acidosis, and may contribute to the removal of phenformin from the market (Wang et al.,

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2003; Jonker and Schinkel, 2004; Koepsell, 2004). Other OCT knockout studies are well reviewed by Koepsell (2004) and Jonker and Schinkel (2004). Defect mutations in hOCTN1 and hOCTN2 have also been demonstrated (Koepsell and Endou, 2004). Kawasaki et al. (2004) identified two SNPs in hOCTN1 of the Japanese population. One of them almost completely abrogated the TEA transport activity in stable transfected HEK293 cells. This result suggests that mutations in hOCTN1 might affect its physiological function and/or the pharmacological characteristics of its substrates. For example, decreased renal secretion of organic cations mediated by hOCTN1 might increase the nephrotoxic potential of relevant substrates. Mutations in hOCTN2 lead to a recessive hereditary disorder called “primary systemic carnitine deficiency (SCD)” (Nezu et al., 1999; Lahjouji et al., 2001). This potentially lethal disease is characterized by progressive infantile-onset cardiomyopathy, skeletal myopathy, hypoketotic hypoglycemic encephalopathy, and extremely low plasma and tissue carnitine concentrations (Tein et al., 1990; Stanley et al., 1991; Pons et al., 1997). Nonsense or missense mutations in hOCTN2 have been demonstrated to cause low or nonfunctional carnitine transporters. Therefore, defect of carnitine reabsorption in kidney and carnitine uptake in cardiac muscles and other organs leads to systemic carnitine depletion, which lead to the inhibition of β-oxidation of fatty acids (Tein, 2003). Patients with SCD are treated by oral administration of carnitine. Due to the potential drug–drug interaction, mediation with carnitine drugs interacting with hOCTN2 in patients with partial defects of hOCTN2 is suggested to be avoided or supplemented with higher dose of carnitine (Koepsell and Endou, 2004). Mutations in hOCTNs might increase susceptibility to Crohn’s disease. Peltekova et al. (2004) first identified that a missense mutation in hOCTN1 gene and a transversion in the promoter region of hOCTN2, which form haplotype related with an increase in the prevalence of Crohn’s disease. The resulting amino acid change in hOCTN1 reduced its affinity and capacity for transporting carnitine. It, however, enhanced the affinity and uptake of other xenobiotics (Peltekova et al., 2004). The hOCTN2 promoter mutation occurred in the heat shock transcription factor binding element region and altered transcription factor binding affinity (Peltekova et al., 2004). Therefore, these variants alter transcription and transporter functions of the OCTNs and interact with variants in another gene associated with Crohn’s disease, CARD15, to increase risk of Crohn’s disease. The increased risk observed in this patient population was later confirmed by Torok et al. (2005).

7.3.4

Nucleoside Transporters (CNT, SLC28A; ENT, SLC29A)

Nucleosides are the ribosylated precursors of purine and pyrimidine nucleotides, and in addition to their biological importance with respect to cellular energy and signal transduction in the form of their phosphorylated analogs (e.g., ATP and cAMP, respectively), their importance to cellular function and physiology is profound. For instance, adenosine alone has been shown to exhibit cardiac and vascular effects (Ely and Berne, 1992), act as a neuromodulator (Dunwiddie, 1985;

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Phillis and Wu, 1981), inhibit lipolysis in fat cells (Fain and Malbon, 1979), and act as an anti-inflammatory (Cronstein, 1994; Griffiths and Jarvis, 1996). In accordance with their overall physiological importance, the cellular transport of nucleosides is mediated by two distinct families of transporter proteins: the highaffinity, concentrative nucleoside transporters (CNT; SLC28) and the low affinity, equilibrative nucleoside transporters (ENT; SLC29). These transporter families are distinguished not only by their structural features, but also by their different transport mechanisms. In short, the SLC28 family is sodium dependent and works through an active symport mechanism, while the SLC29 family functions by a facilitated diffusion mechanism. On the cellular level, it is thought that ENTs and CNTs asymmetrically localize between the apical and basolateral membranes to mediate the vectorial transepithelial flux of nucleosides (Lai et al., 2002; Kong et al., 2004). However, given the broad and overlapping substrate specificities of each individual transporter family, coupled with their differential regulation, it has been surmised that these transporters may not only be key modulators of intracellular nucleoside availability, but may also fulfill various metabolic needs through selective and complementary activation (Pastor-Anglada et al., 2001). Regardless, given the widespread tissue localization, in concert with those items mentioned above, nucleoside transporters should be attractive pharmaceutical targets for therapeutic nucleoside analogs. 7.3.4.1 The Molecular and Structural Characteristics of Nucleoside Transporters CNT (SLC28A) The human SLC28 family consists of three subtypes of sodium-dependent, concentrative nucleoside transporters, hCNT1 (SLC28A1), hCNT2 (SLC28A2; also termed SPNT for sodium-dependent purine nucleoside transporter), and hCNT3 (SLC28A3), although five functionally distinct sodium dependent concentrative nucleoside activities have been reported, as N1 (system cif ), N2 (sytem cit), N3 (sytem cib), N4, and N5 (Griffiths and Jarvis, 1996). In summary, N1 transport has been shown to be selective for purine nucleosides, as well as the nucleobase, uridine; N2 transport is characterized by pyrimidine selectivity and adenosine; N3 transport exhibits broad substrate specificity, transporting both purine and pyrimidines alike; N4 transport is characterized by pyrimidine selectivity, guanosine and adenosine; and N5 is characterized by transport of formycin B and cladribine (Griffiths and Jarvis, 1996). The current topological model for the SLC28 family is based on rCNT1, which is hypothesized to be comprised of 13 putative TMD, although a 15 transmembrane domain model cannot be ruled out (Hamilton et al., 2001). SLC28A1 is chromosomally localized to 15q25–26 and encodes a protein, CNT1, of approximately 649 amino acids in length that exhibits approximately 83% identity with its rat homologue (Ritzel et al., 1997). SLC28A2 is chromosomally localized to 15q13–14, and encodes a protein of approximately 658 amino acids with a molecular weight of approximately 72 kDa and exhibits several putative protein kinase C phosphorylation sites (Wang et al., 1997). hCNT3 is approximately 48 and 47%

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identical to hCNT1 and hCNT2, respectively (Ritzel et al., 2001). The SLC28A3 gene is localized to 9q22.2 and encodes a protein that is approximately 691 amino acids (Ritzel et al., 2001). ENT (SLC29A) The human SLC29 transporter family contains four members, hENT1 (SLC29A1), hENT2 (SLC29A2), hENT3 (SLC29A3), and hENT4 (SLC29A4) that are primarily distinguished from the concentrative nucleoside transporters by their facilitated diffusion transport mechanism. Early research distinguished SLC29 family members by their sensitivity to inhibition by nitrobenzylthioinosine (NBMPR), as either es, equilibrative sensitive, or ei, equilibrative insensitive; however, the usefulness of this terminology for identification has been superceded by the cloning and subsequent characterization of both hENT1 and hENT2, which are responsible for these transport mechanisms (Griffiths and Jarvis, 1996; Kong et al., 2004). Structurally, SLC29 family members are characterized by 11 putative TMD, with an intracellular amine terminus and extracellular carboxy terminus. The gene encoding hENT1 is localized to chromosome 6p21.1–2 (Coe et al., 1997), and encodes a protein that is 456 amino acids long (Griffiths et al., 1997a). Both the rat and mouse homologues have also been cloned and exhibit approximately 78% identity with hENT1 (Yao et al., 1997; Kiss et al., 2000). Similarly, the genes encoding hENT2 and hENT3 have been identified and are chromosomally localized to 11q13 and 10q22.1, respectively (Griffiths et al., 1997b; Williams et al., 1997; Hyde et al., 2001; Clark et al., 2003). Additionally, hENT2 has been shown to be 456 amino acids long, sharing approximately 46% identity with hENT1 (Griffiths et al., 1997b), while hENT3 is 475 amino acids long and exhibits approximately 30–33% identity with mouse, rat, and human ENT1 and ENT2 isoforms (Hyde et al., 2001). Although it has been confirmed as a nucleoside transporter capable of low affinity adenosine transport, hENT4 is a 530 amino acid protein that shows only 18% identity to hENT1 (Acimovic and Coe, 2002; Baldwin et al., 2004). The gene encoding hENT4 is localized to chromosome 7p22.1 (Acimovic and Coe, 2002; Strausberg et al., 2002; Baldwin et al., 2004). Interestingly, hENT4 has recently been shown to be a low affinity monoamine transporter and renamed plasma membrane monoamine transporter, or PMAT (Engel et al., 2004). As such, ENT4 will not be discussed further. Both hENT1 and hENT2 exhibit glycosylation sites in the extracellular loops between TMD one and two, on Asn residue 48 (Yao et al., 1997; Crawford et al., 1998; Ward et al., 2003). In the case of hENT1, glycosylation is not required for transport activity, but may affect the binding affinity to transport inhibitors, such as NBMPR (Vickers et al., 1999; Ward et al., 2003). HENT2 also contains an additional glycosylation site on Asn57, which most likely functions to target the protein to the plasma membrane (Ward et al., 2003). Such structural assessments have not yet been conducted for hENT3, although it does differ from both hENT1 and hENT2 in possessing a long, hydrophilic amine terminus region preceding transmembrane domain one that contains dileucine motifs, which are responsible for its intracellular localization (Baldwin et al., 2005).

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7.3.4.2 The Substrate Specificities of Nucleoside Transporters CNT (SLC28A) Vijayalakshmi and Belt (1988) first showed the differing substrate specificities of CNT1 and CNT2 by their observations in mouse intestinal epithelium of two classes of sodium dependent concentrative nucleoside transport, which was dependent on purine/pyrimidine species. It is now known that CNT1 transports primarily pyrimidine nucleosides and the purine adenosine (N2 transport), while CNT2 transports primarily purine nucleosides and uridine (N1 transport) (Huang et al., 1994; Ritzel et al., 1997; Wang et al., 1997). In contrast, CNT3 has been shown to be more broadly selective, transporting both purine and pyrimidine nucleosides (N3 transport) (Wu et al., 1992). Interestingly, while CNT1 is specific for pyrimidines, it does exhibit specificity for adenosine in a high-affinity, low-capacity manner (Ritzel et al., 2001). Furthermore, while both CNT1 and CNT2 employ a 1:1 Na+ :nucleoside coupling ratio, CNT3 requires a 2:1 ratio (Plagemann and Aran, 1990; Ritzel et al., 2001). Given their respective substrate specificities, SLC28 family members exhibit transport activity for a wide range of pharmaceutically relevant compounds. CNT1 has been shown to exhibit high transport affinity for the antiviral nucleoside analogs zidovudine, lamivudine, and zalcitabine, and the cytotoxic cytidine analogs cytarabine and gemcitabine used for treatment of a wide spectrum of tumors, while CNT2 has been shown to transport the antiviral compounds didanosine (ddI) and ribavirin (Gray et al., 2004). With its broader substrate specificity, CNT3 transports a number of anticancer nucleoside analogs including cladrabine, gemcitabine, 5-fluorouridine, fludarabine, and zebularine (Ritzel et al., 2001). Substrate and cation recognition sites for CNT transporters are both located extracellularly on the carboxy half of the proteins, on TMD 7, 8, and 9 (Wang and Giacomini, 1999; Loewen et al., 1999). Changing serine 319 of CNT1 to glycine has been shown to alter the substrate selectivity to include purines, while the adjacent glutamine residue was shown to be important in modulating the apparent affinity for nucleosides (Wang and Giacomini, 1999). Moreover, changing serine 353 to threonine changed CNT1 into a transporter that was highly selective for uridine (Loewen et al., 1999). Recently, Lai et al. (2005) have demonstrated that G476 is important for correct membrane targeting, folding, and/or intracellular processing of hCNT1 and that F316H mutation confers guanine sensitivity. These researchers speculated that the naturally occurring F316H mutation in hCNT1 is responsible for one of the two CNT activities for which a transporter has not been identified (N4) (Griffiths and Jarvis, 1996; Lai et al., 2005). Although such selectivity studies have not been performed for CNT2 per se, Chang et al. (2004) did explore the structural requirements necessary for purine and pyrimidine transport by hCNT1, hCNT2, and hENT1. Their computer modeling studies, which explore the relationships between hydroxylation position, substrate selectivity, and transporter inhibition, could prove useful for rational drug design of future nucleoside analogs for both cancer and antiviral treatment.

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ENT (SLC29A) Kinetic studies assessing the transport affinities of both hENT1 and hENT2, the first SLC29 family members identified, have been quite extensive. Earlier research identified hENT1 as the es transport system, while hENT2 was identified as the ei system, where the es system bound NMBPR with high affinity (K d = 1–10 nM) and the ei system was not affected at nanomolar concentrations, but was affected at higher concentrations (>10 µM) (Kong et al., 2004). In fact, since ENT transport is mediated via a bidirectional facilitated diffusion mechanism, which is dependant upon substrate concentration gradient, the difference in binding NMBPR was used quite successfully for the early biochemical characterization of these two transporters. The substrate affinities for endogenous nucleosides and some therapeutic nucleoside analogs have been described for hENT1 and hENT2 and recently for hENT3. Both hENT1 and hENT2 have been shown to be broadly selective for both purine and pyrimidine nucleosides; however, hENT2 exhibits 7.7- and 19.3-fold lower affinities for guanosine and cytidine, respectively, but a fourfold higher affinity for inosine (Kong et al., 2004). Further, hENT2 transports nucleobases, whereas hENT1 does not (Yao et al., 2002). In terms of therapeutic nucleoside analogs, hENT1 has been shown to interact with a number of compounds widely used in the treatment of cancer, such as cladribine, gemcitabine, fludarabine, cytarabine, tiazofurin, and benzamide riboside (Griffiths et al., 1997a; Mackey et al., 1998; 1999; Vickers et al., 2002; Damaraju et al., 2005). However, hENT1 only poorly transports the antivirals 2 , 3 -dideoxycytidine (ddC) and 2 , 3 -dideoxyinosine (ddI) and does not transport 3 -azido-3-deoxythymidine (AZT), suggesting an important role of the 3 -hydroxyl group on the ribose moiety for substrate recognition (Yao et al., 2001a). Moreover, Vickers et al. (2002) recently demonstrated the importance of the 3 -hydroxyl group with respect to uridine analogs for both hENT1 and hENT2. Nonetheless, hENT2 demonstrated a much broader substrate selectivity, transporting nucleobases (which lack the ribose moiety), a number of the above compounds, as well as ddC, ddI, and AZT (Yao et al., 2001a; Vickers et al., 2002). However the selectivity of hENT2 for cytidine and its analogs is comparatively lower than that of hENT1, suggesting a lack of tolerance for a 4 -amino moiety on the base (Vickers et al., 2002). Both transporters tolerated halogen substitution at the 5 position on the base, as well as the 2 and 5 positions on the ribose moiety of uridine, suggesting these positions are not essential for uridine-like substrate recognition (Vickers et al., 2002). Interestingly, recent evidence indicates the antidiabetic compound troglitazone, but not the related thiazolidinediones pioglitazone and ciglitazone, inhibits hENT1 transport of adenosine and uridine via a competitive inhibition mechanism (Leung et al., 2005b). Troglitazone had minimal inhibitory effect on hENT2 in this study (Leung et al., 2005b). Interestingly, inhibition of ENT1 in vascular smooth muscle is suggested to increase extracellular adenosine, which causes vasodilation and inhibits vascular smooth muscle cell proliferation (Rubin et al., 2000; Kim et al., 2004; Masaki et al., 2004). In concert, these data suggest a novel therapeutic

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approach to modulate extracellular adenosine concentrations. To our knowledge, the transport characteristics of other diabetic drugs via nucleoside transporters have not been studied. Similar to both hENT1 and hENT2, hENT3 has demonstrated broad selectivity for nucleosides (Baldwin et al., 2005). Additionally, hENT3 has also been shown to transport the nucleobase adenine; however, in contrast to hENT2 it does not transport hypoxanthine (Baldwin et al., 2005). Interestingly, the transport activity of hENT3 was relatively insensitive to many of the classical ENT inhibitors, such as NBMPR, dipyridamole, and dilazep although transport was found to be highly pH dependent, indicative of its intracellular localization (Baldwin et al., 2005). HENT3 has also demonstrated good transport efficiency for a wide range of pharmaceutical purine and pyrimidine analogs, including cladribine, cordycepin, tubercidin, zebularine, 5-fluoro-2-deoxyuridine, ddC, ddI, and AZT (Baldwin et al., 2005). Other compounds, such as gemcitabine and gancyclovir were also substrates for hENT3, although with a much lower transport efficiency (Baldwin et al., 2005). Especially intriguing is the high transport efficiency of AZT, especially compared with hENT1, which does not transport AZT, and hENT2, which has been shown to transport AZT, but at one third its capacity of uridine (Yao et al., 2001a). Comparatively, hENT3 exhibits AZT selectivity comparable to adenosine and inosine, which are higher than that for thymidine and uridine, indicating that the 3 -hydroxy group is not important for substrate recognition (Baldwin et al., 2005). Given the high prevalence of 3 -hydroxyl moieties on many antiviral compounds, it is unfortunate that studies exploring structure–function relationships for hENT3 have not been performed to date. Such studies comparative to either hENT1, or especially hENT2 could prove critical to elucidate the mechanism by which these compounds enter the cell to elicit their cytotoxic effects. Although limited, some literature does explore the structure–function relationships of both hENT1 and hENT2. Using chimeric constructs, researchers have shown the primary sites of substrate binding for hENT1 reside in TMD three through six, while TMD one to two confer a secondary contribution (Sundaram et al., 2001). Molecularly, studies have shown that a single substitution of M33I was sufficient to alter sensitivity to a number of substrates (Visser et al., 2002), while Gly179 is important to NBMPR binding (SenGupta et al., 2002). With respect to ENT2, chimeric studies using the rat homologues of ENT1 and ENT2 have shown that TMD five and six form at least part of the translocation pathway (Yao et al., 2002). Modification of the corresponding Cys140 (transmembrane domain four) inhibits transport, suggesting that this residue lies within, or is adjacent to the substrate translocation pathway (Yao et al., 2001b). However, additional studies are required to fully characterize and elucidate the structure– function relationships of each of these transporters. 7.3.4.3 The Expression of Nucleoside Transporters Tissue expression of CNT1 has been shown in epithelial tissues including small intestine, kidney, and liver. In contrast, CNT2 has been shown to be widely

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distributed in kidney, liver, heart, brain, placenta, pancreas, skeletal muscle, colon, rectum, and throughout the small intestine, while CNT3 has been shown in pancreas, trachea, bone marrow, and mammary gland, with lower levels in intestine, lung, placenta, prostrate, testis, and liver (Gray et al., 2004). Interestingly, only CNT1 and CNT2 were widely expressed on the brush-border membranes of enterocytes along the length of both the fetal and adult small intestines, as determined via nucleoside uptake by intestinal brush-border membrane vesicles (Ngo et al., 2001). Using double transfected MDCK cells, which simultaneously expressed YFP-tagged CNT1 and ENT1, Lai et al. (2002) confirmed the apical cellular localization of CNT1. While the rat homologue of CNT2 has been shown to be apically localized in polarized MDCK and LLC-PK cells, in liver parenchymal cells it is highly expressed in the basolateral (sinusoidal) membrane, suggesting tissue-specific sorting (Mangravite et al., 2001; Duflot et al., 2002). Duflot et al. (2002) also demonstrated the apical localization of CNT1 in liver parenchymal cells, suggesting transcytotic membrane insertion. The subcellular localization of CNT3 has not been reported as of preparation of this chapter. Tissue distribution studies of hENT1, hENT2, and hENT3 suggest these transporters are ubiquitously expressed, at least on the mRNA level, although there does exist appreciable intertissue and interindividual expression differences (Griffiths and Jarvis, 1996; Crawford et al., 1998; Pennycooke et al., 2001; Jennings et al., 2001; Baldwin et al., 2005). On the cellular level, it is thought that ENTs and CNTs asymmetrically distribute between the apical and basolateral membranes to mediate the vectorial transepithelial flux of nucleosides (Lai et al., 2002; Kong et al., 2004). However, no studies directly examine ENT1, or ENT2 protein localization in intestinal epithelial tissues. Nevertheless, hENT1 has been shown to be predominantly basolaterally localized in YFP-tagged ENT1 transfected MDCK cells, with some apical localization (Lai et al., 2002; Mangravite et al., 2003), whereas hENT2 exhibits only basolateral localization (Mangravite et al., 2003). Interestingly, some functional data indicate that ENT1 may also exhibit some intracellular localization (Pisoni and Thoene, 1989; Mani et al., 1998; Jimenez et al., 2000), although further studies are required to determine the functional relevance of intracellular nucleoside transport due to ENT1 (Kong et al., 2004). HENT3 is now known to be an intracellular transporter that colocalized with lysosomal markers, but not with golgi, early endosomes, or endoplasmic reticulum (Baldwin et al., 2005). As expected, truncation of the N-terminus or mutation of its dileucine motif caused relocation of the transporter to the cell surface (Baldwin et al., 2005). Given its lysosomal localization, it should be of no surprise that its transport kinetics were optimum at pH 5.5 (Baldwin et al., 2005). 7.3.4.4 The Regulation of Nucleoside Transporters Due to the inherent importance of nucleosides to overall cellular and tissue function, it is of no surprise that the regulation mechanisms of both CNTs and ENTs are quite complex and difficult to effectively study. In short, expression of both CNT1 and CNT2 has been shown to be tissue specific, dependent on cell cycle, certain

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hormones and cytokines, as well as the presence of substrate (Gomez-Angelats et al., 1996; Del Santo et al., 1998; Soler et al., 1998, 2001; Valdes et al., 2000, 2002). However, with the exception of nutritional regulation studies (Valdes et al., 2000), virtually all of the regulatory studies have been conducted in hepatocytes, or immune cells. Given tissue-specific regulation dependence, one should question the applicability of these data to the intestinal model. In short, not much is known concerning either the transcriptional, or translational regulation of either the SLC28, or SLC29 families. Regulation of hENT1 has been shown to be dependent on deoxynucleotide levels in cultured cancer cells (Pressacco et al., 1995). Moreover, hENT1 expression has also been demonstrated to be a function of cell cycle, with expression doubling between G1 and G2 -M phases (Pressacco et al., 1995). Cultured cells also exhibited an upregulation of hENT1 due to phorbol ester treatment, which appears to be due to either PKC δ, or PKC ε; however, this remains to be elucidated (Coe et al., 2002). Interestingly, glucose treatment has been shown to upregulate ENT1 protein activity, as well as its protein and mRNA expression in human aortic smooth muscle cells (Leung et al., 2005a). The functional significance of this finding is still uknown; however, it has been shown that ENT1 expression is modulated due to diabetes (Pawelczyk et al., 2003), and that it is not transcriptionally controlled via peroxisome proliferator-activated receptor gamma (PPARγ) (Leung et al., 2005b). Still less is known concerning the mechanisms regulating the expression of hENT2 and hENT3.

7.3.5

Monocarboxylate Transporters (MCT; SLC16A)

Many endogenous and exogenous short chain anionic compounds, such as lactic acid, pyruvate, acetoacetate, β-hydroxybutyrate, acetate, propionate, and butyrate, are all good substrates for a family of proton-coupled transporter proteins, termed the monocarboxylate transporter family (MCT; SLC16) (Price et al., 1998). Although investigation of MCT activity was studied biochemically in earlier literature, it was not until the first identified member of the MCT family, MCT1, was cloned in CHO cells (Kim et al., 1992), and later functionally expressed in a breast tumor cell line (Garcia et al., 1994), that the molecular characterization of this transporter family began (Enerson and Drewes, 2003). To date 14 members of the MCT family have been identified, each having unique tissue distribution (Halestrap and Price, 1999; Halestrap and Meredith, 2004). Given the relative size of this transporter family, this assessment will comprise only those MCT members relevant to the intestinal transport of pharmaceutical or therapeutic compounds. Additionally, given that the molecular and functional characterizations of members in this transporter family are still in their infancy, one is expected to consult the literature for a more comprehensive review (Price et al., 1998; Enerson and Drewes, 2003; Halestrap and Meredith, 2004). Finally, given the convoluted nomenclature for this transporter family, representative transporters will be identified by their MCT name, as opposed to SLC or another designation.

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7.3.5.1 Molecular and Structural Characteristics of Monocarboxylate Transporters Kyte–Doolittle hydropathy plots indicate that MCT isoforms exhibit 12 putative TMD, with both the amine and carboxy termini located intracellularly (Price et al., 1998; Halestrap and Price, 1999). This has been confirmed experimentally for MCT1 in rat erythrocytes (Poole et al., 1996). Transporter topology varies with respect to the length of the carboxy-terminus after transmembrane domain 12 and a large intracellular loop located between TMD six and seven (Price et al., 1998); however, these structures are consistent among the MCT isoforms. MCT family members also exhibit two highly conserved motifs: [D/E]G[G/S][W/F][G/A]W which traverses into transmembrane domain one and YfXK[R/K][R/L]XLAX[G/A]XAXAG leading into transmembrane domain five, where residues shown in bold are conserved in all of the sequences, residues in square brackets indicate alternative amino acids, residues that are in normal type are the consensus amino acid at that position, and “X” represents any amino acid (Halestrap and Price, 1999). Site directed mutagenesis studies have been conducted for a number of residues, which have resulted in various changes in substrate specificities; however, most interesting is that mutagenesis of the highly conserved Arg313 in MCT1 results in reduction in affinity for lactate (Rahman et al., 1999). It has been surmised that this positively charged Arg binds the carboxy anion of monocarboxylates, much like lactate dehydrogenase, allowing for transport (Poole and Halestrap, 1993; Carpenter and Halestrap, 1994; Halestrap and Price, 1999). Seemingly another defining factor of MCT family members is the presence of ancillary proteins that are required for proper intracellular trafficking, cell surface expression, and/or function. Studies indicate MCT1, MCT3, and MCT4 require the presence of CD147 [also known as OX-47, extracellular matrix metalloproteinase inducer (EMM-PRIN), HT7 or basigin], or the related protein GP70 (Embigen), both widely distributed cell surface glycoproteins, for proper cell surface expression and function (Philp et al., 2003). These glycoproteins exhibit a single transmembrane domain, two immunoglobulin-like domains in the extracellular region, and a short carboxy terminus cytoplasmic tail (Schuster et al., 1996). In contrast, MCT2 does not interact with CD147 specifically; however, it does appear to require an additional protein for proper expression at the cell surface (Kirk et al., 2000). The associations of MCT1 and MCT4 with CD147 have been confirmed by carboxy terminus tagging with fluorescent proteins (Kirk et al., 2000; Zhao et al., 2001). Lack of CD147 coexpression with MCT1 and MCT4 results in accumulation in the endoplasmic reticulum, or golgi apparatus (Kirk et al., 2000). Studies exploring the coexpression of CD147 or GP70 with other MCT isoforms have not been conducted. 7.3.5.2 The Substrate Specificity of Monocarboxylate Transporters Functional evidence for the proton-dependent symport of monocarboxylates via various MCT isoforms is well established (Enerson and Drewes, 2003).

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Comparatively, MCT1, MCT2, and MCT4 exhibit higher affinities for pyruvate than lactate, although MCT4 exhibits low affinity for both substrates (Enerson and Drewes, 2003). The difference in affinities from pyruvate to lactate indicates a preference for 2-oxoacids over 2-hydroxy acids (Enerson and Drewes, 2003). Interestingly, the affinity for MCT2 for pyruvate is extremely high compared to MCT1 (Lin et al., 1998). Other monocarboxylates known to be substrates for MCTs include butyrate, acetate, propionate, etc. With respect to therapeutic compounds, it has been surmised that MCT isoforms may play a role in the intestinal absorption of some β-lactam antibiotics (Li et al., 1999), penicillins (Itoh et al., 1998), nonsteroidal anti-inflammatory drugs (Emoto et al., 2002), valproic acid (Utoguchi and Audus, 2000; Hosoya et al., 2001), atorvastatin (Wu et al., 2000c), and nateglinide (Okamura et al., 2002). One isoform in the MCT family, MCT10 (TAT1; SLC16A10), is an aromatic amino acid transporter that exhibits proton independent transport of substrates, although it does exhibit 30% identity to other MCTs (Kim et al., 2001a). The high sequence conservation (49%) between MCT10 and MCT8 suggests MCT8 may not function as a monocarboxylate transporter either (Friesema et al., 2005). Indeed, when expressed in Xenopus oocytes, MCT8 transports thyroid hormones T3 and T4 in a sodium and proton independent manner (Enerson and Drewes, 2003). Neither lactate nor aromatic amino acids appear to be substrates for MCT8 (Enerson and Drewes, 2003). Although the substrate affinities of MCT5–8 have not yet been identified, their similarity to MCT10 may also suggest an alternate substrate affinity, differing from MCT1–4 (Enerson and Drewes, 2003). 7.3.5.3 The Expression of Monocarboxylate Transporters Although each MCT isoform exhibits its own unique tissue distribution, of the 14 different MCTs currently identified, MCTs 1, 3–7 were shown to be expressed in the GI tract by Northern blotting (Price et al., 1998). However, at the time of that publication only seven MCT isoforms had been established and the intestinal expression of the remaining MCT isoforms has not yet been investigated. These northern blotting experiments also indicate that MCT2 is not intestinally localized, which has been supported by a recent publication exploring the intestinal distribution of a number of MCT isoforms (Gill et al., 2005). This literature suggests that MCT1, 4, and 5 are the predominant intestinally expressed isoforms, although MCT3 does exhibit some minimal expression (Gill et al., 2005). This study further suggests that MCT1 expression increases along the length of the human intestine, with a predominant expression in the distal colon, followed by proximal colon, ileum, and jejunum (Gill et al., 2005). Results were similar for MCT4 and MCT5, where expression increased moving down the GI tract; however, neither MCT4 nor MCT5 exhibited expression in human ileum (Gill et al., 2005). In contrast to previous data suggesting MCT3 is only expressed in the retinal epithelium (Yoon et al., 1997; Philp et al., 1998) when antibody specific to MCT3 was used at a very low dilution of 1:50, expression was observed in human GI tract. However, in contrast to MCT1, MCT4, and MCT5, expression was higher in ileum as compared to colonic regions (Gill et al., 2005). Expression

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of MCT6 was not observed in the human GI tract (Gill et al., 2005). Gill et al. (2005) also showed differences in the preferential membrane localization of these transporters, suggesting basolateral localization of MCT3, MCT4, and MCT5, while MCT1 is suggested as being apically localized (Gill et al., 2005). The basolateral expression of MCT10 has also been shown in human intestinal epithelial cells (Kim et al., 2001a,b). 7.3.5.4 The Regulation of Monocarboxylate Transporters With the exception of some limited studies conducted using MCT1, regulation mechanisms of the various intestinally expressed MCT isoforms has not been reported. Previous studies have shown that MCT1 is upregulated in response to excess butyrate substrate, which is reflected functionally as an increase in butyrate transport in cultured colonic epithelial cells (AA/C1) (Cuff et al., 2002). Moreover, it has been reported that MCT1 expression is decreased markedly during colon carcinogenesis, indicating a role of MCT1 in cancer prevention via active butyrate influx (Lambert et al., 2002). It has been proposed that the decrease in MCT1 expression during colon carcinogenesis may decrease the intracellular availability of butyrate required to regulate expression of genes associated with the processes maintaining tissue homeostasis within the colonic mucosa (Cuff et al., 2005). In fact, studies suggest that the ability of butyrate to induce cellcycle arrest and differentiation is dependent on the abundance and functionality of MCT1 (Cuff et al., 2005). Furthermore, downregulation of MCT1 using RNAi in various colonic cell lines resulted in consistent inhibition of butyrate influx thus inhibiting its ability to modulate various indicators of carcinogenesis, namely IAP, a marker of differentiation, p21, a cell cycle inhibitor, and CD1, a positive regulator of cell cycle progression (Cuff et al., 2005). This study also concluded that MCT1 inhibition did not affect those genes associated with apoptosis, bcl-xL and bak (Cuff et al., 2005). Whereas it is known that alteration in physiological state changes MCT expression, the underlying cellular and molecular mechanisms are poorly understood (Enerson and Drewes, 2003). However, studies of MCT regulation in other tissue do suggest an upregulation of MCT expression under hypoxia, as well as in the presence of exogenous vascular endothelial growth factor, which is known to be mediated by the hypoxia inducible factor HIF-1 (Enerson and Drewes, 2003). Moreover, mRNA expression of MCT1 in cultured macrophages is upregulated by exposure to lipopolysaccharides, tumor necrosis factor-α, and nitric oxide; however, only lipopolysaccharide and tumor necrosis factor-α treatment related to an increase in MCT1 protein (Hahn et al., 2000). These results suggest the presence of multiple signaling pathways that may converge to regulate MCT1 expression (Enerson and Drewes, 2003). Additionally, Leino et al. (2001) recently demonstrated upregulation of MCT1 and GLUT1 in rat brain after diet-induced ketosis. Results indicated an eightfold increase in MCT1 expression in brain endothelial cells after 4 weeks under ketonemic conditions (Leino et al., 2001). Moreover, an increase in MCT1 levels were shown throughout the rat brain, especially in

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the cerebellum, indicating protein upregulation in response to dietary and possibly pathological stresses (Leino et al., 2001). Although the transport of monocarboxylates by MCTs is important with respect to several disease pathologies, such as cancer and ischemic stroke, given their widespread tissue expression (heart, brain, intestine, liver, etc.) and their substrate’s impact on cellular energy and overall tissue and organism function, it is not expected that pharmaceutical intervention via MCTs is likely in the near future. This is not particularly surprising given the incomplete functional characterization and even identification of these transporters. However, the relative importance of MCTs to proper physiological function necessitates further research into the pathophysiology associated with this transporter family, especially considering the recent finding of the role of MCT8 dysfunction in causing severe X-linked psychomotor retardation, termed Allan–Herndon–Dudley syndrome (Dumitrescu et al., 2004; Friesema et al., 2004).

7.4

Impact of Intestinal Transporters on Bioavailability

The importance of membrane transporter proteins has been well established with respect to the bioavailability of many therapeutic compounds. Notwithstanding their physiologic importance (e.g., fulfillment of nutrient requirements and waste trafficking), membrane transporter proteins can be particularly important due to their substrate specificity and kinetics and provide attractive therapeutic targets for pharmaceutical intervention. However, tissue and temporal expression patterns, as well as cellular localization, are all extremely important in determining the relevance of a particular transporter with respect to the contribution of transporter activity on the bioavailability of exogenous substrates. The cellular permeation of a compound is a function of a multitude of parameters and virtually unpredictable without adequate study. Moreover, given the obvious physiological relevance of many of the endogenous transporter protein substrates, it is also essential to explore the subtle interplay between different transporter proteins to elucidate the underlying mechanisms of transitional trafficking veiled by the net transcellular flux. In short, the interplay of various transporter protein mechanisms along with transport by parallel pathways can significantly impact on the overall bioavailability of a compound. P-gp, possibly the most studied transporter protein, is expressed on the apical surface of intestinal epithelial mucosa where its primary function is to promote the removal of toxic xenobiotics, a side-effect of which is the active efflux of various pharmaceutical compounds. The role of P-gp in restricting intestinal absorption was clearly determined by observing two- to fivefold greater plasma concentrations of the HIV-1 protease inhibitors indinavir, nelfinavir, and saquinavir in mdr1a(−/−) mice, as compared to wild-type mice (Kim et al., 1998). Additionally, Chiou et al. (2000) have also reported the absolute bioavailability of tacrolimus increased from 22% in normal mice to 72% in P-gp knockout mice.

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In lieu of functional knockout studies, a number of SNP have also been identified in the human MDR1 gene. A number of these SNPs have been associated with changes in P-gp expression, thereby affecting the pharmacokinetic profiles of a number of substrates (Hoffmeyer et al., 2000; Kim et al., 2001; Kurata et al., 2002). For example, Kurata et al. (2002) demonstrated the role of human MDR1 gene polymorphism on the bioavailability of digoxin, a known P-gp substrate. Previously, Hoffmeyer et al. (2000) showed that homozygosity for a polymorphism in exon 26 (C3435T) of the MDR1 gene was observed in 24% of a study sample population (n = 188) with these patients exhibiting significantly lower duodenal MDR1 expression as well as significantly increased plasma levels of digoxin. Furthermore, differences in the expression of P-gp, sP-gp, MDR3, MRP’s (1–5), and lung resistance-associated protein (LRP) in the Caucasian and Chinese intestines have also been reported, suggesting variations in drug bioavailability due to ethnicity (Wang et al., 2004). Genetic polymorphism is not limited to the MDR1 gene, but has also been shown in both MRP1 and MRP2 in a Japanese population (Moriya et al., 2002). Clearly, there are multiple factors that influence inter-individual variability in drug absorption and disposition, and determining the precise contribution of genetic polymorphism to this variability in oral absorption will continue to be an interesting and challenging area in the future. While genetic variations and/or environmental factors can obviously affect bioavailability, it is the interplay of various transporter proteins and other metabolic (e.g., cytochrome P-450s) and nonmetabolic (e.g., transduction factors) proteins that are more likely to influence individual therapy. One such example is the underlying interplay between P-gp and CYP3A4, a major drugmetabolizing enzyme (Watkins, 1997). As mentioned above, P-gp is expressed on the apical surface of intestinal epithelial mucosa where its primary function is to promote the removal of toxic xenobiotics, via active efflux into the intestinal lumen. CYP3A4, in contrast, is intracellularly localized and is a broad affinity, metabolizing enzyme. The coexpression of these two proteins results in an increase in drug metabolism through an increase in exposure to not only CYP3A4, but also any proteases, drug metabolizing enzymes, and the natural sink condition of the intestines. Therefore, the repeated exposure to both the harsh intestinal conditions, as well as the drug metabolizing enzyme, results in a net decrease in plasma concentrations, thus limiting overall bioavailability (Fig. 7.3) (Watkins, 1997; Benet et al., 2001). In contrast to the efflux transporter proteins where substrate activity is a negative attribute of potential drug candidates, influx transporters (i.e., peptide transporters) provide a unique opportunity to specifically design and develop therapeutic agents with significantly higher molecular recognition for potentially increasing intestinal drug absorption and bioavailability. This strategy has been utilized in the development of prodrugs exhibiting high affinity for PepT1, as observed in the synthesis of L-Val-acyclovir (valacyclovir) prodrug of acyclovir (Friedrichsen et al., 2002; Bhardwaj et al., 2005b), although affinity for other transporters in addition to PepT1 cannot be ruled out (Phan et al., 2003; Landowski et al., 2003). Acyclovir has an oral bioavailability between 15 and 30% (Fletcher and Bean, 1985), while

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S + Met

CYP3A4 P-gp S Small Intestine

S + Met Intestinal Elimination

F IGURE 7.3. Interplay of P-gp and CYP3A4 affecting the absorption of substrates (S) and potential metabolites (Met) from a representative intestinal epithelium cell. Substrate enters the enterocyte through any number of mechanisms and can either enter the circulation, be metabolized by CYP3A4, or effluxed into the intestinal lumen via P-gp. The effluxed substrate may then be either reabsorbed into the enterocyte where the process may then be repeated, or excreted through intestinal elimination. The reabsorbed substrate may then undergo additional metabolism, be absorbed into the circulation, or be effluxed again. (Modified from Benet and Cummins (2001))

Soul-Lawton et al. (1995) estimated a 54.2% mean bioavailability of acyclovir in healthy volunteers after a single dose of valacyclovir. Other studies have demonstrated three to fivefold increases in bioavailability for valacyclovir as compared to acyclovir (Weller et al. 1993; Steingrimsdottir et al., 2000). A more detailed review of the clinical advantages and pharmacokinetics of valacyclovir were elaborated by MacDougall and Guglielmo (2004). It is also important to mention one study by Kimberlin et al. (1998) where preliminary pharmacokinetic data for acyclovir were contrasted with valacyclovir therapy in pregnant women receiving herpes simplex virus suppressive therapy. Valacyclovir therapy resulted in an increased acyclovir bioavailability and increased acyclovir concentrations in the amniotic fluid with no evidence of a preferential accumulation in the fetus (1.7 mean maternal/umbilical vein plasma ratios for valacyclovir to 1.3 for acyclovir therapy). Other prodrugs, particularly angiotensin-converting enzyme inhibitors (e.g., captopril), have also shown to be PepT substrates, with important consequences in their pharmacokinetics (Sugawara et al., 2000; Shu et al., 2001). Another strategy to increase drug bioavailability via peptide transporters is by stimulating PepT-like transport by increasing the proton driving force. Nozawa et al. (2003) used a proton-releasing polymer, Eudragit L100–55, to increase the luminal/intracellular proton gradient to stimulate PepT1 transport. Incremental increases in intestinal transport activity were seen. Nielsen et al. (2002), Kunta and Sinko (2004), and Steffansen et al. (2004, 2005) have reviewed additional examples of therapeutic applications of the PepT1 transport pathway. Organic anion transporter proteins have also been shown to influence drug absorption of therapeutic compounds. For instance, the intestinal absorption of the

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H1 -histamine receptor antagonist fexofenadine is primarily mediated by OATP-B not P-gp (Dresser et al., 2002). Inhibition of OATP-B, by ingestion of grape fruit juice, decreased the fexofenadine area under the plasma concentration–time curve (AUC), the peak plasma drug concentration (Cmax ), and the urinary excretion values to 30–40% of those compared with the compound ingested with water in human. Since grape fruit juice is a more potent inhibitor of OATPs compared to P-gp, the reduced oral bioavailability of fexofenadine is suggested to result from limited influx process via OATP-B (Dresser et al., 2002). Furthermore, the drug may be cycled between the enterocyte and the intestinal lumen, which may also result in increased metabolism and/or hydrolysis, as mentioned above. However, this model has not been studied specifically for this compound. Further evidence suggests that OATP-B may be involved in the intestinal uptake of pravastatin, as mentioned in Sect. 7.3.2. These data suggest OATP-B not only mediates absorption of anionic compounds, but that its activity may be optimum at the acidic surface microclimate of the small intestine. In addition, infusion of pravastatin directly into the stomach resulted in maximal absorption in the duodenum and enhanced the bioavailability of pravastatin (Triscari et al., 1995). This observation correlates well with the expression patterns of OATP-B in human enterocytes, where OATP-B was immunohistochemically localized at the apical membrane of intestinal epithelial cells (Kobayashi et al., 2003). Therefore, determinations of the intestinal expression patterns of other important OATP/Oatp family members could have critical clinical relevance. Unfortunately, limited in vivo data exist to ascertain the full functional significance of this transporter family with respect to oral drug absorption. It is clear that OCT family members function primarily in the elimination of cationic drugs and other xenobiotics in the kidney and the liver; however, their role with respect to intestinal transport is poorly understood. Studies suggest that OCTs, especially OCT1, mediate the basolateral uptake of organic cations from the blood to the intestine and may play an important role in the secretion of compounds to the small intestinal lumen. Jonker et al. (2001) demonstrated that the intestinal secretion of TEA was reduced about twofold after i.v. administration in Oct1(−/−) knockout mice. Wang et al. (2002) further reported decreased metformin distribution to the mouse small intestine due to OCT1 knockout. Additionally, the role of OCTs in the active intestinal elimination of drugs is supported in part by the finding that in the presence of cephalexin, a known OCT substrate (Karlsson et al., 1993), plasma AUC of ciprofloxacin increased, while intestinal clearances decreased in rats (Dautrey et al., 1999). The role of other efflux membrane transporters may also contribute to this mechanism and cannot be ruled out. Unfortunately, targeting OCTs to increase oral bioavailability of drug substrates does not seem to be an attractive approach since these substrates are excreted across the sinusoidal membrane into the hepatocyte and further across the canalicular membrane to the bile by an as yet unidentified H+ -organic cation exchanger and/or by P-gp. In contrast, targeting OCTNs could be an efficient method to

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increase drug bioavailability, by taking advantage of both the transporter’s tissue and cellular localizations. OCTNs are localized to the apical membranes of intestinal and renal cells. As such, the transporter can effectively increase drug bioavailability via the absorptive intestinal OCTNs, while simultaneously maintaining blood concentrations by renal resorption through OCTNs. In one such example, the peak concentration and AUC of sulpiride were decreased by the concomitant administration of substrates or inhibitors of OCTN1 and OCTN2 in rats (Watanabe et al., 2004). Similar to the synthesis of valacyclovir, additional strategies have been developed to exploit the activity and selectivity of specific transporters. One such example is the development of anticancer agent prodrugs, which has been developed to improve not only their physicochemical properties, but also to promote their selectivity and thus reduce undesirable toxicity effects. Floxuridine and gemcitabine are anticancer agents where studies have shown the feasibility of achieving enhanced transport and selective antiproliferative action of amino acid ester prodrugs in cell systems overexpressing PepT1 (Vig et al., 2003; Landowski et al., 2005; Song et al., 2005). However, one should question the relevance of PepT1 transport for these particular compounds in normal physiologic systems in light of the fact that they are also potential substrates for other transporter families, such as the nucleoside transporters. However, the broad substrate specificity, tissue expression, and physiologic relevance of their substrates limit the utility of both nucleoside and monocarboxylate transporters as effective drug targets (see Sects. 7.3.4 and 7.3.5). This example illustrates one of the many problems inherent to the study of drug transport in that overlapping specificity of many of these transporter systems confounds the applicability of transport data to physiologic systems. Technology hurdles also limit the feasibility of studying multiple transporter systems simultaneously, thereby further limiting our understanding of the relevance of a particular transporter to overall transcellular flux. Drug transporters play an important role in intestinal drug absorption and secretion, and can be major determinants of oral bioavailability. Transporters exhibit affinity for an extraordinary range of compounds and provide great insight for advancing the field of rational drug design. However, understanding the limitations associated with transporter research will aid future scientists in understanding the subtle interactions of each of the distinct transporter families, and may help elucidate their overlapping specificities as well as provide additional therapeutic targets. By understanding the substrate specificity, transport mechanism, and expression profile of transporters, efficient intestinal absorption may be made feasible by strategies including appropriately modifying either the structural recognition elements of NCEs or through rational formulation design to tailor delivery to windows of optimized drug delivery. In addition, complete understanding of the mechanisms of intestinal absorption of various drugs and the underlying regulation of drug transporters could help pharmaceutical scientists to predict the intraand interindividual variability inherent to the study of oral bioavailability.

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8 Bioavailability and Bioequivalence Sam H. Haidar, Hyojong (Hue) Kwon, Robert Lionberger, and Lawrence X. Yu

8.1

Introduction

For systemically acting drugs, absorption is a prerequisite for therapeutic activity when drugs are administered extravascularly. Factors affecting drug absorption have been discussed in previous chapters. This chapter will cover general methods to evaluate bioavailability and bioequivalence. Scientific principles as well as regulatory perspectives related to these two topics will be discussed. Historically, the development of sensitive and precise bioanalytical methods in the 1960s and 1970s allowed for the first time the measurement of very low levels of drug concentrations in biological fluids. As a result, pharmacokinetic profiles of drugs, describing absorption, distribution, and clearance, could be determined. Regulations related to bioavailability and bioequivalence were put into place, considering the latest advances in the science. Currently, bioavailability and bioequivalence play a significant role in the discovery, development, and regulation of new drug products. Additionally, bioequivalence studies are a crucial component of abbreviated new drug applications (ANDAs), leading to market access of safe, effective, and low cost generic drugs.

8.2

Bioavailability and Bioequivalence

8.2.1 Bioavailability and its Utility in Drug Development and Regulation The therapeutic action of a drug is usually correlated with the delivery of the active substance to the site or more accurately, sites, of pharmacological response. US Federal regulations (21 Code of Federal Regulations (CFR), 2006) define bioavailability (BA) as “the rate and extent to which the active ingredient or active moiety is absorbed from a drug product and becomes available at the site of action”.

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Most drugs are systemically acting, meaning that they reach their sites of action through the systemic circulation. Thus, it is common for pharmaceutical scientists to evaluate the bioavailability of a drug product as the fraction of the dose reaching the systemic circulation. BA can depend on the physicochemical properties of the drug substance and the route of administration, in addition to drug product excipients and manufacturing process. The BA of a drug is an important attribute that is investigated early in drug development and used throughout development. In many cases, it is the deciding factor for whether or not a drug candidate is selected for further development (Sun et al., 2004). As stated in the FDA bioavailability and bioequivalence guidance (FDA, 2003a) BA studies help elucidate the process by which a drug is released from its dosage form and reaches the sites of action including the impact of presystemic metabolism and/or transporters. BA studies also provide information about the drug’s pharmacokinetic properties such as dose proportionality, linearity, and effect of food on absorption. When multiple formulations are used in the clinical development program, relative bioavailability studies can link observations of safety and efficacy to drug exposure and provide a basis for labeling or formulation optimization. BA studies can also be used in establishing an exposure–response relationship (FDA Exposure–Response guidance, 2003b).

8.2.2 Bioequivalence and its Utility in Drug Development and Regulation Federal regulations (21 CFR, 2006) define bioequivalence as: The absence of a significant difference in the rate and extent to which the active ingredient or active moiety in pharmaceutical equivalents or pharmaceutical alternatives becomes available at the site of drug action when administered at the same molar dose under similar conditions in an appropriately designed study.

Bioequivalence (BE) studies are a major component of ANDAs. They verify that the active ingredient in a generic drug product will be absorbed into the body to the same extent and at the same rate as its corresponding reference listed drug (RLD) product. The significance of BE studies is that when two pharmaceutically equivalent products are shown to be bioequivalent, the two products are judged to be therapeutically equivalent. Therapeutically equivalent products are expected to have the same safety and efficacy profiles, when administered under the conditions listed in the product labeling. This is the basis for the approval and use of generic drug products. BE studies are not only performed as part of the ANDA process, but also conducted by new drug manufacturers to confirm equivalence between formulations when it is necessary to make manufacturing and/or formulation changes. For example, often the marketed drug product is different in formulation or method of manufacture from the product used in the safety and efficacy clinical trials. These differences may be the result of formulation changes necessary to scale up

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the product from a small (laboratory or pilot) scale size to a large scale (commercial) size. After approval, the New Drug Application or NDA sponsor may significantly modify the scale of product runs, equipment, manufacturing process, formulation and dosage forms, ingredient specifications, source of supplies, and method of synthesis of the active ingredient. In these cases, the marketed or reformulated product must demonstrate bioequivalence to the original formulation to link the safety and efficacy data of the original product to the new product.

8.2.3 Bioavailability and Bioequivalence Studies: General Approaches There are several acceptable approaches for the determination of BA and BE. Title 21 of the Code of Federal Regulations (21 CFR, 2006) §320.24 lists the in vivo and in vitro methods of determining BA or BE for a drug product. They are: 1. (a) An in vivo test in humans in which the concentration of the active ingredient or active moiety, and, when appropriate, its active metabolite(s), in whole blood, plasma, serum, or other appropriate biological fluid is measured as a function of time; (b) an in vitro test that has been correlated with and is predictive of human in vivo bioavailability data. 2. An in vivo test in humans in which the urinary excretion of the active ingredient or active moiety, and, when appropriate, its active metabolite(s), is measured as a function of time. 3. An in vivo test in humans in which an appropriate acute pharmacological effect of the active moiety, and, when appropriate, its active metabolite(s), is measured as a function of time if such effect can be measured with sufficient accuracy, sensitivity, and reproducibility. 4. Well-controlled clinical trials that establish the safety and effectiveness of the drug product, for purposes of measuring bioavailability, or appropriately designed comparative clinical trials, for purposes of demonstrating BE. This approach is the least accurate, sensitive, and reproducible of the general approaches for determining BA or BE. 5. A currently available in vitro test acceptable to the FDA (usually a dissolution rate test) that ensures human in vivo BA. 6. Any other approach deemed adequate by FDA to measure BA or establish BE. For most systemically acting drugs, the active moiety can be detected and accurately measured in the plasma over time. Therefore, the first (pharmacokinetic) method (1) listed above is preferred. This pharmacokinetic method is generally considered as the most sensitive, accurate, and reproducible method for the assessment of BA and BE. Section 8.3 describes the conduct of these types of studies in detail.

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For some drug products, there is sufficient understanding of the physicochemical properties and biological factors that affect BA that there is no need for in vivo BE studies. Section 8.4 describes the situations in which the in vivo studies can be waived. Drugs that do not reach their sites of action through the systemic circulation are defined as locally acting drugs. For locally acting drugs, the bioequivalence method of choice is usually dependent on the attributes of the drug and drug product including physicochemical properties, BA, route of administration, site of action, and ability to detect/measure the active moiety. FDA recommends the most sensitive, accurate, and reproducible method for a particular product be used. Section 8.5 provides examples of the selection of BE methods for locally acting drugs

8.3 Pharmacokinetic Bioavailability and Bioequivalence Studies 8.3.1 Bioavailability Studies: General Guidelines and Recommendations BA for systemically acting, orally administered drug products is usually determined by measuring the concentration of the active ingredient and, when appropriate, its active metabolites over time in samples collected from the systemic circulation. Figure 8.1 shows a typical concentration–time profile. The profile determines the following important parameters: 1. Cmax is the maximum observed plasma concentration. 2. AUC0−t is the area under the concentration–time curve. It is calculated using the trapezoid rule on the actual data points. 3. Tmax is the time at which Cmax is observed. 4. ke is the terminal elimination rate constant determined from fitting the tail of the profile to a linear elimination model: dC/dt = −ke C. 5. t1/2 is the terminal half-life. t1/2 = 0.693/ke. It is the time it takes for the concentration to be reduced by half due to drug elimination. 6. AUC∞ is the area under the concentration–time curve extrapolated to infinity. AUC∞ = AUC0−t + Clast /ke . Clast is the last observed concentration. Cmax is usually correlated with the rate of absorption and AUC reflects the extent of absorption, and thus are considered the parameters most relevant to safety and efficacy. In most cases, BA studies are conducted as a single dose comparison between a test product and a reference product. These studies are generally conducted using a cross-over design in which each subject receives both treatments in a random order. Treatments should be separated by a washout period exceeding five halflives of the active moieties measured. Following administration of the test or reference product, blood samples are collected to obtain a profile of the time-course

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F IGURE 8.1. Example of a plasma-concentration time profile

of the drug for each subject. Drug or active metabolite concentrations in the urine may be used when they are not detectable in the blood or plasma. The sampling schedule, which may differ for each drug, should be of sufficient frequency to ensure precise estimation of the drug’s pharmacokinetic parameters. It is generally recommended that samples should be collected over at least three times the elimination half-life of the drug. As stated previously, a drug’s BA can be impacted by its route of administration. By definition, a drug which is administered intravenously (i.v.) has 100% BA. However, BA generally decreases when the drug is administered by extravascular routes (e.g., oral, transdermal, etc.). This decrease is usually a function of incomplete absorption, and/or presystemic metabolism or degradation. BA may be classified as absolute or relative. Absolute BA is the fraction of the administered dose that reaches the systemic circulation relative to an intravenous dose, while relative bioavailability is the fraction of the dose of a test product that reaches the systemic circulation relative to a non-i.v. reference product. For example, a tablet may have an absolute BA of 60% (or some other value); however, the same tablet would have a relative BA of 100%, if the drug in the tablet is absorbed to the same extent as an oral solution of the same drug. The relative BA of the tablet in this case is specific to the oral solution, and may differ relative to other dosage forms. Absolute BA is calculated by taking the ratio of the dose-corrected AUC of the test product (oral) divided by AUC of the i.v. reference product. Mathematically, the relationship is expressed as AUCPO × DoseIV AUCIV × DosePO

(8.1)

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where AUCPO and AUCIV are the area under the concentration–time curve after oral and intravenous administration, and Dosepo and Doseiv are the amount of dose given orally and i.v., respectively. Similarly, relative BA can be measured using a test product and a reference (non-i.v.) drug, and calculated by F=

AUCtest × Doseref . AUCref × Dosetest

(8.2)

8.3.2 Bioequivalence Studies: General Guidelines and Recommendations It is generally recommended that BE studies be conducted using a single dose, cross-over design. Parallel and replicate designs are also acceptable, and may be more appropriate under certain circumstances. Treatments are usually administered to healthy subjects, representative of the general population. Samples of an accessible biologic fluid, usually blood or urine, are analyzed for drug concentrations. Pharmacokinetic parameters, such as AUC and Cmax , are determined from the resulting concentration–time profiles. 8.3.2.1 Study Design In the standard cross-over design for in vivo BE studies, subjects receive a single dose of test and reference products on separate occasions with random assignment to the two possible sequences of product administration. Treatments are separated by a washout period exceeding five half-lives of the active moieties measured. Parallel designs in which separate groups of subjects receive the test and reference products require larger numbers of subjects and are recommended only in special cases when the half life of the drug is so long that the cross-over design is not feasible. The use of replicate designs for highly variable drugs is discussed in Sect. 8.3.3.3. Single dose studies are recommended over multiple dose studies because single dose studies are generally more sensitive “in assessing release of the drug substance from the drug product into the systemic circulation. . .” (FDA BA/BE guidance, 2003a). 8.3.2.2 Dose For a product with multiple strengths, the highest strength is usually recommended for use in a BE study. The pharmacokinetics of most drugs is well described by linear absorption, distribution, and clearance processes. The rate of linear processes increases proportionally to the amount of drug or the dose. Thus a bioequivalence conclusion for one of these drugs will be same at any dose. For drugs with nonlinear pharmacokinetics, the dose used in the BE study should be the most sensitive to differences in formulation. The most common source of nonlinear pharmacokinetics is saturable metabolism, where the rate of metabolism reaches a maximum that is independent of drug concentration.

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Other potential causes for nonlinear pharmacokinetics are solubility limited absorption or saturable uptake mediated by transporters. If there are safety concerns with administration of a single dose of the highest strength in healthy subjects, FDA will recommend use of a lower dose. 8.3.2.3 Subjects Because determination of BE is dependent on statistical methods, the number of subjects in the study should be sufficient to ensure adequate power. The typical number of subjects is 24–36 with the minimum number of subjects in the study being 12. Healthy subjects are recommended for BE studies for two main reasons: patients are more variable and patients require continuous treatment that does not allow for a washout period. The greater variability observed in patients has a direct impact on the sensitivity of BE testing. Patients are generally used only when drug is not safe to administer in healthy subjects. The physical processes of drug absorption for solid oral dosage forms are usually the same in patients as they are in healthy subjects. Given that BE studies compare the relative performance of two formulations, any conclusion drawn in healthy subjects will also apply to patients. This is true even for products where there is a known difference in BA between patients and healthy subjects. 8.3.2.4 Statistical Analysis of Bioequivalence Pharmacokinetic parameters AUC and Cmax are analyzed statistically because of the variability inherent in human subjects. This variability may be observed when the same subject receives the same drug product on two different occasions, i.e., the resulting plasma concentrations will not be exactly the same. Because of this inherent variability, an individual who takes two different products on separate occasions may show a measurable difference in the pharmacokinetic parameters. In this situation, it is not clear whether this difference is the result of a difference between the products, or the result of normal within subject variability. Thus, FDA recommends that ANDA or NDA applicants use statistical methods to estimate more accurately those differences in pharmacokinetics that result from the two product formulations. When considering the results from BE studies, it is important to understand what statistical tests are used and how FDA uses the results of these statistical tests to determine whether two products are bioequivalent. The following is a qualitative description, drawing on FDA’s responses to citizen petitions related to BE (FDA, 2004). Details of the statistical calculations can be found in the FDA guidance statistical approaches to establishing bioequivalence (FDA, 2001b). The mean is the average of all the differences in pharmacokinetic values observed in the small group of study subjects. For example, in a study the mean AUC of the test product might be 99% of the AUC of the reference product. The mean difference in this case would be 1% and the mean ratio would be 99%. However, if the same BE study is repeated in another small group of subjects, the second study’s mean may be different from the first study’s mean. Therefore, FDA

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uses a statistical confidence interval to provide an estimated range that is likely to contain the mean if the drug were given to the entire population. In our example study with a mean ratio of 99%, the confidence interval might be 89–109%. This confidence interval shows that for the entire population, the ratio of the mean AUC between test and reference products is likely (with a 90% probability) to be between 90 and 118%. If the small study used a greater number of subjects to more accurately reflect the general population’s results, then the 90% confidence interval would be smaller (i.e., a smaller range of the possible pharmacokinetic values in the general population, such as 93–105%). FDA determines whether a study shows that two products are bioequivalent based on the confidence interval and not on the mean value of the study. The results of a study are expressed as a confidence interval for the ratio of test to reference products. To decide whether two products are bioequivalent, the calculated confidence interval is compared to an acceptance interval. The acceptance interval (also referred to as acceptance limits) is expressed as two numbers that provide upper and lower limits on the confidence interval. If the confidence interval is contained within this acceptance interval, then FDA concludes that the study demonstrates BE; if not, then the study does not demonstrate BE. The acceptance interval is a fixed standard, while the confidence interval is determined from the data in a particular study. FDA considers two drug products equivalent when the 90% confidence intervals of the geometric mean ratio for Cmax and AUC are entirely within 80–125% (see Fig. 8.2). The choice of the 80–125% acceptance limits is based on medical opinion and FDA experience which determined that a difference of 20% or less in drug exposure was not clinically significant for most drugs. Thus, the limits of 80–125% were set around a difference of less than 20% in geometric mean ratio (test/reference) for Cmax and AUC, although in practice this difference rarely

F IGURE 8.2. Hypothetical results from bioequivalence (BE) tests for approved generic drugs

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exceeds 10%. FDA has not found any clinical problems resulting from the thousands of drug products approved with the current BE criteria. As mentioned above, the 80–125% boundaries are acceptance limits for the confidence interval and not a judgment about the acceptable mean differences between test and reference products. The sample mean ratio of the pharmacokinetic values for the test and reference products lie at the center of the confidence interval. Because this confidence interval must fall within the 80–125% boundaries, these statistical criteria limit the acceptable range in which the mean values can stray from the 100% ratio. The actual mean differences FDA found for drugs tested and analyzed under this statistical procedure were much smaller than the 80–125% boundaries. In the 1980s, FDA reviewed 224 BE studies that passed the 80–125% criterion (Nightingale and Morrison, 1987). In these studies, the observed mean difference in AUC between the brand name and the generic product was approximately 3.5%. This analysis was repeated for the 127 BE studies conducted for generic drugs approved in 1997 (Henney, 1997). The average observed difference in AUC in these studies was approximately 3.3%. Recently, FDA surveyed the BE data for the 10-year period of 1996–2005. Results from more than 1,500 BE studies were analyzed. Once again, the mean difference in AUCs between generic products and their brand name counterparts averaged less than 4%. Figure 8.2 graphically illustrates the relationship between the mean value obtained from a BE study, the 90% confidence interval for that BE study, and

F IGURE 8.3. Plasma concentrations over time for parent drug and major metabolite, for a drug (Drug A) with extensive presystemic metabolism

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FDA’s acceptance limits of 80–125%. The center of each box is the mean value from a BE study, while the entire box represents the confidence interval from the same BE study. Because the 80–125% acceptance limits are bounds on the confidence intervals, the mean values from passing BE studies must be closer to 100%. As can be seen in Fig. 8.2, the actual mean differences between test and reference listed products will be much smaller than FDA’s BE acceptance limits of 80–125%.

8.4

Bioequivalence: Challenging Topics

Some situations where the evaluation of BE presents a challenge include drugs with active metabolites, enantiomers, endogenous substances, and highly variable drugs. Each of these conditions will be discussed in some detail below.

8.4.1

Drugs with Active Metabolites

Following administration, drugs generally undergo biotransformation or metabolism to facilitate their elimination from the body. Metabolism can be systemic or presystemic. In systemic metabolism, drug in circulating blood is exposed to metabolic enzymes as it passes through the liver and other tissue. Presystemic metabolism occurs when the drug is exposed to metabolic enzymes found in the gut wall, skin, or other absorption sites. Additionally, presystemic metabolism occurs when the drug is metabolized by the liver immediately after oral absorption, prior to reaching the site of action (hepatic first pass effect). The relevance of this distinction to BE is that systemic metabolism is determined by the concentration of drug in the systemic circulation, while the presystemic metabolism can be affected by the rate and extent of the release of the absorption of the drug and its rate of release from the drug product. The biotransformation of drugs can lead to the formation of compounds (metabolites) which may be active or inactive pharmacologically. The metabolites’ activity may impact the efficacy of the drug or its side effects. The guidelines for using metabolites in BA or BE studies differ depending on the circumstances, i.e., whether the studies are part of a drug development program supporting an NDA, or they are BE studies supporting an ANDA for a generic product. During drug development (investigational new drug (IND) or NDA), the objective is to learn as much as possible about a new compound that may become a marketed drug product. Thus, the recommendations for BA studies include measurement of the parent drug and all active metabolites. Determination of the activity of each metabolite relative to the parent is also desired. On the other hand, by the time a patent expires on a drug and it becomes a candidate for generic competition, much is known about the drug’s attributes and clinical performance. As a consequence, the objectives of a BE study supporting an ANDA are different from those for an NDA. For example, the goal of a BE study in the ANDA is to evaluate formulation performance of a generic candidate

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relative to that of the RLD. Given that the concentration–time profile of the parent drug is generally more sensitive to differences in formulation performance, it is recommended that only concentrations of parent drug released from the dosage form be measured. This is true even if the drug has active metabolites. The BA/BE Guidance (FDA, 2003a), however, does provide situations where metabolites should be measured in a BE study. These are quoted below: 1. Measurement of a metabolite may be preferred when parent drug levels are too low to allow reliable analytical measurement in blood, plasma, or serum for an adequate length of time. We recommend that the metabolite data obtained from these studies be subject to a confidence interval approach for BE demonstration. If there is a clinical concern related to efficacy or safety for the parent drug, we also recommend that sponsors and/or applicants contact the appropriate review division to determine whether the parent drug should be measured and analyzed statistically. 2. A metabolite may be formed as a result of gut wall or other presystemic metabolism. If the metabolite contributes meaningfully to safety and/or efficacy, we also recommend that the metabolite and the parent drug be measured. When the relative activity of the metabolite is low and does not contribute meaningfully to safety and/or efficacy, it does not have to be measured. We recommend that the parent drug measured in these BE studies be analyzed using a confidence interval approach. The metabolite data can be used to provide supportive evidence of comparable therapeutic outcome. In the first case, a metabolite is measured in a BE study when levels of the parent drug are too low for accurate measurement, as is the case with some prodrugs. The statistical criteria for BE determination is applied to the metabolite(s) in this case. For example, the 90% confidence interval of the geometric mean ratio of test/reference AUC and Cmax of the metabolite must fall within 80–125%. This may be the only situation where the confidence interval approach is used with the metabolite for the demonstration of bioequivalence. In the second case, measurement of the active metabolite is recommended when there is evidence of hepatic first pass metabolism and/or gut presystemic formation of the metabolite. The active metabolite must also contribute significantly to the efficacy and/or safety profile of the drug. Unlike BE studies involving prodrugs, metabolite concentrations in this case are not subject to the BE statistical criteria (confidence interval approach), but summary statistics of the PK parameters serve as supporting evidence of bioequivalence. The parent drug, however, is evaluated statistically using confidence intervals as it would be in studies that do not include measurement of the metabolite(s). To apply the second case to a particular drug product, there must be presystemic metabolism. One indicator suggesting presystemic metabolism is the early appearance of metabolite levels in the plasma, usually preceding parent drug levels. Plasma levels of the metabolite may also be significantly higher, relative to the parent. Figure 8.3 illustrates the early appearance, as well as higher levels, of

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a major metabolite relative to that of the parent, for a sample drug (Drug “A”) that has significant presystemic metabolism.

8.4.2

Enantiomers vs. Racemates

Enantiomers are stereoisomers, i.e., molecules that are nonsuperimposable mirror images of each other, with identical chemical and physical properties (Wade, 2003). A mixture of equal parts of an optically active isomer and its enantiomer is called a “racemate.” Enantiomers may differ in pharmacological activity and pharmacokinetic properties. This may be related to their three-dimensional fit within cell receptors or enzymes, leading to possible differences in pharmacological responses and potencies, as well as differences in absorption and clearance. It is generally recommended that chiral assays (which can distinguish individual enantiomers) be used in BA studies during drug development. As stated above, enantiomers could potentially have different pharmacokinetics and pharmacological activities. This recommendation, however, does not extend to BE studies supporting ANDAs. The reason is, racemate levels of the parent drug, or in some cases the metabolite, may be adequate at detecting differences in formulation performance. There are exceptions, however, to this general rule. The BA/BE Guidance lists four conditions, all of which have to be met, before measurement of the individual enantiomers are needed in BE studies. They are (1) the enantiomers exhibit different pharmacodynamic properties, (2) the enantiomers exhibit different pharmacokinetic properties, (3) the primary safety and efficacy resides in the minor enantiomer (one with the lowest concentration), and (4) there is evidence of nonlinear absorption for at least one of the enantiomers. In rare cases where a drug product meets all of the above four conditions, then a chiral assay is recommended to measure the concentration of individual enantiomers in a BE study supporting an ANDA.

8.4.3

Endogenous Substances

Drug products whose active ingredient is an endogenous substance (one that naturally occurs in the body) present a challenge to evaluating bioequivalence because a measurement of plasma concentration would include both the endogenous concentration plus the amount added by administration of the drug product (exogenous source). This may act to bias the results of a BE study. To illustrate the potential error we use a model drug where B is the baseline level, R is the change in drug level above the baseline due to the reference product, T is the change in drug level above the baseline due to the test product. The apparent test to reference ratio is B+T , B+R

(8.3)

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which should be compared to the true test to reference ratio of T /R. To estimate the error in this case let B = 200, R = 200, T = 150, and T /R = 0.75. The apparent ratio (unadjusted for baseline) is 0.875 compared to a true ratio (adjusted for baseline) of 0.75. This is a significant reduction in the ability to identify product differences. One approach that has been successful for some drug products is a baseline correction method. In this approach, the measured predose concentration of the endogenous substance is subtracted from the measured concentration profiles after administration of the drug product. The remaining concentration should better reflect the amount delivered by the drug product. When using this approach it should be noted that production of many endogenous substances is under feedback control and may be altered by the administered dose and thus there may be a nonlinear dependence on the external dose. Potassium chloride is an example drug for which the feedback control of the endogenous drug concentration is effective to the extent that there is no significant change in potassium concentration after administration of normal doses. For this drug, FDA recommends a urinary recovery study (FDA, 2002).

8.4.4

Highly Variable Drugs

The BE of highly variable drugs and drug products has been discussed in many conferences and meetings, nationally and internationally (Blume and Midha, 1993; Shah et al., 1996). Highly variable drugs are generally defined as drugs or drug products which exhibit within subject variability of 30% or greater. Drugs with high within subject variability can present challenges in BE studies because of impact on sample size. For example, when comparing the BA of a highly variable reference product with itself, the sample size needed to demonstrate BE can exceed 100 subjects, although there are no true differences in BA between test and reference in this case. Table 8.1 (adapted from Patterson et al., 2001) provides more precise information about the association between within subject variability and sample size needed to achieve adequate statistical power. Evaluating the BE of highly variable drugs using the standard criteria may present ethical concerns, i.e., unnecessary human testing, in addition to the practical difficulties of large BE studies. For this reason, the FDA has been evaluating different approaches for determining BE that would decrease sample size, without increasing patient risk. Several papers have been published on this topic. Some of the methods studied are summarized below. 8.4.4.1 Static Expansion of the BE Limits Sample size in BE studies is generally determined by the BA parameter with the highest within subject variability. In most cases, this parameter is Cmax . The greater variability observed with Cmax may result from the fact that this parameter is a single point measurement, which is highly dependent on the sampling time/frequency and the elimination rate of the drug. Arbitrary widening of the BE

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TABLE 8.1. Sample sizes providing 90% power in two-way, cross over bioequivalence studies CVw % 30

45

60

75

% difference in true BA ratio

Number of subjects needed

0 5 10 0 5 10 0 5 10 0 5 10

40 54 112 84 112 230 140 184 384 200 264 554

CVw %, within subject coefficient of variation; BA, bioavailability

limits for Cmax has been proposed as one approach to reduce sample size when evaluating the BE of highly variable drugs. This entails increasing the 90% confidence interval limits for Cmax from 80–125% (current FDA criterion) to 75–133%, or even 70–143%. 8.4.4.2 Expansion of Bioequivalence Limits Based on Fixed Sample Size The basis of this approach is the belief that only a reasonable number of subjects should be used in BE studies (Boddy et al., 1995). To conduct a study with the above method, a fixed number of subjects, e.g., 24, is used in a standard twoperiod, cross-over design comparing the reference product with itself. For a highly variable drug, this study is likely to fail the 80–125% criteria, because of low power with only 24 subjects. However, the 90% confidence interval obtained from the reference product would become the new “goalpost,” or criteria, for subsequent studies comparing a test product (proposed generic) with the reference product (RLD), using the same number of subjects (24). According to Boddy et al. (1995), a drawback of this method is that the wider acceptance limits are based on controlling the sample size, instead of a meaningful measure of formulation differences (Boddy et al., 1995). Additionally, when the test product differs from the RLD by a small measure, there is no guarantee that the confidence interval for the test vs. reference product will fall within the “goalposts” set by the first study, where the reference is compared with itself. 8.4.4.3 Scaled Average Bioequivalence Finally, a third proposed approach that is currently favored by the FDA is scaled average BE. This method entails widening of the BE limits as a function of the within subject variability of the reference product. In this case, instead of using fixed limits, i.e., 80–125%, to determine if a test product is BE to the reference product, the limits expand as within subject variability of the reference increases.

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The variability of the reference is determined in a replicate or partial replicate design, where the reference product is administered twice to the same subject at different periods. Mathematically, this approach may be described by 0.223 σwr , BE limits, upper, lower = exp ± (8.4) σw0 where σwr is the within subject standard deviation of the reference, and σw0 is a constant set by the regulatory agency. One concern about the use of average scaling for BE purposes is the lack of sensitivity of this method to differences in the point estimate, or the test/reference ratio of the geometric means. At least in theory, the lack of sensitivity may lead two products with unacceptably large differences in formulation performance to be declared BE. For this reason, the FDA has proposed the use of point estimate constraints in conjunction with scaled average BE. For example, if a BE study passes the confidence interval criteria (scaling), but the mean ratio between test and reference exceeds a predefined limit (e.g., ±20), then the two products may not be judged BE for regulatory purposes. In a simulation-based study conducted at the FDA, the impact of scaled average BE on power was evaluated, and compared to the power of average BE (traditional criteria). Using a sample size of 36 subjects, one million studies were simulated for each variable tested. As can be seen in Fig. 8.4, the scaled average BE can have a significant impact on power even when the point estimate constraint is applied. For example, at within subject variability of 60%, the power of the study using average BE is about 24%, when the test and reference have zero differences in BA. Applying scaled average BE with point estimate constraint under the same test conditions, the power increases to >90%. Thus, scaled average BE appears to have a great practical advantage over traditional BE methods, when it comes to highly variable drugs.

8.5

Biowaivers

For some drug products, there is sufficient understanding of the physicochemical properties and biological factors that affect BA that there is no need for in vivo BE studies. Sponsors may request waivers of BE studies (biowaivers) for solutions, products with a range of strengths, and biopharmaceutical classification system (BCS) Class 1 drugs.

8.5.1

Solutions

In vivo BA/BE is self-evident for certain drug products, such as topical solutions, solution nasal spray, oral solutions, elixirs, syrups, tinctures, or other solubilized forms of the drug. For these products, in vivo BA/BE can be waived, according to 21 CFR 320.22(b).

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F IGURE 8.4. The difference in power, or percent of studies passing BE, between scaled average BE with a point estimate constraint and average BE (traditional criteria). A million BE studies with a sample size of 36 subjects were simulated. The geometric mean ratio reflects differences between the test and reference products

This waiver assumes that release of the drug substance from the drug product is self-evident and that the solutions do not contain any excipient that significantly affects drug absorption (21 CFR 320.22(b)(3)(iii)). The FDA can deny a biowaiver request if differences in excipients have the potential to change BE. For example, xylitol, sorbitol, and mannitol are commonly used formulation excipients for drug products (Fassihi et al., 1991; Fukahori et al., 1998). They are also used as artificial sweeteners in the food industry. These excipients are not well absorbed in the gastrointestinal (GI) tract. Additionally, they increase the osmotic pressure in the intestine, which changes the flux of water in the GI tract. This osmotic stress can change the gastric emptying time and the intestinal transit times through both the upper and lower parts of the intestine. Transit times in the GI can impact drug absorption. The total amount of drug absorbed depends on the rate of absorption from the intestine and the total time that the drug is present in the intestine. When transit or emptying times are decreased, there is less time available for drug molecules in solution to be absorbed and thus, the total absorption may be decreased. Scintigraphic evidence suggests that osmotic agents can have minor effects on the residence time in the upper intestinal tract, but significantly reduce the residence time in the lower intestinal tract (Adkin et al., 1995; Kruger et al., 1992). The osmotic pressure changes may also affect the rate of transport across

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the intestinal wall in addition to changing transit times, which could lead to changes in absorption of low permeability drugs (Polli et al., 2004). As an example of this effect, Chen et al. (2007) measured the pharmacokinetics of ranitidine in a four way cross-over study of ranitidine oral solution dosed with various amounts of sorbitol (Chen et al., 2007). Doses of sorbitol greater than 1.25 g significantly reduced the BA of ranitidine from an oral solution. Another example of a pharmaceutical excipient with a demonstrated effect on drug absorption is polyethylene glycol 400 (PEG 400). Several studies investigated the effect of PEG 400 on the absorption characteristics of ranitidine from the gastrointestinal tract (Basit et al., 2002; Schulze et al., 2003). These studies show that there is no significant effect of PEG 400 on gastric emptying; however, the presence of PEG 400 reduced the mean small intestinal transit times of the ranitidine solutions containing PEG 400. This resulted in changes in drug absorption that depended upon the amount of PEG 400. Low concentrations of PEG 400 increased the absorption of ranitidine, presumably due to changes in intestinal permeability of ranitidine, whereas high concentrations of PEG 400 reduced ranitidine absorption possibly due to shorter small intestinal transit time.

8.5.2

Lower Strength

Waiver of in vivo studies for different strengths of a drug product can be granted under §320.22(d)(2) when (1) the drug product is in the same dosage form, but in a different strength; (2) this different strength is proportionally similar in its active and inactive ingredients to the strength of the product for which the same manufacturer has conducted an appropriate in vivo study; and (3) the new strength meets an appropriate in vitro dissolution test. The FDA guidance (FDA, 2003a) defines proportionally similar in the following ways: 1. All active and inactive ingredients are in exactly the same proportion between different strengths (e.g., a tablet of 50-mg strength has all the inactive ingredients, exactly half that of a tablet of 100-mg strength, and twice that of a tablet of 25-mg strength). 2. Active and inactive ingredients are not in exactly the same proportion between different strengths as stated above, but the ratios of inactive ingredients to total weight of the dosage form are within the limits defined by the SUPAC-IR (FDA, 1995) and SUPAC-MR (FDA, 1997a) guidances up to and including Level II changes. 3. For high potency drug substances, where the amount of the active drug substance in the dosage form is relatively low, the total weight of the dosage form remains nearly the same for all strengths (within ±10% of the total weight of the strength on which a biostudy was performed), the same inactive ingredients are used for all strengths, and the change in any strength is obtained by altering the amount of the active ingredients and one or more of the inactive ingredients. The changes in the inactive ingredients are within the limits defined by the SUPAC-IR (FDA, 1995) and SUPAC-MR (FDA, 1997a) guidances up to and including Level II changes.

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Biopharmaceutical Classification System

The BCS is a scientific framework for classifying a drug substance based on its aqueous solubility and intestinal permeability (Amidon et al., 1995). The solubility classification of a drug in the BCS is based on the highest dose strength in an IR product. A drug substance is considered highly soluble when the highest strength is soluble in 250 ml or less of aqueous media over the pH range of 1.0–7.5. Otherwise, the drug substance is considered poorly soluble. The volume estimate of 250 ml is derived from typical BE study protocols that prescribe administration of a drug product to fasting human volunteers with a glass (about 8 oz) of water. The permeability classification is based on the extent of intestinal absorption of a drug substance in humans. The BCS guidance indicates for permeability that “In the absence of evidence suggesting instability in the gastrointestinal tract, a drug substance is considered to be highly permeable when gastrointestinal absorption in humans is determined to be 90% or more of an administered dose based on a mass balance determination or in comparison to an intravenous reference dose.” The BCS guidance also provides for high permeability to be determined in in-vitro epithelial cell culture studies using suitable high and low permeability reference standards. Otherwise, the drug substance is considered to be poorly permeable. Solubility and permeability classifications result in four classes of drug substance: BCS Class 1: highly soluble and highly permeable BCS Class 2: poorly soluble and highly permeable BCS Class 3: highly soluble and poorly permeable BCS Class 4: poorly soluble and poorly permeable An IR drug product is considered rapidly dissolving when not less than 85% of the labeled amount of the drug substance dissolves within 30 min using USP Apparatus I at 100 rpm or USP Apparatus II at 50 rpm in a volume of 900 ml or less of each of the following media: (a) acidic media, such as 0.1N HCl or USP simulated gastric fluid without enzymes (SGF); (b) a pH 4.5 buffer; and (c) a pH 6.8 buffer or USP simulated intestinal fluid without enzymes (SIF). Otherwise, the drug product is considered to be a slow dissolution product. When combined with the in vitro dissolution characteristics of the drug product, the BCS takes into account three major factors: solubility, intestinal permeability, and dissolution rate. These factors govern the rate and extent of oral drug absorption from IR solid oral dosage forms (FDA, 2001a). The FDA BCS guidance indicates that sponsors of NDAs and ANDAs may request biowaivers for highly soluble and highly permeable drug substances (Class 1) formulated in IR solid oral dosage forms that exhibit rapid and similar in vitro dissolution. Rapid and similar dissolution is demonstrated by 1. Both drug products do not have less than 85% dissolution in 30 min in 900 ml at pHs of 1.2, 4.5, and 6.8

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2. Similarity is demonstrated by an f2 comparison in all three pH conditions (the f2 test is not necessary if both products have 85% dissolution in 15 min or less). Other conditions that should be met to qualify for a biowaiver are (a) the drug must be stable in the gastrointestinal tract, (b) excipients used in the IR solid oral dosage forms have no significant effect on the rate and extent of oral drug absorption, (c) the drug must not have a narrow therapeutic index, and (d) the product is designed not to be absorbed in the oral cavity. The BCS guidance is generally considered to be conservative with respect to the class boundaries of solubility and permeability, and the dissolution criteria. Thus, the possibility of modifying these boundaries and criteria to allow waivers of in vivo BE studies, i.e., biowaivers, for additional drug products has received increasing attention. There are possible opportunities to expand the BCS-based biowaivers to drugs that are not BCS Class 1. 8.5.3.1 Biowaivers for BCS Class 2 Drugs with pH Dependent Solubility Some drugs (weak bases) are classified as BCS Class 2 because they are highly soluble at low pH, but fail to meet the BCS solubility limit at higher pH. For these BCS Class 2 drugs, their absorption is complete before they reach a pH where their solubility is decreased significantly. For other BCS Class 2 drugs (weak acids), limited solubility at low pH (acid) may not be physiologically relevant and the solubility at higher pH (e.g., pH > 5) is more appropriate. This may be true because most drugs are absorbed in the intestinal region (Yazdanian et al., 2004; Rinaki et al., 2004). It was questioned by Polli et al. (2004) that a solubility of the highest strength in 250 ml over the range pH 1–7.5 is conservative and solubility may need to be conducted only between pH 4.5 and 6.8 considering the pH range of the small intestine. Yu et al. (2002) suggest a potential intermediate solubility class for drugs that are soluble either in the stomach or in the intestine, because BCS Class 2 drugs would be absorbed in the intestine due to high permeability as long as the drugs are dissolved before or at the time when they reach the absorbing region of the intestine. 8.5.3.2 Biowaivers for BCS Class 3 Drugs For rapidly dissolving dosage forms of BCS Class 3 drugs (high solubility, low permeability), intestinal permeability is considered to be the major rate-controlling step in oral drug absorption. Thus, these rapidly dissolving BCS Class 3 drug products are expected to behave like an oral solution. Drug dissolution and other formulation differences are unlikely to have effect on the rate and extent of drug absorption, as long as excipients do not alter intestinal permeability or intestinal residence time of the drug. To ensure rapid dissolution in vivo using in vitro dissolution, Yu et al. (2002) and Polli et al. (2004) suggest a more rapid in vitro dissolution criterion (not less than 85% within 15 min), because the sink condition common in in-vitro dissolution may not exist in vivo.

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Locally Acting Drugs

Locally acting drug products require exploration of alternative bioequivalence methods because plasma concentration profiles of these products are not always appropriate surrogates of pharmacological activity. Examples of locally acting products include topical dermatological products, inhalation and nasal products. For many of these products, FDA recommends a BE study with clinical endpoints. A BE study with clinical endpoints will use a product-specific clinical indication recommended by FDA. Patients in the study would be given the test product, the reference product, and/or a placebo. The placebo arm ensures that the study and its conduct are sufficiently sensitive to differences between treatments. If the reference product is labeled for multiple indications, then the indication that is most sensitive to difference in local delivery of drug is usually preferred. Most clinical endpoint BE studies have a dichotomous endpoint; the treatment either succeeds or fails. To decide if the test product is bioequivalent to the reference, the success proportion for each treatment is calculated, and if the 90% confidence interval for the difference in success is within −20% to +20%, then the test product passes. For dichotomous endpoints, there is no meaning to betweensubject variability and all studies must enroll approximately 200–600 subjects to ensure sufficient power. Some clinical endpoints are continuous variables or can be treated as such. For example, a reduction in a symptom score is a categorical endpoint, but for equivalence purposes may be treated as continuous data if certain assumptions are made. For these studies, the 90% confidence interval of test/reference ratio must be within 80–125%. The number of subjects required will depend on the between subject variability of the particular clinical endpoint. Because clinical endpoint BE studies can be larger than studies conducted in support of the initial NDA and the insensitivity of some clinical endpoints to formulation differences, there is much interest in developing new BE methods that are more efficient and more sensitive at detecting product differences. A recent addition to the Federal Food Drug and Cosmetic Act at Section 505(j)(8)(A)(ii) indicates that “For a drug that is not intended to be absorbed into the bloodstream, the Secretary may assess bioavailability by scientifically valid measurements intended to reflect the rate and extent to which the active ingredient or therapeutic ingredient becomes available at the site of drug action.” In the sections below, we will identify some alternative approaches to BE that have been employed, and point out some scientific challenges in developing new BE methods for locally acting products.

8.6.1

Topical Dermatological Products

Topical dermatological products are intended to treat conditions of the skin by direct application of the drug product to the skin. The skin consists of multiple layers beginning with the approximately 10 µm thick stratum corneum, the ≈100 µm thick living epidermis, and the ≈1,000 µm thick dermis. Depending on the drug,

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topical dermatological products can act at any of these layers, but because the drug is applied to the surface of the skin it must diffuse across each layer sequentially. Thus drug can reach at sites of action without ever being systemically absorbed. This definition excludes products such as transdermal delivery systems that are intended to deliver drug systemically. There are a variety of BE approaches that are or can be used for topical dermatological products: 1. Biowaivers (topical solutions) This is discussed in the section on biowaivers. For topical products, a biowaiver usually requires that the test and reference product contain equivalent amounts of the same inactive ingredients. This requirement is necessary because many excipients in topical products are penetration enhancers that can alter the permeability of the skin. Differences in excipients can also change how a topical product spreads or adheres to the skin, which can alter its efficacy. 2. In vitro tests (no current topical dermatological products qualify) The SUPAC-SS (FDA, 1997b) guidance discusses the role of in vitro release testing for semisolid dosage forms. The guidance states that “An in vitro release rate can reflect the combined effect of several physical and chemical parameters, including solubility and particle size of the active ingredient and rheological properties of the dosage form. In most cases, in vitro release rate is a useful test to assess product sameness between pre-change and post-change products” (FDA, 1997b). The use of in vitro release tests is currently limited to evaluating changes in manufacturing process, or scale-up by the same manufacturer and is not used for BE. However, when the test and the reference product have identical compositions, the only differences are in manufacturing and process scale. 3. In vivo pharmacodynamic studies (topical corticosteroids) For topical corticosteroids, there is an FDA guidance (1995b) that describes a pharmacodynamic BE study. As in all pharmacodynamic BE studies, it is necessary to establish sufficient sensitivity in the dose–response curve to detect differences between products. For the skin blanching study, the dose is varied by changing the amount of time the topical product is applied to the skin. A pilot study using a range of application times of the reference product is used to identify patients in whom skin blanching is sensitive to differences in application time. Then, these patients are used to compare the test and reference products. 4. In vivo pharmacokinetic studies (some topical anesthetics) For some topical dermatological products such as lidocaine/prilocaine cream,1 a pharmacokinetic BE study has been the only recommended study. For other topical products, a pharmacokinetic study is requested in addition to other studies 1 Publicly available at http://www.fda.gov/cder/foi/nda/2003/076453.pdf

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TABLE 8.2. Examples of study resultsa using a clinical endpoint to demonstrate BE N 728 453 447

% cure test

% cure ref

50 46 29

48 40 27

90% CI [−12, +16] [−8, +20] [−9, +13]

a All of these studies had three arms and used the difference

in cure rate as the endpoint with an acceptable 90% confidence interval of −20 to +20 percentage points

to evaluate whether the test and reference products provide equivalent systemic exposure. This additional BE study is usually requested because there are known safety issues related to systemic exposure. 5. Clinical endpoint BE studies (most other products) As mentioned before, most other topical products establish BE through a clinical endpoint study. Table 8.2 provides some example results of clinical studies used in support of ANDAs (Lionberger, 2004). All of these studies had three arms and used the difference in cure rate as the endpoint with an acceptable 90% confidence interval of −20 to +20 percentage points. The results show that even with relatively large numbers of patients in each study, the confidence intervals were close to the limits defined by FDA. This suggests that these studies were at a high risk of failure.

8.6.2 Locally Acting Nasal and Oral Inhalation Drug Products Locally acting nasal and inhalation drug products present significant BE challenges that have limited generic competition in these product categories. FDA has published a draft BE guidance for nasal spray products (FDA, 2003c) and approved one suspension nasal spray product following the publication of this guidance. Currently (2006), there is no BE guidance for inhalation products such as metered dose inhalers (MDI) and dry powder inhalers (DPI). Given that the performance of these products is determined by the properties of both the formulation and the delivery device, the general approach to BE includes demonstration of: 1. 2. 3. 4.

Qualitatively the same, and quantitatively essentially the same, formulations Container and closure systems that are as close as possible in critical attributes Equivalent drug product performance (through in vitro tests) Equivalent local delivery (through clinical bioequivalence or pharmacodynamic studies) 5. Equivalent systemic exposure (through a pharmacokinetic study)

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8.6.2.1 Nasal Spray Products For nasal spray solutions that are qualitatively and quantitatively the same as the reference product, and for which the container and closure system are as close as possible in critical attributes including metering chamber volume, the local delivery, and systemic exposure tests can be waived and BE can be demonstrated through equivalent in vitro drug product performance. Nasal spray suspension must demonstrate equivalence in three categories listed above (local delivery, systemic exposure, and device performance). There are six measurable properties identified for use in comparing the drug product-device performance of nasal spray products: 1. Single actuation content (SAC) through container (product) life 2. Droplet size distribution by laser diffraction 3. Drug in small particles/droplets, or particle/droplet size distribution by cascade impactor 4. Spray pattern 5. Plume geometry 6. Priming and repriming The tests require the use of 10 units from each of three lots of test and three lots of reference products. The FDA guidance (FDA, 2003c) did not specify the criteria for establishing equivalence. Prior to August 2005, FDA had evaluated in vitro studies on nasal sprays based on the ratios of geometric means of the test and reference products falling between acceptance limits of 90–111% and evidence for comparable variability of the test and reference products. Since 2005, FDA has been using population bioequivalence (PBE) to compare test and reference products. Information regarding the PBE methodology has been posted on the Agency Web site since April 11, 2003. Inherent in the PBE method is the principle that the BE acceptance limits depend upon the relative variability of the test and reference products observed in the study. This ensures that the acceptance limits are appropriate for the specific products being compared and are based on the characteristics of the approved RLD. In the case of low variability data for the reference product, the acceptance limits narrow toward the 90–111% criteria used in the previous geometric mean method, enabling only test products with comparable variability to meet the acceptance criteria. Conversely, in the case of a high variability reference product, the acceptance limits might be wider. Usually local delivery BE studies of inhalation products require either pharmacodynamic or clinical studies with a demonstrated dose–response. However, nasal sprays for treatment of seasonal allergic rhinitis (SAR), perennial allergic rhinitis (PAR), and perennial nonallergic rhinitis (PNAR) indications have very limited dose–response. For these indications, equivalent local delivery is assumed to occur when products meet the formulation and device recommendations listed above, and when drug-device performance demonstrates equivalence by showing similar effectiveness in a comparative clinical trial of test and reference products. This clinical trial involves a large number of subjects, which may range from 500 to

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1,000 patients. The lowest dose is usually recommended to increase the sensitivity of the study to potential differences between test and reference products, assuming an E max model, as described later in this section. The test product demonstrates equivalent systemic exposure in a pharmacokinetic study in healthy subjects. Because locally acting nasal spray products are not designed to deliver drug systemically, the conduct of the pharmacokinetic study can be challenging due to very low plasma concentrations. Significant analytical development effort may be required to develop a sensitive method to quantify the low plasma concentrations. 8.6.2.2 Oral Inhalation Products BE of MDI and DPI products follow the same general approach recommended for nasal spray products (equivalent in vitro drug product performance, local delivery, and systemic exposure). As opposed to solution formulation nasal sprays for which in vivo studies are not deemed necessary to establish BE, in vivo studies are recommended for both solution and suspension MDIs, as well as DPIs. For the drug product performance tests, a complete list of required tests is not available. However, the critical product quality attributes of MDI and DPI products have been discussed in the scientific literature and in product labeling. Almost all DPIs currently approved in the United States are breath-actuated so patients’ aspiratory effort provides energy for delivery of the powder formulation. There is population variation in the ability of asthma patients to induce flow through the device (Frijlink and De Boer, 2004). A generic sponsor should try to match the performance of the reference product. Localization of delivery in the lung is clearly related to the distribution of particle sizes emitted from the device. In vitro tests utilizing a cascade impactor can measure the aerodynamic particle size distribution in a way that is related to deposition in the lung, although it may not be predictive of in vivo deposition (Mitchell and Nagel, 2003). All of these aspects of product performance are affected by the properties of the particles in the formulation. Part of the challenge in designing a DPI is that performance also depends on the device used and the interactions of the particles in the formulation with the device. The aerosol produced by a given patient will depend on the design of the device (its resistance to airflow). The patient effort results in a velocity gradient applied to the dry powder formulation. The effect of a specific velocity gradient on a powder formulation (how much aerosol will be created) will depend on the particle properties. Thus, manufacturing of the powders for DPI may involve critical process parameters to control particle size and shape and surface properties (Telko and Hickey, 2005). Local delivery studies in asthma patients are challenging because many asthma drugs have a very shallow dose–response curve (the difference between one puff and two puffs may not be clinically detectable) and high between-subject variability, which requires a very large number of subjects to be used in a PD equivalence test. This is particularly true of dose–response for inhaled corticosteroids, as described below. For the nasal spray products, the establishment of dose–response in the clinical endpoint was not required, but because of the more complex nature

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of particle delivery to the lung, FDA considers establishing the dose sensitivity of the local delivery study of inhalation products essential to establishment of equivalence. Equivalence of local delivery for a test orally inhaled product relative to the reference orally inhaled product may be evaluated using a “dose-scale” method. The relative BA is determined in terms of “delivered” dose of the test formulation required to produce a PD response of the same magnitude as exhibited by the reference formulation, and its calculation takes into consideration the within-study dose–response. In the dose-scale method, the PD response (E R ) to varying doses of the reference product is fit to an E max model to determine the function, φR , φR = E 0R +

E max R DoseR . E D50R + DoseR

(8.5)

The relative BA “F” of a dose of the test product relative to that of the reference product can be calculated by applying the inverse of φR to the mean of the response data of the PD response (E T ) of test product: F = φR−1 (E T )/DoseT .

(8.6)

The Office of Generic Drugs (OGD) has previously used this approach for statistical evaluation of PD BE studies in ANDAs for albuterol MDIs. The applications contained studies based on bronchoprovocation model (PD measure: Histamine PC20 ) or the bronchodilatation model (PD Measures: AUEC-FEV1 and FEV1max ). The number of subjects needed to establish BE of a product using the dose-scale method is a function of the slope of the dose–response curve and the variability in the pharmacodynamic response. As the ratio of the variance relative to the slope decreases, fewer subjects are needed to determine the relative BA. BE using a histamine challenge study has been demonstrated for an albuterol inhalation aerosol with 24 subjects (Stewart et al., 2000). The number of subjects required estimating relative BA of an inhaled corticosteroid (ICS) is large; however, estimates suggest that hundreds of subjects would be needed to establish BE for a parallel study design of an ICS. Traditionally, a cross-over study design could not be used for BE studies of ICS due to the long washout period required between treatments. In the literature, an asthma stability model has been suggested as a method for comparing local delivery of ICS using an FEV1 endpoint in a cross-over design. This is estimated to require many fewer subjects to meet the BE requirements (Ahrens et al., 2001). Another challenge to the design of local delivery studies is that several inhalation products contain two active ingredients, a short acting ICS and long acting beta-agonist. To demonstrate BE, local delivery of both components must be equivalent. However, the FEV1 endpoint used in the asthma stability model is affected by both components. Exhaled nitric oxide (eNO) has been suggested as a pharmacodynamic endpoint for ICS (Silkoff et al., 2001), as eNO levels appear to be unaffected by concomitant administration of beta-agonists. This endpoint, combined with an FEV1 endpoint for the beta-agonist, may enable equivalence of both components to be established.

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Conclusions

Evaluation of BE for systemically acting drugs using pharmacokinetics is well established. Unusual cases such as endogenous substances and highly variable drugs sometimes require new study designs and new statistical analysis procedures. The knowledge available about formulation development and formulation performance for oral dosage forms has allowed the FDA to determine that in vitro testing in some cases can provide adequate evidence of BE. Drug companies can now request waivers of in vivo BE studies (biowaivers) for some of their products, greatly reducing the cost of such studies. Opportunities for future expansion of biowaivers have been identified and discussed above. Locally acting drugs are more complex in terms of BA/BE. An appropriate BE method often needs to be established based on a scientific analysis of each drug product. As illustrated in the case studies, all of the following types of studies have been used by FDA to evaluate bioequivalence of locally acting drugs: 1. 2. 3. 4.

Clinical endpoint BE study Pharmacodynamic endpoint BE study Pharmacokinetic BE study In vitro BE study

The development of generic versions of topical dermatological, nasal, and inhalation products can be significantly impacted by the required BE testing. Development of new and more efficient methods for evaluating BE of locally acting drugs could lead to faster development of generic drugs and facilitate formulation and manufacturing changes.

Acknowledgment. The authors would like to thank Dr. Wallace Adams for his valuable suggestions.

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Nightingale, S., and Morrison, J. C. (1987). Generic drugs and the prescribing physician. JAMA 258(9):1200–1204. Patterson, S. D., Zariffa, N. M.-D, Montague, T. H., and Howland, K. (2001). Nontraditional study designs to demonstrate average bioequivalence for highly variable drug products. Eur. J. Clin. Pharmacol. 57:663–670. Polli, J. E., Yu, L. X., Cook, J. A., Amidon, G. L., Borchardt, R. T., Burnside, B. A., Burton, P. S., Chen, M. L., Conner, D. P., Faustino, P. J., Hawi, A. A., Hussain, A. S., Joshi, H. N., Kwei, G., Lee, V. H., Lesko, L. J., Lipper, R. A., Loper, A. E., Nerurkar, S. G., Polli, J. W., Sanvordeker, D. R., Taneja, R., Uppoor, R. S., Vattikonda, C. S., Wilding, I., and Zhang, G. (2004). Summary workshop report: biopharmaceutics classification system – implementation challenges and extension opportunities. J. Pharm. Sci. 93(6):1375–1381. Rinaki, E., Dokoumetzidis, A., Valsami, G., and Macheras, P. (2004). Identification of biowaivers among Class II drugs: theoretical justification and practical examples. Pharm. Res. 21:1567–1572. Schulze, J. D., Waddington, W. A., Eli, P. J., Parsons, G. E., Coffin, M. D., and Basit, A. W. (2003). Concentration-dependent effects of polyethylene glycol 400 on gastrointestinal transit and drug absorption. Pharm. Res. 20(12):1984–1988. Shah, V. P., Yacobi, A., Barr, W. H., Benet, L. Z., Breimer, D., Dobrinska, M. R., Endrenyi, L., Fairweather, W., Gillespie, W., Gonzalez, M. A., Hooper, J., Jackson, A., Lesko, L. J., Midha, K. K., Noonan, P. K., Patnaik, R., and Williams, R. L. (1996). Evaluation of orally administered highly variable drugs and drug formulations. Pharm. Res. 13(11):1590–1594. Silkoff, P. E., McClean, P., Spino, M., Erlich, L., Slutsky, A. S., and Zamel, N. (2001). Dose–response relationship and reproducibility of the fall in exhaled nitric oxide after inhaled beclomethasone dipropionate therapy in asthma patients. Chest 119:1322–1328. Stewart, B. A., Ahrens, R. C., Carrier, S., Frosolono, M., Lux, C., Han, S. H., and Milavetz, G. (2000). Demonstration of in vivo bioequivalence of a generic albuterol metered-dose inhaler to Ventolin. Chest 117(3):714–721. Sun, D., Yu, L. X., Hussain, M. A., Wall, D. A., Smith, R. L., and Amidon, G. L. (2004). In vitro testing of drug absorption for drug “developability” assessment: forming an interface between in vitro preclinical data and clinical outcome. Curr. Opin. Drug Discov. Devel. 7(1):75–85. Telko, M. J., and Hickey, A. J. (2005). Dry powder inhaler formulation. Respir. Care 50:1209–1227. Wade, L. G. (2003). Organic Chemistry, 5th Edition. Pearson Education, Inc., Upper Saddle, NJ. Yazdanian, M., Briggs, K., Jankovsky, C., and Hawi, A. (2004). The “high solubility” definition of the current FDA guidance on biopharmaceutical classification system may be too strict for acidic drugs. Pharm. Res. 21: 293–299. Yu, L. X. (2006). Evolving bioavailability/bioequivalence regulatory standards, strategies in oral drug delivery 2005, Garmisch-Partenkirchen, Germany. Yu, L. X., Ellison, D. C., Conner, D. P., Lesko, L. J., and Hussain, A. S. (2001). Influence of drug release properties of conventional solid dosage forms on the systemic exposure of highly soluble drugs. AAPS Pharm. Sci. 3:E24. Yu, L. X., Amidon, G. L., and Polli, J. E. (2002). Biopharmaceutics classification system: the scientific basis for biowaiver extensions. Pharm. Res. 19:921–925.

9 A Biopharmaceutical Classification System Approach to Dissolution: Mechanisms and Strategies William E. Bowen, Qingxi Wang, W. Peter Wuelfing, Denise L. Thomas, Eric D. Nelson, Yun Mao, Brian Hill, Mark Thompson, Kimberly Gallagher, and Robert A. Reed

9.1

Introduction

Dissolution testing is a common characterization method employed by the pharmaceutical industry to design formulations and assess product quality. It is a required performance test by many regulatory authorities for solid oral dosage forms, transdermal patches, stents, and oral suspensions. Dissolution testing is unique in that it is the only finished product test method in routine use that measures the effect of the formulation and physical properties of the active pharmaceutical ingredient (API) on the in vitro rate of drug solubilization. As a result, dissolution testing is the only test that monitors the impact of environmental storage conditions and manufacturing process upon the rate of drug release from the dosage form. These sensitivities have led to the use of the dissolution test as a measure of formulation bioperformance.

9.2 Biopharmaceutical Classification System Approach to Dissolution The BCS was introduced by Amidon et al. (1995) as a means to predict oral drug absorption based on the contribution of solubility, permeability, and dissolution to oral drug absorption (FDA, 2000; European Agency for the Evaluation of Medicinal Products, 2001). A high solubility drug is defined by the BCS as the highest dose that is soluble in 250 mL or less in aqueous media from pH 1 to 7.5. A compound is considered highly permeable by the BCS if its extent of absorption in humans is 90% or more of an administered dose. Lastly, a drug is considered rapidly dissolving if no less than 85% of label claim is released within 30 min across the aforementioned pH range in either USP dissolution apparatus 290

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I (baskets) at 100 rpm or USP dissolution apparatus II (paddles) at 50 rpm. Drugs are then defined based on these solubility, permeability, and dissolution characteristics to fall within one of four BCS classifications (Fig. 9.1). According to the BCS approach, the rate of drug absorption is determined by the dissolution rate (kd ) and the permeability rate (kp ) (Fig. 9.2). The rate of dissolution is a function of drug solubility and formulation characteristics, while the permeability rate is largely a function of a drug compound’s chemical structure (polarity, functional groups, salt form, etc.). Three distinct scenarios are defined by this BCS absorption model. In the simplest case both kd and kp are large. This results in the drug being rapidly solubilized and well absorbed. When kd is much greater than kp , the drug is rapidly released and solubilized from

F IGURE 9.1. Biopharmaceutical Classification System (BCS) drug classification matrix

Formulated drug

kd

Solubilized drug

kp

Absorbed drug

Where: Scenario 1: both kd & kp are fast - well absorbed Scenario 2: kd >> kp - permeation control Scenario 3: kd 150 um 20 0 0

15

30

45

60

75

Time (minutes) F IGURE 9.11. Dissolution results for Case Study 3 generated using USP Apparatus II (paddles) at 50 rpm in 900 mL of water. Each profile represents release rates from granules that are less than 150 µm or greater than 150 µm in size. The paddle speed was increased to 150 rpm for the final 15 min to measure the full drug content of the granules

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not the rate limiting step to drug release. Dissolution testing is a better monitor for quality control purposes. Clearly, an understanding of the mechanistic aspects of the product performance should guide the decision to define the best means of quality control. The previous case studies are three examples from the authors’ experience with 15 BCS class I/III compounds. The dissolution results of 13 of these compounds showed greater than 85% drug release in 15 min with tablet disintegration as the rate limiting step to dissolution, thus making them amenable to disintegration testing. One drug product did not meet the definition of rapid release, but drug release was still under disintegration control and thus still a candidate for disintegration testing. Only one drug product out of the 15 was found to exhibit a drug dissolution mechanism appropriate for monitoring via traditional dissolution analysis (Case Study 3). It is important to note that even in this instance, drug release could be attenuated through control of the manufacturing process to yield another instance of tablet disintegration controlled dissolution. It should be the goal of formulation development to keep BCS class I/III IR formulations rapidly dissolving if the desire is to simulate dosing of an oral solution. High solubility drug formulated as drug filled/drug blend in capsules also fit into this paradigm as ultimately the rupture of a gelatin capsule will be the rate limiting step to drug release. Application of disintegration during development should be supplemented with dissolution analysis when concerns over form changes, agglomeration (drug in capsule), or adherence to particular excipients exist. In addition, it is worthwhile to consider that the application of the decision tree in Fig. 9.7 to BCS class II/IV drug compounds that are highly soluble at gastric pHs provided a clear understanding of their dissolution mechanism and in vivo absorption are developed (vide infra Case Study 10).

9.5.2

BCS Class II and IV Case Studies

Twenty five BCS class II and IV compounds are present in the BCS matrix shown in Fig. 9.6. The overall dissolution rate (kd ) of these formulations is generally governed by the low solubility of the drug substance and corresponding slow intrinsic drug solubilization (kid). The disintegration of such a dosage form will initially produce a suspension of the API, whose dissolution then depends mainly on the intrinsic solubilization of the drug, its particle size distribution, and the driving force for solubilization created by the absorption of drug in the small intestine. In most cases, the bioavailability of a well-formulated immediate release dosage form of this type will be similar to that of a drug suspension. Liquid filled capsule (LFC) formulations, when the API is dissolved in a liquid vehicle to maximize bioavailability, are an exception to this suspension model as the dissolution of the dosage form is simply the capsule rupture. In addition, some formulations result in an overall dissolution rate that is a combination of both formulation disintegration and intrinsic drug solubilization rates. Thus, the value added by traditional dissolution testing of BCS class II/IV compounds varies depending upon the difference between kid and kdd . This variability is captured in the BCS class II/IV

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BCS Classification II/IV Immediate Release Dosage Form

API solubilized in formulation (LFCs, etc.)

No

Dissolution mechanism characterization Perform dissolution of dosage form, API (different PSD) and/or granules

Kid rate limiting?

No Consider reformulation or process change

Case 4 Yes

Yes

Disintegration or capsule rupture limited?

Case 10 CQA API remains in solution

Yes

CQA Tablet disintegration capsule rupture

Disintegration test or capsule rupture test

No

No

Cases 8-9

Kid rate limiting? Yes

Cases 5-7

CQA Formulation properties

CQA API particle size, etc.

Monitor dissolution profiles

Control API particle size, etc.

F IGURE 9.12. Decision tree for BCS class II and IV formulations

dissolution quality attribute decision tree shown in Fig. 9.12. The following case studies of BCS class II/IV drugs compare formulation data highlighting real situations where critical quality attribute driven drug release testing can be applied. 9.5.2.1 Case Study 4: Liquid Filled (True Solution) Capsules LFCs, while typically formulated for BCS class II/IV drugs, can fall into two general classes: (1) true solution filled and (2) API suspension filled. Drug release in true solution filled products is dominated by capsule rupture as the drug is already solubilized in the formulation. Suspension filled capsules involve capsule rupture and likely a subsequent intrinsic drug solubilization step. In the case of the true solution filled capsules (Fig. 9.12), the formulation design and development is to ensure that the drug remains solubilized such that one is dosing a true solution. The fact that the drug is already in solution heightens the point that rupture is the critical quality attribute for these formulations. 9.5.2.2 Case Studies 5, 6, and 7: Intrinsic Rate of Drug Solubilization Controlled Dissolution A common scenario with low solubility drugs (BCS class II/IV) is an in vitro release profile that is dominated by the intrinsic rate of solubilization. The following three case studies are examples of intrinsic drug solubilization controlled

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dissolution. Case Study 5 is a BCS class II compound with pH independent low aqueous solubility (20 min. The slowed dissolution at the earlier time points for the conventional tablet can be attributed to the longer disintegration time observed for this formulation (Fig. 9.14). Overall, the rapid permeation rate for this drug leads to comparable in vivo extent of absorption for the two formulations with an observed decrease in Cmax and increase in Tmax for the conventional tablet, as suggested by the in vitro results. Hence for the lyophilized tablet formulation, the compound API particle size is the appropriate quality attribute. The intrinsic properties of the drug substance can also control dissolution rate in submicron sized drug containing formulations. Such dosage forms are produced from surfactant and polymer-stabilized colloidal drug dispersions, and are explicitly designed to release submicron sized API upon formulation disintegration to enhance dissolution kinetics and improve bioavailability. Case Study 6 involves a BCS class IV compound with extremely low aqueous solubility across pH 1–7.5. The formulation consists of high intensity wet milled API and stabilizing excipients coated onto microcrystalline cellulose beads and filled into

% Dissolved

100

90

80

Pin milled Jet milled

70

60 0

30

60

90

120

150

Time (minutes) F IGURE 9.13. Dissolution results for Case Study 5 formulated in an orally disintegrating tablet. The jet milled API and the pin milled API had mean particle sizes of 4.8 and 12.0 µm, respectively. Dissolution profiles were generated using USP Apparatus II (paddles) at 100 rpm in 900 mL of 0.6% SDS

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% Dissolved

100

80

60

Tablet Oral Disintegrating Tablet Crushed Tablet

40 0

10

20

30

Time (minutes)

F IGURE 9.14. Dissolution results for Case Study 5 conventional, crushed conventional, and orally disintegrating tablets manufactured from the same API lot. Dissolution profiles were generated using USP Apparatus II (paddles) at 50 rpm in 900 mL of 2.0% SDS

% Dissolved

100

80 Beads Capsules

60

40 0

20

40

60

Time (minutes)

F IGURE 9.15. Dissolution of Case Study 6 capsules and capsule contents, which are coated beads with 355 microns

20 0 0

30

60

90

Time (minutes) F IGURE 9.16. Dissolution results for Case Study 7 capsules as a function of API particle size. Dissolution profiles were generated using USP Apparatus II (paddles) at 100 rpm in 900 mL of 2% Tween 80 media

9. A Biopharmaceutical Classification System Approach to Dissolution

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94

% Dissolved at 60 minutes

93

y = − 0.1728x + 104.46 R2 = 0.8825

92 91 90 89 88 87 86 85

60

65

70

75

80 85 90 API Mean (microns)

95

100

105

F IGURE 9.17. Percentage dissolved in 60 min for Case Study 7 capsules as a function of API D90 value. Dissolution profiles were generated using USP Apparatus II (paddles) at 100 rpm in 900 mL of 2% Tween 80 media 100

% Dissolved

80

60

40 Formulation I Formulation II

20

0 0.0

1.5

3.0

4.5

6.0

Time (hours)

F IGURE 9.18. Dissolution results for Case Study 8 tablet formulations. Dissolution profiles were generated using USP Apparatus II (paddles) at 100 rpm in 900 mL of pH 6.8 buffer

ultimately the overall dissolution rate. The dissolution profiles for two formulation designs of a BCS class IV drug are given in Fig. 9.18. Formulation I is intended to rapidly disintegrate while Formulation II is designed to increase the impact of the formulation on the overall release rate. These same tablet formulations

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1 Formulation I

Fraction Absorbed

0.8

Formulation II

0.6

0.4

0.2

0 0

0.2

0.4

0.6

0.8

1

Fraction Dissolved F IGURE 9.19. Wagner–Nelson deconvolution of human in vivo and in vitro dissolution profiles shown in Fig. 9.18 for Case Study 8 Formulation I and II. Formulation I exhibits more permeation control character and Formulation II exhibits primarily dissolution control character

were also dosed in humans and pharmacokinetic (PK) data collected. As introduced earlier, Wagner–Nelson models were then applied to compare the in vitro and in vivo performance of these two formulations (Fig. 9.19). The results of this analysis suggest that Formulation I is largely exhibiting permeation control. Here dissolution testing provides limited value and a disintegration test would be a more appropriate measure of product performance. However, as might be anticipated, Formulation II exhibits dissolution control behavior in vivo, thus dissolution profiles would add value to characterize the quality of this product. Clearly, a Wagner–Nelson type analysis is a useful tool for identifying the value of the dissolution testing. Case Study 9 is another example of where the combination of the formulation properties and the process parameters can greatly impact drug release profiles. In this case, two drug substances are incorporated in a single capsule formulation. The first API is a BCS class IV compound with low aqueous solubility from pH 3.0–7.5 but high solubility at low pH. The second API is a BCS class II compound with poor solubility across the pH range of 1.0–7.5. The two APIs are mixed with lactose and microcrystalline cellulose and granules of the blend formed through a roller compaction process. The granules are then encapsulated. In exploring the process design space for the roller compaction unit operation, varying the roll pressure had a significant impact on the release profile (note both APIs had similar release rates) (Fig. 9.20). Furthermore, it was demonstrated that the capsule shell

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100

% Dissolved

80 60 60 bar

40

90 bar 120 bar

20 0 0

20

40

60

Time (minutes) F IGURE 9.20. Dissolution results for Case Study 9 capsules as a function of the roll compaction pressure applied ranging from 60 to 120 bar. Dissolution profiles were generated using USP Apparatus II (paddles) at 50 rpm in 900 mL of 2% SDS media

had no impact on the overall release rate and that the drug release from the roller compacted granules is the rate determining step. Hence, neither the capsule disintegration nor the API particle size distribution was contributing to the in vitro dissolution rate. In this case, monitoring the drug release by dissolution testing presents the best measure of product quality. 9.5.2.4 Case Study 10: API with High Solubility at Gastric pHs BCS class II/IV compounds can also have pH dependent solubility, exhibiting high aqueous solubility in gastric pHs (e.g., amines) or intestinal pHs (e.g., many organic acids). The API used in Case Study 10, while defined as BCS Class IV because of its low solubility across the pH range of pH 3.0–7.5 and low permeability, behaves like a BCS Class III drug as it is highly soluble at gastric pH’s (>200 mg/mL). At gastric pHs, the in vitro release profile is rapid (>90% release at 20 min) for both granules and the encapsulated product. Therefore, the drug behaves like a rapidly dissolving BCS class III compound as the formulation mimics dosing an oral solution. Dissolution testing results of the encapsulated product and the unencapsulated granulation show that the drug is rapidly released from the granules and the dissolution profile of the encapsulated material is dominated by the capsule shell rupture time (Fig. 9.21). As is typical of many hard gelatin encapsulated products, the key quality concern is gelatin crosslinking that may ultimately slow the drug dissolution rate. As dissolution measures capsule rupture for this product, disintegration is a suitable surrogate test to monitor the product quality.

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100

% Dissolved

80 60 40 Unstressed capsules Stressed capsules

20

Granules

0 0

10

20

30

Time (minutes) F IGURE 9.21. Dissolution results for Case Study 10 granules, unstressed capsules, and stressed capsules. The slowing observed for stressed capsules is readily attributable to hard gelatin crosslinking. Dissolution profiles were generated using USP Apparatus II (paddles) at 50 rpm in 0.1N HCl

In summary, BCS class II/IV compounds are subject to the same mechanistic dissolution considerations as described for BCS class I/III compounds. It should be the goal of formulation development to keep BCS II/IV IR formulations rapidly disintegrating if the desire is to simulate dosing of an oral suspension. API properties (e.g., particle size) of BCS II/IV compounds are frequently identified as quality attributes and are controlled in lieu of dissolution monitoring, provided a correlation with dissolution is established during development. In the case of LFCs, capsule shell rupture presents a sufficient quality measure as the API is solubilized, and rupture in the gut releases a drug solution. Lastly, for BCS II/IV compounds with high solubility in gastric pH, consideration should be given a simple disintegration test if the product dissolution is demonstrated to be disintegration controlled.

9.5.3

Controlled Release Dosage Form Case Study

Controlled release formulations, often called sustaining release formulations, may include BCS class I–IV drug compounds and are specifically designed such that the formulation “controls” the overall drug release rate. At the extreme of a high solubility and high permeability drug, one can expect that as the drug is released, it is rapidly dissolved and absorbed and an IVIVC can be established. However, at the other extreme, a low solubility and low permeability drug, one can envision a CR product that is still dominated by intrinsic rate of solubilization or drug permeation and the ability to generate an IVIVC is greatly challenged. Controlled release products are often developed to maintain therapeutic activity for an extended time

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or to reduce high plasma concentrations to minimize adverse effects. Therefore, it is desirable to develop an IVIVC so that an in vitro dissolution test can be used to indicate or predict changes which may have an effect on the efficacy or safety of the product. Case Study 11 involves a BCS class I compound with high permeability and high solubility across pH 1–7.5. A CR gel extrusion module (GEM) tablet was developed to reduce the dosing frequency of the drug product. The GEM tablet consists of a core tablet of drug, a water swellable carbomer polymer, sodium phosphate, lactose, microcrystalline cellulose, and magnesium stearate. The core tablet is manufactured by a roller compaction process and coated to provide a rigid, water impermeable cellulose polymer membrane. Holes are then drilled through the coating. The release mechanism involves water ingress through the drilled holes, then polymer hydration followed by extrusion of drug particles present in the core tablets through holes in the coating, and rapid intrinsic solubilization of the drug particles. Subsequently, the Case Study 11 drug is rapidly solubilized and readily absorbed. Formulations with high, medium, and low release rates were generated by varying the number of holes drilled through the outer coating to give corresponding in vitro dissolution profiles (Fig. 9.22). These formulations were also evaluated in a single dose pharmacokinetic (PK) study in humans and their PK behavior compared against that of an immediate release tablet formulation (Fig. 9.23). The mean plasma profiles of the drug were deconvoluted according to the Wagner–Nelson method to generate in vivo fraction absorbed versus time profiles. An IVIVC model was then established by plotting the mean fraction

% In Vitro Dissolution

100 80 60 40 20

Low Release Rate Medium Release Rate High Release Rate

0 0

4

8

12

16

20

Time (hr) F IGURE 9.22. Dissolution results for Case Study 11 in designed controlled release tablets with low, medium, and high release rates. Dissolution profiles were generated using USP Apparatus II (paddles) at 100 rpm in 900 mL of water

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Drug Concentration (ng /mL)

18 16

Immediate Release FCT High Release GEM

14

Medium Release GEM Low Release GEM

12 10 8 6 4 2 0 0

6

12

18

24 Time (hr)

30

36

42

48

F IGURE 9.23. Mean plasma concentration of Case Study 11 drug versus time for an immediate release (IR) formulation and three designed controlled release tablets with low, medium, and high in vitro release rates Fraction of Drug Absorbed to time t

1.0 Low Release Rate Medium Release Rate High Release Rate

0.8

0.6

0.4

0.2

0.0 0

0.2

0.4

0.6

0.8

1.0

Fraction of Drug Dissolved In Vitro to Time t

F IGURE 9.24. Fraction absorbed versus fraction dissolved for Case Study 11 three designed controlled release tablets with low, medium, and high in vitro release rates

absorbed in vivo versus the mean fraction dissolved in vitro (Fig. 9.24). A good linear relationship was obtained with correlation coefficient of 0.98. Evaluation of the predicted error in in vivo parameters (data not shown here) meets FDA criteria for valid Level A IVIVC. In this instance, the number and size of the holes in the

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impermeable membrane coating are an appropriate quality attribute and can be used as a surrogate for dissolution. Clearly, dissolution profiles need to be monitored during the development of controlled release dosage forms. However, once the dissolution mechanism is well understood, the impact of formulation properties on dissolution profiles well characterized and an IVIVC established, the identification of quality attribute alternatives to dissolution may be explored. These quality attributes can then be monitored to ensure product quality in lieu of dissolution. In this instance, the number and size of the holes in the impermeable membrane coating are an appropriate quality attribute and can be used as a surrogate for dissolution.

9.5.4 Pharmaceutical Quality Assessment Implications of Dissolution The aforementioned case studies illustrate the value of understanding the relationship between a drug’s solubility characteristics and its mechanism of dissolution once formulated. This understanding provides a quality attribute framework to assess biorelevant features of formulated drugs. Further, this knowledge can then be employed to guide formulation design and selection and process scale-up and optimization. The overall mechanistic guidance from these laboratories is provided in Table 9.1. As previously noted, formulations where the dissolution rate limiting step is governed by API intrinsic solubilization (kdd kid) are better controlled by monitoring critical quality attributes such as API particle size or surface area as opposed to less specific dissolution testing. Fast disintegrating tablets, capsules, and submicron drug containing formulations are where these characteristics would most frequently be observed. Formulations where the rate of TABLE 9.1. Critical quality attributes controlling dissolution performance as a function of dissolution mechanism Scenarios

Dissolution rate-limiting step

Critical quality attributes and test

Formulation types

kdd kid

API intrinsic solubilization

kdd kid

Formulation disintegration

kdd ∼ kid both fast

Formulation disintegration

kdd ∼ kid both slow

API intrinsic solubilization and formulation disintegration

API particle size, surface area solubility, etc. Disintegration, porosity, hardness, capsule rupture test Disintegration, porosity, hardness, capsule rupture test Dissolution profiles, disintegration, API particle size, surface area solubility, etc.

Fast disintegrating tablets, capsules, nanoformulation IR tablets, LFCs, API or powder blend in capsules IR tablets, LFCs, API or powder blend in capsules Tablets

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dissolution is limited by the rate of disintegration (kdd kid) are best monitored by critical quality attributes such as disintegration, porosity, hardness, or capsule rupture. IR tablets, LFCs, and API or powder blend filled capsules are all formulations subject to disintegration control. In instances where drug intrinsic solubility and disintegration are competitive and both fast, the disintegration of the formulation is the most specific measure of dissolution as drug is solubilized as quickly as it is exposed to the dissolution media. Appropriate critical quality attributes in this case include disintegration, porosity, hardness, or capsule rupture. When drug intrinsic solubility and disintegration are competitive and both slow, the intrinsic rate of drug solubilization and disintegration affect the overall dissolution rate. Appropriate critical quality attributes include dissolution profiles, disintegration, API particle size, and surface area depending upon the specifics of the case in question. Application of the principles discussed herein has the potential to improve product quality and process consistency by increasing the specificity in quality attributes definition and measurement. It is envisioned that this approach to dissolution is in line with recent science-based pharmaceutical development guidance from the ICH. Further, this approach guides the industry away from often meaningless dissolution testing approaches into BCS aligned strategies that are rational and scientifically defendable.

9.6

Conclusion

The BCS provides a science-based framework for the identification of drug dissolution quality attributes and the development of rational methods for ensuring product quality. Experience generated in this laboratory for BCS class I–IV compounds in a variety of formulation types clearly shows that a thorough mechanistic understanding of drug dissolution combined with BCS class information can be employed to determine rational product quality testing strategies. In this context, the application of traditional dissolution testing may not be an appropriate pharmaceutical quality assessment approach as the test often measures a combination of cohesive properties of the formulation and intrinsic properties of the API. As a result, alternative methods may be suitable for measurement of key formulation quality attributes provided that the appropriate studies have been performed to establish a correlation between the quality attribute and the formulation. Application of the concepts discussed herein to pharmaceutical development should be utilized to help guide formulation and process development in an efficient and focused manner.

References Aiche, J. M.; Pierre, N.; Beyssac, E.; Prasad, V. K.; Skelly, J. P. (1989). New Results on an In Vitro Model for the Study of the Influence of Fatty Meals on the Bioavailability of Theophylline Controlled-Release Formulations. J. Pharm. Sci. 78: 261–263.

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Amidon, G. L.; Lennernas, H.; Shah, V. P.; Crison, J. R. (1995). A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of In Vitro Drug Product Dissolution and In Vivo Bioavailability. Pharm. Res. 12: 413–420. Brown, C. K.; Chokshi, H. P.; Nickerson, B.; Reed, R. A.; Rohrs, B. R.; Shah, P. A. (2004). Acceptable Analytical Practices for Dissolution Testing of Poorly Soluble Compounds. Pharm. Technol. 28: 56–65. Buchwald, P. (2003). Direct, Differential-Equation-Based In-Vitro–In-Vivo Correlation (IVIVC) Method. J. Pharm. Pharmacol. 55: 495–504. Caramella, C.; Ferrari, F.; Boneroni, M. C.; Sangalli, M. E.; Debernardi di Valserra, M.; Feletti, F.; Galmozzi, M. R. (1993). In Vitro/In Vivo Correlation of Prolonged Release Dosage Forms Containing Diltiazem HCl. Biopharm. Drug Dispos. 14: 143–160. Civiale, C.; Ritschel, W. A.; Shiu, G. K.; Aiache, J. M.; Beyssac, E. (1991). In Vivo–In Vitro Correlation of Salbutamol Release from a Controlled Release Osmotic Pump Delivery System. Methods Find. Exp. Clin. Pharmacol. 13: 491–498. Devi, K. P.; Rao, K. V. R.; Baveja, S.; Fathi, M.; Roth, M. (1989). Zero-Order Release Formulation of Oxprenolol Hydrochloride with Swelling and Erosion Control. Pharm. Res. 6: 313–317. Dhopeshwarker, V.; O’Keefe, J. C.; Zatz, J. L.; Deeter, R.; Horton, M. (1994). Development of an Oral Sustained-Release Antibiotic Matrix Tablet Using In-Vitro/In-Vivo Correlations. Drug Dev. Ind. Pharm. 20: 1851–1867. Dietrich, R.; Brausse, R.; Benedikt, G.; Steinijans, V. W. (1988). Feasibility of In Vitro/In Vivo Correlation in the Case of a New Sustained-Release Theophylline Pellet Formulation. Arzneimittelforschung/Drug Res. 38: 1229–1237. Dunne, A.; O’Hara, T.; Devane, J. (1977). Level A In-Vivo–In-Vitro Correlation: Nonlinear Models and Statistical Methodology. J. Pharm. Sci. 86(11): 1245–1249. European Agency for the Evaluation of Medicinal Products. (2001). Note for Guidance on the Investigation of Bioavailability and Bioequivalence. (CPMP/EWP/QWP/1401/98). Fassihi, R. A.; Ritschel, W. A. (1993). Multiple-Layer, Direct-Compression, ControlledRelease System: In Vitro and In Vivo Evaluation. J. Pharm. Sci. 82: 750–754. FDA. (1997a). Guidance for Industry: Dissolution Testing of Immediate Release Solid Oral Dosage Forms. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Rockville, MD. FDA. (1997b). Guidance for Industry: Extended Release Oral Dosage Forms: Development, Evaluation, and Application of In Vitro/In Vivo Correlation. (1997b). U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Rockville, MD. FDA. (2000). Guidance for Industry: Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage Forms Based on a Biopharmaceutics Classification System. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Rockville, MD. Fed. Regist. (2005). 70: 6888–6889. Harrison, L. I.; Mitra, A. K.; Kehe, C. R.; Klinger, N. M.; Wick, K. A.; McCarville, S. E.; Cooper, K. M.; Chang, S. F.; Roddy, P. J.; Berge, S. M.; Kisicki, J. C.; Dockhorn, R. (1993). Kinetics of Absorption of New Once-a-Day Formulation of Theophylline in the Presence and Absence of Food. J. Pharm. Sci. 82: 644–648. Humbert, H.; Cabiac, M.-D.; Bosshardt, H. (1994). In Vitro-In Vivo Correlation of a Modified-Release Oral Form of Ketotifen: In Vitro Dissolution Rate Specification. J. Pharm. Sci. 83: 131–136.

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International Conference on Harmonization. (1999). Specifications: Test Procedures and Acceptance Criteria for New Drug Substances and New Drug Products: Chemical Substances, Q6A. International Conference on Harmonization. (2004). Pharmaceutical Development, Q8. Johnson, K. C.; Swindell, A. C. (1996). Guidance in the Setting of Drug Particle Size Specification to Minimize Variability in Absorption. Pharm. Res. 13: 1795–1798. Hussein, Z.; Friedman, M. (1990). Release and Absorption Characteristics of Novel Theophylline Sustained-Release Formulations: In Vitro–In Vivo Correlation. Pharm. Res. 7: 1167–1171. Llabres, M.; Farina, J. B. (1989). Gastrointestinal Bioavailability Assessment of Commercially Prepared Sustained-Release Lithium Tablets Using a Deconvolution Technique. Drug Dev. Ind. Pharm. 15: 1827–1841. Mojaverian, P.; Radwanski, E.; Lin, C.; Cho, P.; Vadino, W. A.; Rosen J. M. (1992). Correlation of In Vitro Release Rate and In Vivo Absorption Characteristics of Four Chlorpheniramine Maleate Extended-Release Formulations. Pharm. Res. 9: 450–456. Polli, J. E.; Crison, J. R.; Amidon, G. L. (1996). Novel Approach to the Analysis of In-Vitro–In-Vivo Relationships. J. Pharm. Sci. 85: 753–760. Wagner, J. G. (1975a). Application of the Loo-Reigelaman Absorption Method. J. Pharmacokinet. Biopharm. 3: 51–57. Wagner, J. G. (1975b). Fundamentals of Clinical Pharmacokinetics, 1st Edition. Drug Intelligence Publications: Hamilton, IL.

10 Food Effects on Drug Bioavailability: Implications for New and Generic Drug Development Barbara Myers Davit and Dale P. Conner

10.1 10.1.1

Introduction Objectives

The US FDA’s mission is to ensure that safe and effective drugs are approved for use by American consumers. Among the many factors that the FDA examines to ensure that drug products are safe and effective is the effect of food on the bioavailability of the active drug from the drug product. Thus, the FDA asks applicants seeking marketing approval of new or generic drug products to conduct human pharmacokinetic studies examining the effects of food on drug absorption and bioavailability. The objectives of this chapter are to (1) describe several mechanisms by which food affects drug bioavailability; (2) present examples from FDA-approved drug product labeling to show how food effects are considered in optimizing therapy; (3) distinguish between food effects on drug substance versus drug product; and (4) explain the role of food-effect studies in new and generic drug development.

10.1.2

Oral Bioavailability Defined

Oral bioavailability of drugs is determined by the administered dose that is absorbed from the gastrointestinal (GI) tract. Bioavailability is defined in the FDA’s regulations as “the rate and extent to which the active ingredient or active moiety is absorbed from a drug product and becomes available at the site of action” (21 CFR Sect. 320.21(a)).

10.1.3

How Food Can Affect Drug Bioavailability

Food can affect drug bioavailability either by interacting directly with drug substance or indirectly by altering drug release and subsequent absorption from the drug product. The first part of this chapter will discuss various types of food effects

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on the drug substance, emphasizing the study of food effects in the context of new drug evaluation. Examples will be used to illustrate each type of food effect. Each example will be followed by a brief discussion of how the food effect should be considered in optimizing the treatment regimen. The FDA-approved labels will be used to illustrate this concept. The latter part of this chapter will discuss food effects on drug product, with an emphasis on the role of food-effect studies in generic drug product approval.

10.2

Food Interactions with Drug Substance

10.2.1 Pharmacokinetic Parameters Used to Characterize Food Effects on Drug Bioavailability Food interactions with drug substance can be manifested by the following effects on oral bioavailability: prolonged rate of absorption, decreased absorption, increased absorption, and unaffected absorption. Food effects on rate of drug absorption are reflected as changes in peak plasma concentrations (Cmax ) and time to reach peak plasma concentrations (Tmax ). Food effects on extent of drug absorption are reflected as changes in the area under the drug plasma concentration versus time curve (AUC0−t and AUC∞ ). Therefore, in characterizing food effects for regulatory purposes, applicants generally conduct human pharmacokinetic studies in which the investigator administers the drug to healthy subjects1 with food and determines any changes in Cmax , Tmax , and AUC.

10.2.2 Prolonged Rate of Drug Absorption in the Presence of Food A prolonged or decreased rate of absorption in the presence of food is generally observed for drugs that show high solubility, high GI permeability, and rapid oral absorption (Fleisher et al., 1999; Martinez and Amidon, 2002; Wu and Benet, 2005). The major mechanism responsible for delayed drug absorption in the fed state is delayed gastric emptying rate, thought to occur by a feedback mechanism from receptors in the proximal small intestine (Singh and Malhotra, 2004). Thus, the presence of food in the stomach delays the onset of the intestinal absorption process, which is manifested by a decrease in Cmax and prolongation of Tmax . Generally, there will be no effect on AUC unless the drug is unstable in the GI tract. Two such examples are the antiretroviral nucleoside analogs lamivudine and zidovudine (Moore et al., 1999). For both drugs, food slows the rate of absorption, delaying the Tmax , and reducing the Cmax . The extent of absorption of both 1 In some cases, for reasons of safety, it may be appropriate to evaluate food effects on drug

pharmacokinetics in patients rather than healthy subjects.

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lamivudine and zidovudine is not altered by administration with food. In these cases, the food effects on Tmax and Cmax are not considered clinically important, and the FDA-approved labels for lamivudine and zidovudine drug products briefly describe the food effects and states that these products may be administered with R c label, 2006a). or without food (GlaxoSmithKline, Combivir

10.2.3

Decreased Drug Absorption in the Presence of Food

10.2.3.1 Overview In decreased drug absorption, the extent of absorption (AUC) is significantly decreased. The decrease in AUC is also reflected in a decrease in Cmax . Three main mechanisms underlying decreased absorption are instability in gastric acids, physical/chemical binding with food, and increased first-pass metabolism/clearance (Welling, 1996; Fleisher et al., 1999; Singh and Malhotra, 2004). 10.2.3.2 Instability in Gastric Acids One mechanism underlying decreased absorption is instability in gastric acids. Prolonged gastric residence time in the fed state can accelerate the rate of hydrolysis for acid-labile drugs. The antiretroviral nucleoside analog didanosine is such an example. Due to its acid lability, didanosine is formulated as buffered tablets and enteric coated tablets, although these formulations do not provide complete protection from these food effects. When the buffered tablet was given 2 h after a meal, the didanosine Cmax and AUC both were decreased by approximately 55% R compared to administration in the fasting state (Bristol-Myers Squibb, Videx label, 2006a). When the enteric coated tablet was given with food, Cmax and AUC decreased by approximately 46% and 19%, respectively, when compared to R label, 2006b). administration in the fasting state (Bristol-Myers Squibb, Videx Consequently, the FDA-approved labels for these products state that they should be taken on an empty stomach. 10.2.3.3 Physical or Chemical Binding with Food Components A second mechanism causing decreased drug absorption is physical or chemical binding of drug with food components. One example is ciprofloxacin, a broad spectrum quinolone antimicrobial agent. Multivalent cations, such as aluminum, iron, magnesium, and calcium, chelate with ciprofloxacin in the GI tract, resulting in reduced bioavailability (Lomaestro and Bailie, 1995). Neuhofel et al. (2002) showed that both ciprofloxacin AUC and Cmax were reduced by about 21% and 22%, respectively, by calcium-fortified orange juice compared with nonfortified orange juice. This reduced bioavailability can result in loss of antibacterial efficacy (Fleisher et al., 1999). As a result, the FDA-approved label cautions that ciprofloxacin should not be taken with dairy products (like milk or yogurt) or calcium-fortified juices since ciprofloxacin absorption may be significantly

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R c reduced (Bayer, Cipro label, 2005a,b). However, the label also states that ciprofloxacin may be taken without regard to meals, and may be taken with a meal that contains dairy products or calcium-fortified juices.2

10.2.3.4 Increased First-Pass Metabolism and Clearance A third mechanism causing decreased drug absorption is increased first-pass metabolism and clearance. For example, repeated intake of specific food constituents, such as cruciferous vegetables and charcoal-broiled meats, both of which induce cytochrome P450 1A2 (CYP1A2) can enhance first-pass metabolism and clearance of certain drugs, such as clozapine, imipramine, and theophylline, by inducing drug-metabolizing enzymes (Martinez and Amidon, 2002; Harris et al., 2003). In general, the effects of these diet–drug interactions appear to be small. Because diets are so diverse and complex, it can be very difficult to assess the frequency and clinical relevance of these interactions (Harris et al., 2003). However, of note is the fact that the FDA-approved label for the narrow therapeutic index drug theophylline (used to treat chronic asthma) states that higher doses of theophylline may be required to achieve the desired effect when it is coadministered with St John’s Wort, a dietary supplement which is a potent inducer R c label, 2004). The of drug metabolizing enzymes (Purdue Pharma, Uniphyl theophylline label also cautions against abruptly stopping St John’s Wort as this may result in theophylline toxicity.

10.2.4

Increased Drug Absorption in the Presence of Food

10.2.4.1 Inhibition of First-Pass Effect In increased drug absorption, the AUC is significantly increased and is usually associated with an increased Cmax . One possible mechanism underlying increased drug absorption is the inhibition of the first-pass effect. One of the best-known such food–drug interactions is with grapefruit juice, which is recognized to enhance bioavailability of a number of drugs by inhibiting in vivo both the efflux transporter P-glycoprotein (a multidrug resistance protein) and CYP3A (Martinez and Amidon, 2002; Deferme and Augustijns, 2003; Harris et al., 2003). Such interactions appear to be more marked and prevalent for drugs that undergo intestinal metabolism (Maka and Murphy, 2000; Singh and Malhotra, 2004). Grapefruit juice markedly increases concentrations of some orally administered medications by as much as 300% (Harris et al., 2004). Sponsors developing drugs determined to be CYP3A and/or P-glycoprotein substrates may conduct studies investigating effects of grapefruit juice on pharmacokinetics; the results of these 2 According to the FDA-approved label for ciprofloxacin extended-release tablets (Bayer,

R R Cipro XR label, 2005), ciprofloxacin bioavailability is not affected when coadministered with the FDA standard high-fat meal, which contains dairy products (FDA Guidance for Industry, Food-Effect Bioavailability and Fed Bioequivalence Studies, 2003).

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studies are usually summarized in the FDA-approved label. The nature of the labeling comments depends on the magnitude of the drug–grapefruit interaction and the potential effects of increased drug systemic exposure on safety or efficacy. The FDA-approved label for the immunosuppressant cyclosporine,3 for example, states that patients on cyclosporine should avoid grapefruit juice and grapefruit R c label, 2005). On the other hand, the FDA-approved label for (Novartis, Neoral the calcium channel blocker felodipine4 notes that the bioavailability of felodipine is increased approximately twofold when taken with grapefruit juice, but provides no specific recommendations about grapefruit juice consumption and felodipine dosing other than a statement that caution should be used when CYP3A4 inhibitors R 5 c label, 2003). are administered with felodipine (AstraZeneca, Plendil Intake of a high-protein meal has been shown to increase the bioavailability of highly extracted drugs such as propranolol when compared to a high-carbohydrate meal or administration in the fasted state. Various mechanisms have been proposed for this effect, including increase in splanchic (hepatic) blood flow (McLean et al., 1978); metabolic inhibition by amino acids (Semple and Fangming, 1995); and saturation of hepatic first-pass metabolism due to increased GI absorption (Tam, 1993). The FDA-approved propranolol label notes a food effect on propranolol bioavailability and advises patients to take the drug product (at bedtime) consisR XL label, tently on either an empty stomach or with food (Reliant, InnoPran c 2005). 10.2.4.2 Physicochemical and Physiological Effects For many lipophilic drugs, co administration with a high-fat meal will significantly enhance the drug’s GI absorption and oral bioavailability (Welling, 1996; Fleischer et al., 1999; Singh and Malhotra, 2004). A classic example is isotretinoin, a retinoid used to treat severe recalcitrant nodular acne (Colburn et al., 1983). Food increases peak and total isotretinoin plasma exposure by over 200%, and because food improves drug bioavailability, the isotretinoin label stipulates that the drug R product be given with food (Roche, Accutane label, 2005). Such food effects can be physicochemical or physiological. Physicochemical effects would include increased drug dissolution and solubility in the components of the meal (Fleischer et al., 1999; Singh and Malhotra, 2004). Higher lipid solubility in the presence of a fatty meal may therefore lead to improved partitioning of the drug into the intestinal membrane. This is thought to be one of the mechanisms contributing to the increased absorption of the lipophilic antiparasitic drug atovaquone (Rolan 3 Cyclosporine is a substrate of both CYP3A and P-glycoprotein (Soldner et al., 1999). 4 Felodipine is a substrate of CYP3A only (Soldner et al., 1999).

R 5 The Plendil label, in the PRECAUTIONS section, states that increases in felodipine

concentrations resulting from coadministration with CYP3A4 inhibitors [such as grapefruit juice] may lead to increased effects such as lower blood pressure and increased heart rate R c (AstraZeneca, Plendil label, 2003).

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et al., 1992). Dietary fat taken with atovaquone increases the rate and extent of absorption, increasing AUC 2–3 times and Cmax five times over fasting. The R , an atovaquone-containing combination drug FDA-approved label for Malarone product, states that the drug product must be taken with food or a milky drink R c (GlaxoSmithKline, Malarone label, 2006b). 10.2.4.3 Effects of Bile Release Physiological mechanisms in response to a fatty meal mainly include increased bile output, enhanced lymphatic effect, and increased gastric residence time due to the fact that a fatty meal delays gastric emptying (Fleischer et al., 1999; Singh and Malhotra, 2004). Increased bile output can lead to an increase in bile salts in the GI tract, which in turn can enhance the dissolution rate of low-solubility drugs by micellar solubilization. Increased transport of drugs via the lymphatic system could thus minimize first-pass metabolism through the GI tract and/or liver. It is thought that bile release is at least partially responsible for increased absorption of the cefalosporin antibiotic cefuroxime in the presence of food (Williams and Harding, 1984). The FDA-approved label for cefuroxime axetil notes that the absolute bioavailability of cefuroxime increases from 37 to 52% when the tablet R c label, 2003). As formulation is taken with a meal (GlaxoSmithKline, Ceftin this increase is relatively modest, the label states that cefuroxime axetil can be administered without regard to meals. 10.2.4.4 Effects of Longer Gastric Residence Time The longer gastric residence time of drug particles may substantially increase the extent of drug dissolution of poorly soluble drugs, which exhibit higher solubility in the lower gastric pH (Fleischer et al., 1999; Singh and Malhotra, 2004). Fluids ingested along with the meal as well as food-induced biliary and pancreatic secretion could contribute to a postprandial increase in bioavailability of such poorly water-soluble drugs. The bioavailability of the antibacterial drug nitrofurantoin is increased in the presence of food because of delayed gastric emptying resulting in increased dissolution, and therefore, absorption (Maka and Murphy, 2000). The FDA-approved nitrofurantoin label states that the drug product should R be given with food to improve drug absorption (Procter & Gamble, Macrobid label, 2003).

10.2.5

Drug Absorption Unaffected by Food

Finally, for many drugs, there is no significant change in the rate or extent of absorption. Drugs that are not affected by food are (a) those that are relatively insensitive to food-induced changes in the GI tract; (b) those that are rapidly and completely absorbed from the GI tract, e.g., finasteride (an inhibitor of Type II 5α-reductase indicated for the treatment of male pattern baldness); and (c) those that are well-absorbed from both the large and small intestine, e.g., bicalutamide

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(a nonsteroidal antiandrogen used in the treatment of prostate cancer) (Singh and Malhotra, 2004). The FDA recommends that, when such absence of a food effect is documented, the product label should state that a food effect is not present (Marroum and Gobburu, 2002). Accordingly, the FDA-approved labels for both products provide this information. The finasteride tablet label states that finasteride bioavailability is not affected by food, and that the drug may be administered R c label, 1997). The bicalutamide tablet with or without meals (Merck, Propecia label states that coadministration with food has no clinically significant effect on rate or extent of absorption, and that the drug may be taken with or without food R label, 2005a). (AstraZeneca, Casodex

10.2.6 FDA Guidance for Industry on Characterizing Food Effects in Drug Development 10.2.6.1 Objectives In practice, it is difficult to determine the exact mechanism by which food changes the bioavailability of a drug product without performing specific mechanistic studies. For this reason, the food-effect bioavailability studies conducted during new drug development are generally exploratory and descriptive, and where appropriate, are used to make labeling claims for the drug product. FDA’s Guidance for Industry entitled Food-Effect Bioavailability and Fed Bioequivalence Studies (2003) gives information to sponsors and/or applicants about food-effect bioavailability studies for orally administered drug products. The guidance provides recommendations for when food-effect studies are appropriate, as well as recommendations on study design, data analysis, and product labeling. 10.2.6.2 Recommended Designs for Food-Effect Bioavailability Studies In the Food-Effect Bioavailability and Fed Bioequivalence Studies Guidance (2003), the FDA asks sponsors to conduct food-effect bioavailability studies for all new chemical entities as early in drug development as possible, i.e., during the investigational new drug (IND) phase of development. The FDA recommends a randomized, balanced, single-dose, two treatment (fed versus fasting), two period, two sequence crossover design for studying food effects on drug bioavailability. The formulation should be tested on an empty stomach (fasting conditions) in one period and following a test meal (fed conditions) in the other period. The FDA also recommends use of high calorie and high-fat meals in these studies. 10.2.6.3 Recommendations for Drug Product Labeling Regulatory Guidance The FDA asks investigators to compare the following pharmacokinetic parameters in these studies: the area under the plasma concentration versus time curve (AUC∞ , AUC0−t ); peak exposure (Cmax ); time to peak exposure (Tmax ); lag-time

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(Tlag ) for modified-release products, if present; termination elimination half-life (T1/2 ); and any other relevant parameters. The effect of food on the absorption and bioavailability of the drug should be described in the Clinical Pharmacology section of the drug product labeling. The Dosage and Administration section of the labeling should provide instructions for drug administration in relation to food based on clinical relevance. For example, the label should state whether changes in systemic exposure caused by coadministration with food results in safety or efficacy concerns, or when there is no important change in systemic exposure but there is a possibility that the drug substance causes GI irritation when taken without food. Labeling Examples How FDA-approved labels deal with effects of food on drug efficacy has been illustrated with several of the examples presented above. How the FDA-approved label deals with food effects on drug safety is well-illustrated by the label for the antiretroviral nonnucleoside reverse transcriptase inhibitor efavirenz. The efavirenz label states that a high-fat/high-calorie meal or a reduced-fat, normal caloric meal given with the efavirenz capsule was associated with a mean increase of 22 and 17% in the mean AUC∞ and a mean increase of 39 and 51% in efavirenz R Cmax , respectively (Bristol-Myers Squibb, Sustiva label, 2005). Administration of the efavirenz tablet with a high-fat/high-caloric meal was associated with a 28% in the mean AUC and a 79% increase in the mean Cmax . Consequently, the label recommends that efavirenz be taken on an empty stomach. The respective labels for the antiprotozoal agents tinidazole and mefloquine illustrate how FDA-approved labels deal with food effects and GI irritation. The tinidazole label states that food has a minimal effect on Cmax and Tmax and no R label, 2006). However, the label effect on AUC and T1/2 (Mission, Tindamax recommends that tinidazole taken with food minimizes the incidence of epigastric discomfort and other GI side effects. Similarly, the label for mefloquine cauR tions that the drug should not be taken on an empty stomach (Roche, Lariam label, 2004).

10.3 10.3.1

Food Interactions with Drug Product Introduction

Food effects on absorption of the drug substance should be distinguished from food effects on drug absorption arising via interactions with the formulation. For example, many excipients used to get extended-release behavior depend on the environmental conditions of the GI tract to provide the extended delivery of the drug (Gai et al., 1997). Thus, by producing physiological changes in the GI tract, such as changes in gastric pH, gastric emptying rate, intestinal motility, and

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intestinal secretions, food can interact with components of the formulation and therefore play an important role in drug bioavailability.

10.3.2 Issues with Modified-Release Drug Products: Potential for Dose-Dumping One way in which food can interact with formulation components is by causing an extended-release formulation, meant to release drug slowly over a prolonged period, to “dose-dump.” Dose-dumping is a term that describes the rapid release of the active ingredient from the dosage form into the GI tract (FDA, 2005). Food effects on dose-dumping were first observed for extended-release formulations of theophylline. One of the most dramatic interactions with food occurred with some extended-release theophylline formulations. Shortly after the approval of R by the FDA,6 Hendeles et al. the theophylline extended-release product Theo-24 (1985) showed that, when the product was given to human subjects together with a high-fat meal, the theophylline Cmax was over twice as high as when given to fasted subjects. Karim et al. (1985) reported similar findings when the extendedR release theophylline tablet product Uniphyl was given to healthy male subjects with a high-fat breakfast. Gai et al. (1997) compared the effects of fasting versus “normal,” high-fat, and high-fat/high-protein meals on theophylline rate (Cmax ) and extent (AUC) of absorption from two different extended-release tablet formulations, one based on a Carbopol 974P hydrophilic matrix, the other based on Cutina HR hydrogenated castor oil lipid matrix. Compared with fasting, any class of meal given with the hydrophilic matrix tablet produced a higher theophylline Cmax but not AUC. By contrast, when given with the lipid matrix tablet, the high-fat and highfat/high/protein meals increased both the theophylline AUC and Cmax , whereas AUC and Cmax following a normal meal were comparable to values in fasting subjects. The authors suggested that, for the hydrophilic matrix tablet, food increased the rate but not extent of theophylline absorption due to the delay in gastric emptying, whereas, for the lipid matrix tablet, the surface-active effect of bile salts together with erosion promoted by lipase action were responsible for the increases in both the rate and extent of theophylline absorption. Su et al. (2003) found that coadministration of a standard Chinese breakfast (rich in carbohydrate) with the theophylline porous matrix extended-release tablet R increased the rate but not extent of theophylline product Euphyllin Retard absorption. Su also found that within-subject variability in Cmax was greater in fed subjects than in fasted subjects. The authors suggested that the delay in gastric emptying was responsible for the increase in Cmax . R 6 Theo-24 was approved by the FDA in 1983 (Orange Book, 2006). At that time, the FDA

asked applicants developing theophylline sustained-release drug products to characterize pharmacokinetics in fasting subjects (Weinberger, 2004).

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Because theophylline is a narrow therapeutic index drug,7 the rapid absorption of relatively large amounts of the drug can cause serious toxicity. The findings from the above studies underscore how important it is for dosage forms to release drug predictably regardless of whether taken with food or on an empty stomach.

10.3.3 Issues with Modified-Release Drug Products: Formulation-Dependant Food Effects 10.3.3.1 In Vitro Drug Release Predictive of Food Effects Schug et al. (2002a) observed formulation-dependent food effects on relative bioavailability from two extended-release nifedipine (a calcium channel blocker) products approved for marketing in the European Union. The two products were R XL, a monolithic modified-release tablet with an erosive polymer Slofedipine R matrix, and Adalat OROS formulated with the osmotically driven Gastrointestinal Therapeutic System (GITS) (Grundy and Foster, 1996). Nifedipine bioavailability from the two formulations was comparable in fasted subjects. However, food caused a pronounced delay in nifedipine absorption from Slofedipine XL but not from Adalat. As a result, the relative bioavailability of nifedipine from Slofedipine XL was only 28% that of Adalat OROS over the intended dosing interval of 24 h. The two formulations had different dissolution properties in vitro. Drug release from Slofedipine XL was minimal at low pH and more rapid at pH 6.8, suggesting that retention of the relatively acid resistant Slofedipine XL formulation in the acidic medium of the stomach in the presence of food led to reduced in vivo bioavailability over the 24-h dosing interval. By contrast, drug release from Adalat OROS was more rapid in acidic media, suggesting that dissolved nifedipine could still leave the stomach even if coadministration with food prolonged capsule retention by the stomach. 10.3.3.2 In Vitro Drug Release Profiles Not Predictive of Food Effects An entirely different food effect on another nifedipine bioavailability was observed R . with a third extended-release formulation marketed in Europe, Nifedicron Nifedicron consists of a capsule containing several mini tablets. Food had a pronounced effect on nifedipine bioavailability from Nifedicron, compared with fasted conditions, resulting in a pronounced increase in Cmax values. The resulting point estimate for Cmax (fed/fasted) was 340%. Food also increased the mean AUC0–24 by 57% compared with the mean values in fasted subjects. In the same study, the investigators compared nifedipine bioavailability from Adalat OROS in fasted and fed subjects and found no effect on AUC0–24h and a 21% increase in 7 In its Guidance for Industry, Bioavailability and Bioequivalence Studies of Orally Admin-

istered Drug Products – General Considerations (2003), the FDA defines a narrow therapeutic index or narrow therapeutic range drugs as “drug products containing certain drug substances subject to therapeutic drug concentration or pharmacodynamic monitoring, and/or where product labeling indicates a narrow therapeutic range designation.”

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Cmax . Both products were robust toward differing pH values of the media during in vitro drug release testing. Thus, the observed potential for dose-dumping in the presence of food was not anticipated.

10.3.4 Implications for Development of Generic Modified-Release Drug Products 10.3.4.1 Introduction The studies summarized above illustrate two problems related to formulationdependent food interactions with modified-release8 dosage forms; first, dosedumping interactions between food and extended-release formulations can sometimes result in serious adverse events; and second, formulation-dependent food interactions are not always predictable from the in vitro characteristics of the dosage form. Because the result of the interaction between the food and the system used to control the liberation of the drug is difficult to predict, it becomes essential to test possible interactions with each new formulation. These potential formulation-dependent food interactions have implications for the generic drug industry. Under the FDA’s regulations, inactive ingredients in a generic solid oral dosage form drug product can differ from the inactive ingredients used in the corresponding innovator drug product (or reference drug product) (21 CFR Sect. 320.1(c)). In addition, generic modified-release products may be formulated with a different release mechanism than their corresponding reference products (Pfizer v. Shalala, 1998). Thus, because (1) each generic modifiedrelease drug product can have a different formulation and release mechanism than its corresponding reference drug product; and (2) the relative direction and magnitude of food effects on modified-release formulations may be difficult to predict, the FDA asks generic drug applicants to conduct studies comparing the relative bioavailability of the generic and corresponding reference drug products under fed conditions. Such studies are called fed bioequivalence studies. 10.3.4.2 Role of In Vivo Fed Bioequivalence Studies In its regulations (21 CFR Sect. 320.1(e)), the FDA defines bioequivalence as The absence of a significant difference in the rate and extent to which the active ingredient or active moiety in pharmaceutical equivalents or pharmaceutical alternatives becomes available at the site of drug action when administered at the same molar dose under similar conditions in an appropriately designed study.

Each new generic drug product must demonstrate bioequivalence to its corresponding reference drug product as a condition of approval.9 Where the focus 8 Modified-release products include delayed-release products and extended- (controlled-)

release products (FDA Guidance for Industry, Bioavailability and Bioequivalence Studies of Orally Administered Drug Products – General Considerations, 2003). 9 The FDA can waive in vivo bioequivalence study requirements for those generic drug products that meet the criteria of 21 CFR §320.22.

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is on release of the drug substance from the drug product into the systemic circulation, the FDA asks applicants submitting Abbreviated New Drug Applications (ANDAs) to determine bioequivalence in an in vivo human study with pharmacokinetic endpoints in which drug rate and extent of absorption is determined. The study should be conducted between the generic product and corresponding reference drug product using the strength listed in the FDA Approved Drug Products with Therapeutic Equivalence Evaluations (2006) or “Orange Book.” In its Guidance for Industry, Bioavailability and Bioequivalence Studies for Orally Administered Drug Products: General Considerations (2003), the FDA recommends that the parameters AUC0−t , AUC∞ , and Cmax be used as measures for bioequivalence. The FDA concludes that the generic product and corresponding reference product are bioequivalent when the 90% confidence interval for the ratio of population geometric means – based on ln-transformed data – is contained within the bioequivalence limits for AUC and Cmax . Bioequivalence studies with pharmacokinetic endpoints are always conducted in healthy normal human subjects under fasting conditions unless precluded for reasons of safety. For the reasons described above, for new generic modifiedrelease drug products, the FDA also asks applicants to conduct fed bioequivalence studies. Thus, the FDA asks that each ANDA for a systemically available solid oral modified-release dosage form be supported by an acceptable fasting and fed bioequivalence study.10 By approving a new generic drug product, the FDA is determining that the generic is therapeutically equivalent to the corresponding reference drug product. Thus, if the new generic modified-release drug product and corresponding reference product are bioequivalent under both fasted and fed conditions, the two will produce the same therapeutic response whether or not taken with food, and the two can be switched under similar conditions of use.

10.3.5 Implications for Development of Generic Immediate-Release Drug Products 10.3.5.1 BCS Class I Drugs The FDA considers a different set of criteria in deciding whether to ask for fed bioequivalence studies (in addition to fasting bioequivalence studies) of new generic immediate release drug products.11 The FDA Guidance for Industry FoodEffect Bioavailability and Fed Bioequivalence Studies (2003) notes that important 10 These studies are generally conducted on the highest strength of modified-release tablet

or capsule of the product line. The FDA may request additional fasting studies on lower strengths of a modified-release tablet product line if it makes a determination that one or more of the lower strengths are not proportionally similar to the strength for which bioequivalence was determined. 11 The FDA requests applicants submitting ANDAs to conduct bioequivalence studies in fasted subjects for all immediate-release drug products, unless safety precludes administration on an empty stomach. For example, for drugs such as mefloquine, which should be taken with food for safety reasons, the FDA asks generic drug applicants to conduct only fed bioequivalence studies.

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food effects on bioavailability are least likely to occur with many rapidly dissolving, immediate-release drug products containing highly soluble and highly permeable drug substances (Biopharmaceutics Classification System Class I, or BCS Class I drug substances). Because BCS Class I drugs and drug products are rapidly dissolving across a wide pH range, formulation effects on absorption are minimized (FDA Advisory Committee for Pharmaceutical Sciences, 2002). However, for some BCS Class I drugs, food can influence bioavailability when there is a high presystemic metabolism effect, extensive absorption, complexation, or instability of the drug substance in the GI tract. In some cases, excipients or interactions between excipients and food-induced changes in gut physiology can contribute to these food effects and influence drug bioavailability. However, results of fed bioequivalence studies of representative BCS Class I drug products have showed that food had no effect on the drug bioavailability from different formulations of the same BCS Class I drug (FDA Advisory Committee for Pharmaceutical Science, 2002). Thus, if the FDA determines that a particular drug substance is in BCS Class I based on satisfaction of the criteria detailed in the FDA Guidance for Industry Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage Forms Based on a Biopharmaceutics Classification System (2000), the applicant submitting an ANDA can request a waiver of both fasting and fed bioequivalence studies. 10.3.5.2 Label-Driven Criteria for Requesting Fed Bioequivalence Studies Regulatory Guidance For other immediate-release drug products (BCS Class II, III, and IV),12 food effects can result from a complex combination of factors that influence the in vitro dissolution of the drug product and/or the absorption of the drug substance. As with modified-release drug products, the FDA focuses on whether a new generic immediate-release drug product will be bioequivalent to the corresponding reference drug product under conditions of use. As a result, the question of whether a fed bioequivalence study should be conducted or not is driven by the drug product label. Thus, the FDA asks applicants submitting ANDAs for immediaterelease drug products to conduct a fed bioequivalence study in addition to the fasted study, unless one of three exceptions is met. As stated in the FDA Guidance for Industry, Food-Effect Bioavailability and Fed Bioequivalence Studies (2003), these exceptions are as follows: (1) the drug is in BCS Class I; (2) the Dosage and Administration section of the FDA-approved reference drug product label states that the product should be taken only on an empty stomach; or (3) the RLD label does not make any statements about the effect of food on drug absorption or administration. 12 BCS Class II drug substances are low solubility, high permeability, and exhibit dissolu-

tion rate-limited absorption; BCS Class III drug substances are high solubility, low permeability, and exhibit permeability rate-limited absorption; BCS Class IV drug substances are low solubility, low permeability, and exhibit very poor oral bioavailability (Martinez and Amidon, 2002).

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Examples How the FDA decides whether or not to conduct a fed bioequivalence study for potential generic immediate-release drug products can be illustrated by several examples. The most commonly occurring situation is one in which the FDAapproved label summarizes food effects on bioavailability, and provide instructions for whether the drug product should be given with food. This is typified by the label for the zidovudine capsules, described above, which states that the product may be administered with or without food and that the extent of zidovudine absorption was similar when compared in human subjects (GlaxoSmithKline, R c label, 2006c). Since none of the above three exceptions apply in this Retrovir case, the FDA asks generic companies developing zidovudine capsules to conduct both fasting and fed bioequivalence studies against the corresponding reference. The label-driven criteria for whether or not a fed bioequivalence study should be conducted means that the FDA asks for a fed bioequivalence study even if the label documents no food effect on drug absorption. This is illustrated by finasteride tablets. As described above, the finasteride label states that food does not affect rate or extent of absorption and that the product may be given with or without food. Since the label makes statements about (lack of) food effects on both absorption and administration, the FDA asks applicants submitting ANDAs for generic finasteride tablets to conduct both fasting and fed bioequivalence studies. A case in which the FDA would request only a fasting bioequivalence study for a generic drug product is illustrated by the drug didanosine. As described above, the FDA-approved label for didanosine delayed-release pellets states that, in the presence of food, the didanosine Cmax and AUC were reduced by approximately 46% and 19%, respectively, compared to the fasting state. The Dosage and Administration section of the didanosine delayed-release pellets label states that the product R EC, 2006a). should be taken on an empty stomach (Bristol-Myers Squibb, Videx Since this product meets exception (2), above, the FDA asks applicants developing generic versions of didanosine delayed-release pellets to conduct only fasted bioequivalence studies.

10.3.6 Recommendations for Designing Fed Bioequivalence Studies With respect to fed bioequivalence study design, the FDA recommends a similar design to that recommended for food-effect bioavailability studies, except that the treatments should consist of both test and reference product formulations following a test meal. The FDA asks that both food-effect bioavailability and fed bioequivalence studies be conducted using meal conditions that are expected to provide the greatest effects on GI physiology. It is thought that this will provide the greatest effect on systemic drug availability. A high-fat (approximately 50% of the total caloric content of the meal) and high-calorie (approximately 800–1,000 kcal) meal is recommended as a test meal. The test meal should derive approximately 150, 250, and 500–600 kcal from protein, fat, and carbohydrates, respectively. The FDA

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asks applicants to provide the caloric breakdown of the meal in the study report submitted to the ANDA.

10.3.7

Food Effects and Generic Drug Product Labeling

Thus, characterizing food effects on the drug product formulation is an important part of generic drug development.13 The conclusion of bioequivalence under fed conditions indicates that, with respect to food, the language in the FDA-approved label of the generic drug product can be the same as for the reference drug product.

10.3.8 Sprinkle Studies in New and Generic Drug Product Development 10.3.8.1 Sprinkle Studies in Development of New Modified-Release Capsules In some cases it may be desirable to offer patients the option of sprinkling the contents of a modified-release drug capsule drug product onto a soft food such as applesauce, and swallowing without chewing. Because, direct contact with food prior to administration can potentially interact with release-controlling excipients, in these cases the FDA asks new drug applicants to conduct additional in vivo “sprinkle” relative bioavailability studies. These sprinkle bioavailability studies compare the drug product opened up and sprinkled on the soft food with the drug product given on an empty stomach. The label should indicate whether the drug capsule product can be given by sprinkling the contents on a soft food, and summarize the results of the sprinkle bioavailability study. 10.3.8.2 Sprinkle Studies in Development of Generic Modified-Release Capsules If a generic modified-release capsule drug product is developed to a corresponding reference drug product labeled for sprinkling on a soft food, then the FDA asks the ANDA applicant to conduct a sprinkle bioequivalence study. The sprinkle bioequivalence studies compare the generic product opened up and sprinkled on the soft food with the reference product opened up and sprinkled on the soft food. As with fasted and fed bioequivalence studies, the FDA concludes that the generic is bioequivalent to the reference under sprinkled conditions if the geometric mean test/reference ratios for AUC and Cmax fall within the bioequivalence limits of 80–125%. In this case, a conclusion of bioequivalence indicates that the language in the FDA-approved label of the generic drug product can be the same as for the reference drug product with respect to sprinkling on soft food for administration. 13 Studies investigating food effects on drug products also serve an important role in

new drug development. For example, when a sponsor develops a modified-release formulation of an already-marketed immediate-release product, it is particularly important to compare food effects on the immediate-release versus the proposed new modified-release formulation.

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Sprinkle bioequivalence studies are not necessary for immediate-release drug capsule products, as the capsule contents do not contain release-controlling excipients. 10.3.8.3 Example The role of sprinkle studies in generic drug product development is illustrated by the case of omeprazole delayed-release capsules. The Dosage and Administration section of the FDA-approved omeprazole label states that the capsule contents can be sprinkled on one tablespoon of applesauce and swallowed with a glass of R cool water to ensure complete swallowing of the pellets (AstraZeneca, Prilosec c label, 2005b). Because this is a modified-release drug product, the FDA asks generic drug applicants to conduct fasting and fed bioequivalence studies to document bioequivalence. Because of the labeling statements about administration with applesauce, the FDA also asks that generic drug applicants conduct a bioequivalence study in which subjects, following an overnight fast, receive the generic or reference product mixed in applesauce and swallowed with 240 mL of water.

10.4

Summary and Conclusions

The various examples presented above show that food can alter the bioavailability of a drug substance in many ways. These changes in bioavailability can sometimes necessitate dosage adjustments or specific dosing instructions in relation to administration with meals (FDA, 2001). The physiological changes occurring in response to food intake can also influence the demonstration of bioequivalence between a new generic drug product and its corresponding reference drug product. The FDA expects developers of both new and generic drug products to characterize these food effects during drug development, and has posted a guidance for industry providing recommendations about when food-effect bioavailability and fed-bioequivalence studies should be conducted, how these studies should be designed/analyzed, and how study results should be reported in drug product labeling. The ultimate objective is to provide an informative drug product label that will convey to clinical practitioners and patients information essential for the safe and effective use of either an innovator or generic drug product.

References R c AstraZeneca Pharmaceuticals LP, Wilmington DE 19850 (2003). Plendil ExtendedRelease Tablets package insert. R Tablets AstraZeneca Pharmaceuticals LP, Wilmington DE 19850 (2005a). Casodex package insert. R R 2005b). Prilosec DelayedAstraZeneca Pharmaceuticals LP, Wilmington DE 19850 ( Release Capsules package insert. c Bayer Pharmaceuticals Corporation, 400 Morgan Lane, West Haven CT 06516 (2005a). R R Tablets and Cipro Oral Suspension package insert. Cipro

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c Bayer Pharmaceuticals Corporation, 400 Morgan Lane, West Haven CT 06516 (2005b). R XL Tablets package insert. Cipro R Capsules and Bristol-Myers Squibb Company, Princeton, NJ 08543 (2005). Sustiva Tablets package insert. Bristol-Myers Squibb Virology, Bristol-Myers Company, Princeton, NJ 08543 (2006a). R Buffered Tablets package insert. Videx Bristol-Myers Squibb Virology, Bristol-Myers Company, Princeton, NJ 08543 (2006b). R EC package insert. Videx Colburn, W.A., Gibsen, D.M., Wiens, R.E., and Hannigan, J.J. (1983). Food increases the bioavailability of isotretinoin. J. Clin. Pharmacol. 23:534–539. Deferme, S. and Augustijns, P. (2003). The effect of food components on the absorption of P-gp substrates: a review. J. Pharm. Pharmacol. 55:153–162. R for safety reasons FDA News (2005). FDA asks Purdue Pharma to withdraw Palladone (July 12, 2005), http://www.fda.gov/bbs/topics/news/2005/NEW01205.html. Fleisher, D., Li, C., Zhou, Y., Pao, L., and Karim, A. (1999). Drug, meal and formulation interactions influencing drug absorption after oral administration: clinical implications. Clin. Pharmacokinet. 36:233–254. Gai, M.N., Isla, A., Andonaeugui, M.T., Thielemann, A.M., and Seitz, C. (1997). Evaluation of the effect of three different diets on the bioavailability of two sustained release theophylline matrix tablets. Int. J. Clin. Pharm. Ther. 35:565–571. R c Tablets and GlaxoSmithKline, Research Triangle Park, NC 27709 (2003). Ceftin R Ceftin Oral Suspension package insert. R c Tablets GlaxoSmithKline, Research Triangle Park, NC 27709 (2006a). Combivir package insert. R c Tablets packGlaxoSmithKline, Research Triangle Park, NC 27709 (2006b). Malarone age insert. R c Tablets, CapGlaxoSmithKline, Research Triangle Park, NC 27709 (2006c). Retrovir sules, and Syrup package insert. Grundy, J.S. and Foster, R.T. (1996). The nifedipine gastrointestinal therapeutic system (GITS). Evaluation of pharmaceutical, pharmacokinetic and pharmacologic properties. Clin. Pharmacokinet. 30:28–51. Harris, R.Z., Jang, G.R., and Tsunoda, S. (2003). Dietary effects on drug metabolism and transport. Clin. Pharmacokinet. 42:1071–1088. Hendeles, L., Weingerger, M., Milavetz, G., Hill, M., and Vaughan, L. (1985). Foodinduced “dose-dumping” from a once-a-day theophylline product as a cause of theophylline toxicity. Chest 87:758–765. Karim, A., Burns, T., Wearley, L., Streicher, J., and Palmer, M. (1985). Food-induced changes in theophylline absorption from controlled-release formulations. I. Substantial increased and decreased absorption with Uniphyl tablets and Theo-Dur Sprinkle. Clin. Pharmacol. Ther. 38:77–83. Lomaestro, B.M. and Bailie, G.R. (1995). Absorption interactions with fluoroquinolones. Drug Saf. 12:314–333. Maka, D.A. and Murphy, L.K. (2000). Drug-nutrient interactions: a review. AACN Clin. Issues 11:580–589. Marroum, P.J. and Gobburu, J. (2002). The product label: how pharmacokinetics and pharmacodynamics reach the prescriber. Clin. Pharmacokinet. 41:161–169. Martinez, M.N. and Amidon, G.L. (2002). A mechanistic approach to understanding the factors affecting drug absorption: a review of fundamentals. J. Clin. Pharmacol. 42:620–643.

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McLean, A.J., McNamara, P.J., du Souich, P., Gibaldi, M., and Laika, D. (1978). Food, splanchnic blood flow, and bioavailability of drugs subject to first-pass metabolism. Clin. Pharmacol. Ther. 24:5–10. R c Tablets package Merck & Co., Inc., Whitehouse Station, NJ 08889 (1997). Propecia insert. R Tablets Mission Pharmacal Company, San Antonio, TX 78230-1355 (2006). Tindamax package insert. Moore, K.H.P., Shaw, S., Laurent, Z.L., Lloyd, P., Duncan, B., Morris, D.M., O’Mara, M.J., and Pakes, G.E. (1999). Lamivudine/zidovudine as a combined formulation tablet: bioequivalence compared with lamivudine and zidovudine administered concurrently and the effect of food on absorption. J. Clin. Pharmacol. 39:593–605. Neuhofel, A.L., Wilton, J.H., Victory, J.M., Hejmanowski, L.G., and Amsden, G.W. (2002). Lack of bioequivalence of ciprofloxacin when administered with calcium-fortified orange juice: a new twist on an old interaction. J. Clin. Pharmacol. 42:461–466. R c Soft Novartis Pharmaceuticals Corporation, East Hanover, NJ 07936 (2005). Neoral R Oral Solution, Modified, USP, package insert. Gelatin Capsules USP and Neoral Pfizer, Inc. v. Shalala, 1 F.Supp.2d 38 (D.D.C. 1998). R Procter & Gamble Pharmaceuticals, Inc., Cincinnati, OH 45202 (2003). Macrobid Capsules package insert. R c Purdue Pharmaceutical Products L.P., Stamford, CT 09601-3431 (2004). Uniphyl Tablets package insert. R c Reliant Pharmaceuticals, Inc., Liberty Corner, NJ 07938 (2005). InnoPran XL package insert. R Roche Laboratories, Inc., Nutley, NJ 07110-1199 (2004). Lariam Tablets package insert. R Roche Laboratories, Inc., Nutley, NJ 07110-1199 (2005). Accutane Capsules package insert. Rolan, P.E., Mercer, A.J., Weatherley, B.C., Holdich, T., Meire, H., Peck, R.W., Ridout, G., and Posner, J. (1992). Investigation of the factors responsible for a food-induced increase in absorption of a novel protozoal drug 566C80. Br. J. Clin. Pharmacol. 33:226P–227P. Schug, B.S., Brendel, E., Chantraine, E., Wolf, D., Martin, W., Schall, R., and Blume, H.H. (2002a). The effect of food on the pharmacokinetics of nifedipine in two slow release formulations: pronounced lag-time after a high fat breakfast. J. Clin. Pharmacol. 53:582–588. Schug, B.S., Brendel, E., Wonnemann, M., Wolf, D., Wargenau, M., Dikngler, A., and Blume, H.H. (2002b). Dosage form-related food interaction observed in a marketed once-daily nifedipine formulation after a high-fat American breakfast. Eur. J. Clin. Pharmacol. 58:119–125. Semple, H.A. and Fangming, X. (1995). Interaction between propranolol and amino acids in the single-pass isolated, perfused rat liver. Drug Metab. Dispos. 23:794–798. Singh, B.N. and Malhotra, B.K. (2004). Effects of food on the clinical pharmacokinetics of anticancer agents: underlying mechanisms and implications for oral chemotherapy. Clin. Pharmacokinet. 43:1127–1156. Soldner, A., Christians, U., Susanto, M., Wacher, V.J., Silverman, J.A., and Benet, L.Z. (1999). Grapefruit juice activates P-glycoprotein-mediated drug transport. Pharm. Res. 16:478–485. Su, Y.-M., Cheng, T.-P., and Wen, C.-Y. (2003). Study of the effect of food on the absorption of theophylline. J. Chin. Med. Assoc. 66: 715–721.

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Tam, Y.K. (1993). Individual variation in first-pass metabolism. Clin. Pharmacokinet. 25:300–328. U.S. Code of Federal Regulations (2006a). Title 21, Part 320 – Bioavailability and Bioequivalence Requirements, Subpart A – General Provisions, Section 320.1 Definitions. U.S. Government Printing Office, Washington, D.C., pp. 184–185. U.S. Code of Federal Regulations (2006b). Title 21, Part 320 – Bioavailability and Bioequivalence Requirements, Subpart B – Procedures for Determining the Bioavailability or Bioequivalence of a Drug Product, Section 320.22. Criteria for waiver of evidence of in vivo bioavailability or bioequivalence. U.S. Government Printing Office, Washington, D.C., pp. 186–188. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (2000). Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage Forms Based on a Biopharmaceutics Classification System (August 31, 2000); http://www.fda.gov/ cder/guidance/3618fnl.pdf. U.S. Department of Health and Human Services Food and Drug Administration, Center for Drug Evaluation and Research (2002). Advisory Committee for Pharmaceutical Sciences meeting transcript (May 7, 2002), http://www.fda.gov/ohrms/ dockets/ac/02/transcripts/3860T1.pdf. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (2003a). Guidance for Industry: Bioavailability and Bioequivalence Studies for Orally Administered Drug Products – General Considerations (March 19, 2003); http://www.fda.gov/cder/guidance/5356fnl.pdf. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (2003b). Guidance for Industry: Food-Effect Bioavailability and Fed Bioequivalence Studies (January 31, 2003); http://www.fda.gov/ cder/guidance/5194fnl.pdf. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Sciences, Office of Generic Drugs (2006). Approved Drug Products with Therapeutic Equivalence Evaluations (Orange Book), 26th Ed. (April 11, 2006); http://www.fda.gov/ cder/orange/obannual.pdf. U.S. Food and Drug Administration (2001). Draft guidance for industry on food-effect bioavailability and fed bioequivalence studies: study design, data analysis, and labeling; availability. Fed. Regist. 66:59433. U.S. Food and Drug Administration (2003). Guidance for industry on food-effect bioavailability and fed bioequivalence studies; availability. Fed. Regist. 68:5026–5027. Weinberger, M.M. (1984). Theophylline QID, BID and now QD? A report on 24-hour dosing with slow-release theophylline formulations with emphasis on analysis of data used to obtain Food and Drug approval for Theo-24. Pharmacotherapy 4:181–198. Welling, P.G. (1996). Effects of food on drug absorption. Annu. Rev. Nutr. 6:383–415. Williams, P.E. and Harding, S.M. (1984). The absolute bioavailabaility of oral cefuroxime axetil in male and female volunteers after fasting and after food. J. Antimicrob. Chemother. 13:191–196. Wu, C-Y. and Benet, L.Z. (2005). Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a Biopharmaceutics Drug Disposition Classification System. Pharm. Res. 22:11–23.

11 In Vitro–In Vivo Correlation on Parenteral Dosage Forms Banu S. Zolnik and Diane J. Burgess

11.1

IVIVC Definition

In vitro and in vivo correlation (IVIVC) for drug products, especially for solid oral dosage forms, has been developed to predict product bioavailability from in vitro dissolution. Biological properties such as Cmax , or AUC have been used to correlate with in vitro dissolution behavior such as percent drug release in order to establish IVIVC. IVIVC can be used to set product dissolution specifications; and as a surrogate for in vivo bioequivalence in the case of any changes with respect to formulation, process, or manufacturing site.

11.2

Modified Release Parenteral Products

Modified release (MR) parenteral products achieve sustained blood levels of therapeutics consequently decreasing dosing frequency and increasing patient compliance. These systems offer advantages over traditional dosage forms due to their sustained release capabilities and therefore more consistent blood levels that can result in a lowering of the systemic toxicity of drugs. The efficacy of chemotherapeutic agents has been reported to improve when steady relatively low blood levels were achieved compared to high dose i.v. bolus injections (Herben et al., 1998; Hochster et al., 1994). This can be accomplished by encapsulation of chemotherapeutics within liposomal and polymeric delivery systems. In addition, modified release parenteral products are used for targeted and localized drug delivery, which also reduces unwanted side effects. Potential drug candidates for MR parenterals are chemotherapeutics or other drugs with a high incidence of adverse side effects; proteins or other macromolecules due to their instability in the gastrointestinal (GI) tract; drugs with short half-lives; drugs with low solubility; and drugs that are susceptible to high firstpass effect. Modified release parenterals include: microspheres; liposomes; emulsions; suspensions; implants; drug eluting stents; and dendrimers. Recent developments in 336

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synthetic chemistry have been utilized to make dendrimers, liposomes, and other parenteral delivery systems multifunctional through the addition of targeting moieties, and imaging agents. The reader is referred to the detailed reviews on the incorporation of monoclonal antibodies and other ligands to such delivery systems (Torchilin, 2005; Torchilin and Levchenko, 2003; Torchilin and Lukyanov, 2003). In addition, delivery system particle size and drug loading can be manipulated to alter tissue distribution as well as release rates. Surface modification with polyethylene glycol (PEG) or other polymers has been utilized to increase the blood circulation half-life.

11.3

Factors to Consider for Meaningful IVIVC

Strategies to develop meaningful IVIVC for MR products are summarized below. It is important first to obtain in vivo data, and then identify the in vivo drug release mechanism. The in vitro release method can then be designed with consideration to the in vivo release profile and mechanism.

11.3.1

Product Related Factors

There are several factors related to the formulation of MR parenterals that may affect the in vivo performance of these products when administered via parenteral routes (i.m., i.v., s.c., intra-CSF). These factors include formulation dispersibility, stability, injection volume, viscosity, and biocompatibility. To ensure dispersibility and also ease of injection, microspheres, and other dispersed system parenterals can be suspended in a vehicle containing an isotonic solution of carboxymethylcellulose, surfactant prior to administration (http://www.gene.com/gene/products/ information/opportunistic/nutropin-depot/insert.jsp). The injection of a homogenous suspension of microspheres should be assured otherwise erroneous dosing may occur that would affect the in vivo data and hence the development of an IVIVC. On the other hand, the presence of surfactant could affect the release properties in vivo by enhancing drug solubility and diffusion or affecting viscosity. It has been reported that variation in the injection depth for i.m. administration resulted in large variations in plasma drug concentrations (Zuidema et al., 1994). Formulation stability should be monitored prior to injection of dispersed systems since any particle size change may result in adverse effects and alteration of drug release characteristics. Another important factor with respect to microspheres is the reconstitution time since premature drug release may occur in the delivery vehicle due to dissolution of surface associated drug from the microspheres. This may result in an underestimation of the initial dose released (burst release) upon administration. R EL (CrEL; polyoxyethyleneglycerol Nonionic surfactants such as Cremophor triricinolate 35) and polysorbate 80 (Tween 80) have been used to solubilize a variety of drugs prior to i.v. administration. A detailed review by Tije et al. reports

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adverse effects such as acute hypersensitivity and peripheral neurotoxicity as well as altered pharmacokinetics of chemotherapeutics when administered with these surfactants (ten Tije et al., 2003). In addition, there may be toxicity issues with certain excipients, especially when used at high concentration. For example, administration of propylene glycol at concentrations above 40% has been reported to cause muscle damage. Consequently, in vivo markers, such as cytosolic enzymes, creatine kinase and lactate dehydrogenase, should be monitored as these are indicators of tissue damage which may result from either the drug, or the excipients. The encapsulated drug formulation may result in a reduction in toxicity, for example microspheres or liposomes can be used to isolate high concentrations of irritant drugs which are then released slowly at levels that either do not show toxicity or show limited toxicity in vivo. For example, it has been shown that encapsulation of tissue irritant drugs into liposome formulations reduced muscle damage considerably (Kadir et al., 1999). Toxicity and irritancy at the in vivo site can affect drug release due to resulting edema as well as the presence of increased numbers of neutrophils and macrophages. The different manufacturing techniques used to prepare polymeric delivery systems as well as liposomes mostly involve the use of organic solvents. The processes of removal of organic solvent and of determining the amount of residual solvent in the product are crucial due to the in vivo relevance (toxicity, tolerance, systemic side effects).

11.3.2

Factors Affecting In Vitro Release

In vitro release methods are an integral part of the product development process to establish quality, performance, and batch to batch consistency as well as in vivo and in vitro relationships. Current uses of in vitro release testing are summarized in Table 11.1. Unfortunately, there is a lack of standards or guidance documents for in vitro release testing methods for modified release parenterals. The United States Pharmacopeia (USP) apparatus for dissolution testing methods were developed for solid oral dosage forms and transdermal products. Briefly, USP Apparatus 1 (basket) and 2 (paddle) are suitable for solid dosage forms. Apparatus 3 (reciprocating cylinder) and Apparatus 4 (flow-through cell) were developed for drugs with limited solubility and are useful for MR products. Apparatus 5 (paddle over disc), Apparatus 6 (cylinder), and 7 (reciprocating disk) were developed for transdermal delivery systems. In some cases current USP methods have been modified to overcome limitations of the existing methods for application to MR parenterals TABLE 11.1. Current uses of in vitro release testing method • • • • •

Formulation development Quality assurance and process control Evaluation of the changes in the manufacturing process Substantiation of label claims Compendial testing

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products. For example, USP Apparatus 4 has been adapted for microsphere testing through the inclusion of glass beads in the flow-through cells (Zolnik et al., 2005). The glass beads are interspersed between the microspheres to prevent aggregation during the release study and to more closely simulate the in vivo conditions where the microspheres are interspersed among the cells, e.g, at the s.c. site (Zolnik et al., 2005). Moreover, the addition of the glass beads in the flow-through cells allows laminar flow of release media and prevents the formation of channels in the solid bed where the media flow through while other areas in the bed would remain unwetted. Figure 11.1 displays the schematic diagram of flow-through cell containing microspheres and glass beads in the closed mode. In vitro release testing methods currently used in research and development as well as quality control include: dialysis sac, sample-and-separate, ultrafiltration, continuous flow methods, and microdialysis. The dialysis sac method involves suspending microspheres or other dispersed systems in a dialysis sac with a semipermeable membrane that allows diffusion of the drug, and then drug concentration is monitored in the receiver chamber. Disadvantages of this method include (a) potential for dispersed system aggregation due to the lack of agitation and (b) violation of sink conditions may result when drug release from the microspheres is faster than drug diffusion through the membrane (Chidambaram and Burgess, 1999). A reversed dialysis method has been developed by Chidambaram and Burgess, where the dispersed phase is placed in the large chamber with the media and the sacs contain only media. The sacs are then sampled at the different time points. This method overcomes the problem of violation of sink conditions (Chidambaram and Burgess, 1999). The sample and separate technique utilizes USP Apparatus 2 (paddle method) where microspheres are dispersed in the media

UV probe Filter system Microspheres Glass Beads Flow-Through cell

Magnetic-Stirrer

Piston pump

F IGURE 11.1. Schematic diagram of 12 mm flow-through cell containing microspheres and glass beads in the flow-through method (closed system). Placement of the fiber optic probe in the reservoir vessel is also shown

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and at different time points samples are withdrawn, separated via ultracentrifugation or filtration and the filtrate is analyzed for drug content with an appropriate analytical method. The disadvantages of this method are the difficulty in separation of the delivery system from the media, for example, ultrafiltration requires 1 or 2 h at high centrifugational force (150, 000 × g) and this often is an undesirable method due to disruption of the delivery system and consequent alteration in the release pattern (Chidambaram and Burgess, 1999). As an alternative, low pressure ultrafiltration has been used to prevent disruption. The disadvantage of this method is the lack of available membranes with appropriate cut off points since some delivery systems are in the submicron and micron size range (Magenheim et al., 1993). The continuous flow method (USP Apparatus 4) consists of a reservoir, a pump and flow-through cells where the microspheres or other dispersed systems are contained. The continuous flow method avoids problems associated with separation of the dispersed system from the media since the dispersed system is isolated in the flow-through cells and the media can be sampled from the reservoir. Another advantage of the flow-through method for dispersed systems is that since the dispersed system is isolated from the media reservoir this allows in situ monitoring. UV fiber optic probes can be placed in the media reservoir vessel thus avoiding the potential problem of interference from dispersed system particles sticking to the probe. In situ monitoring has the advantage that multiple time points can be analyzed to allow for complete characterization of the release profile. For example, this method has been used to characterize the burst release phase from microspheres. Schematic showing the placement of the in situ probes is shown in Fig. 11.1 (Zolnik et al., 2005). In addition, violation of sink conditions for drugs with limited solubility is not an issue with the continuous flow USP 4 method due to the ease of media replacement. Microdialysis has been used to study pharmacokinetics of drugs in peripheral tissues (Boschi and Scherrmann, 2000; de la Pena et al., 2000). Recently microdialysis has been used to monitor drug release in vitro (Dash et al., 1999). The basic principle of this technique is to measure drug release continuously from an implant site by mimicking a capillary blood vessel with a thin dialysis tube. An advantage of this technique is that the flow rate of the media can be adjusted to as low as 0.5 µl/min. Other advantages of this technique are (a) small volume (b) continuous monitoring of drug release, and (c) online analysis (Dash et al., 1999). Dash et al. had compared a microdialysis method with the USP Apparatus 3 method to monitor ciprofloxacin release from PLGA implants and reported that both these methods were in close agreement. Researchers have also evaluated miniaturized methods where small volumes of media are employed due to the in vivo relevance (volume at the s.c. site is low). However, the disadvantages of this method are violation of sink conditions and the potential for dispersed system aggregation due to the limited volume and lack of agitation. In order to develop meaningful IVIVC, study design for in vitro release should be performed after in vivo data are available, so that media conditions can be manipulated to mimic the in vivo behavior. To this end, researchers

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have investigated different media conditions to aid in the development of a relationship between in vivo and in vitro release data. The use of cosolvent, addition of surfactants and enzymes, variation in pH, ionic strength, agitation and temperature have been investigated (Agrawal et al., 1997; Aso et al., 1994; Blanco-Prieto et al., 1999; Hakkarainen et al., 1996; Jiang et al., 2002; Li et al., 2000; Makino et al., 1986). For example, acidic media have been used to mimic drug release from PLGA microspheres in vivo (Blanco-Prieto et al., 1999; Heya et al., 1994a). There is no single in vitro release testing method suitable for all parenterals delivery systems due to their complexities. However, USP Apparatus 4 is recommended for modified release oral formulations and is appropriate for modified release parenterals. USP Apparatus 4 has been recommended for MR microsphere products (Burgess et al., 2004). The physicochemical properties of drugs and delivery systems should be taken into account when choosing an appropriate release method. In addition, the in vitro method should be able to discriminate between formulations with different in vivo release characteristics. 11.3.2.1 Accelerated In Vitro Release Testing Since MR parenterals may be intended to release drug for days, weeks, and even months, accelerated in vitro release testing methods are required for routine testing of these products. Therefore, if the accelerated method is to be used as a surrogate for in vivo studies IVIVC must be established using the accelerated method. A problem here is that accelerated methods, by their nature, often change the mechanism of drug release and this can make the establishment of an IVIVC more difficult. For example, elevated temperature accelerated conditions have been shown to alter the mechanism of release from PLGA microspheres from degradation controlled to diffusion controlled (Zolnik et al., 2006). On the other hand, under pH accelerated conditions, release from PLGA microspheres appeared to be degradation controlled eventhough morphological changes occurred during degradation that were distinctly different from those that occur during “real-time” in vitro release testing.

11.3.3

Mathematical Models of In Vitro Drug Release

Different models have been developed depending on the governing, rate-limiting step of drug release. For MR systems the mathematical models used can be categorized as: diffusion controlled, swelling controlled, and erosion controlled release systems. Mathematical models to evaluate drug release have been extensively used, especially for solid dosage forms to understand drug transport through barriers. Fick’s second law of diffusion states that the rate of change in concentration is proportional to the rate of change in the concentration gradient at that point where the proportionality constant is equal to the diffusivity “D”. The assumption is

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constant diffusivity. 2 d C d2 C d2 C dC =D + + 2 dt dx 2 dy 2 dz

(11.1)

Various exact solutions of (11.1) depending on the boundary condition of the system were reviewed in detail by Flynn et al. (1974). The commonly used form of (11.1) is below. The assumptions necessary to arrive at (11.2) are (a) sink conditions are maintained; (b) diffusivity is constant; and (c) steady state is reached. DC0 dM = dt h

(11.2)

Higuchi derived the following equation for systems when boundaries change with time, such as drug release from a semisolid ointment. The change in the amount released per unit area, dM, is equal to a change in the thickness of the moved boundary, dh. A is the total amount of drug in the matrix. Cs is the saturation concentration of the drug within the matrix dM = A dh −

Cs dh. 2

(11.3)

According to Ficks law, dM is equal to (11.2). The equation which describes the amount released as a linear function of the square root of time can be derived (11.4) after setting (11.2) and (11.3) equal. The assumptions used in this derivation are: initial drug loading is much higher than drug solubility, swelling of the system is negligible, sink conditions are maintained, and edge effects are negligible.

M = 2Cs D A. (11.4) There are several mathematical models derived for different systems and different geometries (such as, spheres), as well as for release of drugs suspended in spherical particles, and for systems where the rate of drug release is swelling controlled. Ritger and Peppas (1987) derived a semiempirical equation known as the power law (11.5) for systems with different geometries (slab, cylinder, and sphere) to describe drug release for diffusion controlled, swelling controlled, and controlled by intermediate anomalous mass transport. Mt = kt n , M∞

(11.5)

where k is a constant and n is the release exponent indicative of the drug release mechanism. In the case of Fickian diffusion controlled release, n equals to 0.43 for spherical geometry. In order to identify the drug release mechanism from low molecular weight PLGA microspheres, (11.3) was utilized. Diffusion kinetics were confirmed for different flow rates using modified USP Apparatus 4 (Zolnik et al., 2006). Modeling of drug release from biodegradable polymers such as PLGA is complex since

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it involves not only diffusion phenomena of drug release but also physicochemical changes in the polymer. Empirical models have been derived based on the assumption that one net mechanism with zero order process can describe all mechanisms involved, such as dissolution, swelling, and polymer degradation. Mechanistic models based on Monte Carlo simulations have been applied to describe polymer degradation and diffusion phenomena (Siepmann and Gopferich, 2001). Drug release from such systems has also been modeled by including the dependence of the diffusion coefficient on the polymer molecular weight change (Faisant et al., 2002). Lemaire et al. were able to show the relative dominance between the diffusion and erosion release kinetics when different parameters such as erosion rate, initial pore size, porosity and the diffusion coefficient of the drug were varied (Lemaire et al., 2003). It has been established that PLGA degradation followed pseudo-first-order degradation kinetics (11.6). Mw(t) = Mw0 e−kdeg t

(11.6)

First order degradation kinetics have been observed from PLGA microspheres at elevated temperature. This was used to establish drug release mechanisms under accelerated release conditions where temperature varied between 37 and 70◦ C (Zolnik et al., 2006). It should be noted that when an initial burst release exists, it is recommended to test the burst phase separately under “real-time” conditions, as under accelerated conditions the burst phase is usually not observed. Likewise it is often necessary to model the release separately from the burst phase. High correlation has been observed for drug release from PLGA microspheres postburst release (Zolnik et al., 2006).

11.3.4

Factors Affecting In Vivo Release

In vivo release from MR parenterals such as microspheres may be affected by the environment at the site of administration for example s.c. or i.m. injected products are generally retained at the administration site depending on the particle size. In vivo factors that affect drug release can be classified as delivery system independent and delivery system dependent. Delivery system independent factors include barriers to drug diffusion (e.g., fluid viscosity and connective tissue); drug partitioning at the site (e.g., uptake into fatty tissue); available fluid volume at the site; and in the case of intramuscular injection muscle movement may also be an important factor. For example, factors related to subcutaneous tissue are interstitial fluid volume, blood flow rate, osmotic pressure, and the presence of plasma proteins. It has been reported that the diffusion of macromolecules from the interstitium may be delayed by the fibrous collagen network, and the gel structure of proteoglycans as well as possible electrostatic interaction with components of the interstitium. More information on protein absorption and bioavailability from the subcutaneous tissue can be found in a detailed review article by Porter and Charman (2000). Delivery system dependent factors are those specific to a particular delivery system and include enzymatic degradation of susceptible polymers, protein adsorption, phagocytosis

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as well as inflammatory reaction. For example, the initial acute phase of inflammation results in an influx of fluid together with phagocytic cells and the increased fluid volume may increase drug release and adsorption. Whereas, the chronic stage of inflammation can lead to fibrosis which in turn results in isolation of the delivery system with consequent reduction in the fluid volume. A major challenge to in vivo delivery of drug carriers following IV administration is the rapid removal of these particles from circulation by the reticuloendothelial system (RES) mainly the Kupffer cells of the liver and the macrophages of the spleen and bone marrow. In order to reduce interaction with plasma proteins and consequently prevent RES uptake, and increase blood circulation time, MR parenterals have been surface modified with PEG polymers. A thorough review on this subject can be found in an article by Moghimi et al. (2001). Different drugs have been coencapsulated in microspheres to alter their in vivo behavior. For example, dexamethasone was coencapsulated with bupivacaine to increase the concentration of bupivacaine at the local site by decreasing its clearance from the tissues due to the vasoconstrictive nature of dexamethasone. In this case, in vitro release of bupivacaine was not altered when dexamethasone was incorporated. Care should be taken to determine the pharmacodynamic effects when drugs are given in combination in such formulations (McDonald et al., 2002).

11.4

In Vitro–In Vivo Correlation

IVIVC can be categorized as follows: Level A, point-to-point correlation over the entire release profile and is used to claim biowaivers; Level B, mean in vitro dissolution time is compared to either the mean residence time or the mean in vivo dissolution time; Level C, single point correlation between a dissolution parameter (for example, the amount dissolved at a particular time or the time required for in vitro dissolution of a fixed percentage of the dose) and an in vivo parameter (for example, Cmax or AUC); Multiple Level C correlation, a Level C correlation at several time points in the release profile. Figure 11.2 summarizes general considerations with respect to in vivo release and distribution of protein loaded microspheres for establishing IVIVC. In this scheme Morita et al. compartmentalized the events involving in vivo pharmacokinetics of protein release from microspheres as: drug release rate constants (K rel ) from microspheres, protein degradation constant as K deg , drug absorption to systemic circulation defined as K a , and distribution to target tissues as K d while drug elimination from kidney or liver defined as K el . In this scheme, Morita et al. have also included the possible immune response effects on in vivo pharmacokinetics of proteins due to generation of specific antibodies. The authors indicated that antibodies generated in normal mice may alter the clearance rate of bovine derived superoxide dismutase and this affect was not observed in severe combined immunodeficiency disease mice. In this scheme, there are three output functions which are used to establish IVIVC, X1 in vitro release profile correlated to either Y1

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F IGURE 11.2. General considerations for the IVIVC of protein loaded microspheres

defined as disappearance profile from the administration site, or plasma concentration time profile as Y2. The pharmacological effects of drugs at the target tissue are defined as Y3 (Morita et al., 2001). Different levels of correlations can be achieved by comparing X1 (in vitro drug release) to Y1 (in vivo disappearance) or Y2 (plasma concentration time profile). If Y2 is used, convolution procedure or any other modeling technique can be used to relate plasma concentration time profile to in vivo absorption or release rate. If a linear relationship between the in vitro and release data does not occur then, IVIVC can be achieved by mathematical modeling (e.g. time variant nonlinear modeling) of the in vitro and in vivo data (Young et al., 2005).

11.5

Microspheres

Microspheres are polymeric spherical particles in the micron size range. Drug can be entrapped in these particles either in the form of microcapsules with a polymer coating surrounding a drug core or in the form of micromatrices with the drug dispersed throughout the polymer (Burgess and Hickey, 1994). Both natural and synthetic polymers have been used to form microspheres (Cleland, 1997). In this chapter, synthetic polymers such polyesters, poly(lactic acid) (PLA) and poly(lactic-co-glycolic acid) polymers (PLGA), will be reviewed. These polymers gained importance in the field of drug delivery due to their biodegradability and R , that releases relative biocompatibility (Kulkarni et al., 1971). Lupron Depot potent analogue of luteinizing hormone–releasing hormone (LH–RH) over periods of 1 and 3 months (Okada, 1997), was the first controlled release microsphere product available on the US market for the treatment of hormone dependent

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prostate and mammary tumors, and endometriosis. Since 1989, the Food and Drug Administration has approved the following five PLGA microsphere products (Lupron Depot, Sandostatin LAR, Nutropin Depot and Trelstar Depot, Risperdal Consta). Microspheres are designed as modified release drug delivery systems where drug is released in periods of days to months. From a safety and efficacy perspective, it is important to understand drug release kinetics from such formulations. Microsphere systems tend to exhibit complex release kinetics with: an initial burst release, as a result of surface associated drug and this is usually diffusion controlled. Following the burst release phase, the mechanism of release may be diffusion or erosion controlled or a combination of thereof (Gopferich, 1996; Lewis, 1990; Okada, 1997). Drug release from PLGA microspheres typically falls under the combination of diffusion and erosion controlled where an initial burst release is followed by a lag phase and then a secondary, apparent zero order release phase. The lag phase is considered to be a result of the time required for the build up of acid byproducts, and, hence for sufficient bulk erosion to take place, to increase porosity and allow for the subsequent secondary apparent zero order phase (Brunner et al., 1999; Mader et al., 1998; Shenderova et al., 1999). In the literature different levels of IVIVC have been established for PLGA microspheres. In a study by Zolnik and Burgess, a biorelevant in vitro release method (USP 4 method) and Sprague Dawley rat model was utilized to obtain a relationship between in vitro and in vivo release of dexamethasone from two different PLGA microsphere formulations. A linear IVIVC using the time shifting/scaling method discussed above was established for microsphere formulations prepared with different molecular weights of PLGA. The time scaling/shifting method was applied to in vitro data due the observance of faster in vivo release of dexamethasone (Zolnik 2005). In addition, the release of dexamethasone was able to control the inflammatory reaction that would otherwise occur to the presence of microspheres and to the tissue damage that occurs due to needle injection and this appeared to result in faster in vivo kinetics compared to the in vitro kinetics. In a previous publication from our laboratory, it has been reported that release kinetics of vascular endothelial growth factor (VEGF) from PLGA microspheres was slower in vivo compared to in vitro (Kim and Burgess, 2002). A possible explanation for this is the severe inflammatory reaction that occurred in the presence of these microspheres. This is considered to be a result of both tissue reaction to the PLGA microspheres as well as to the foreign protein (human VEGF was used in a rat model). It has also been shown for leuprolide that repeated injections of Lupron depot did not alter the bioavailability and urinary excretion of leuprolide and the mean serum levels and AUC of Lupron Depot, correlated linearly with each dose. More detailed information on the formulation, drug release and in vivo animal models of leuprorelin depot can be found in a comprehensive review by Okada (1997). The evaluation of different in vitro conditions to mimic in vivo release of thyrotropin releasing hormone (TRH) from PLGA microspheres were investigated by Heya et al. (1994a). Authors concluded that the selection of the media conditions (such as medium pH, buffer concentration, ionic strength) is important to obtain an

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in vitro release profile that mimics in vivo release, especially for hydrophilic drugs (Heya et al., 1994a). In the follow-up study, Heya et al. examined the pharmacokinetics of TRH from PLGA microspheres and determined that sustained in vitro release kinetics were mimicked in vivo using 33 mM pH 7 phosphate buffer containing 0.02% Tween 80 (Heya et al., 1994b). Other examples of Level A correlation were demonstrated by Cheung et al. and utilized the continuous flow method in dynamic and static mode to mimic in vivo release from locoregionally administered dextran-based microspheres (Cheung et al., 2004). An example of Level B correlation was shown for release of the somatostatin analogue vapreotide from PLA and PLGA microspheres where the mean in vivo residence time was correlated with the mean in vitro dissolution time (Blanco-Prieto et al., 2004). A linear IVIVC was demonstrated in a different polymer system by van Dijkhuizen-Radersma (2004) in a study of protein release from poly(ethylene glycol) terephthalate (PEGT)/poly(butylene terephthalate) PBT microspheres. Similar to the properties of polyesters, poly(ether–ester) PEGT/PBT multiblock copolymers exhibit biodegradability and biocompatibility. Three different microsphere formulations with varied PEGT/PBT weight ratio and PEG segment length were investigated. The diffusion coefficient of drugs from PEGT/PBT microspheres was dependent on polymer swelling which in turn was related to its PEG segment length. In vitro release from PEGT/PBT microspheres correlated with the volume swelling ratios, faster release was obtained using polymers with higher swelling ratios. In vivo release kinetics are often not predicted by in vitro release methods, possibly due to selection of inappropriate in vitro release conditions, and methods (Diaz et al., 1999; Jiang et al., 2003). It should be also noted that inappropriate selection of animal model may result in unsuccessful IVIVC. For example, Perugini et al. demonstrated that a rat model was not suitable to induce osteopenia and therefore, IVIVC could not be established (Perugini et al., 2003). The difficulties in determination of drug amounts in the biological matrix also resulted in lack of IVIVC (Yenice et al., 2003). In addition, there are several comprehensive and well-designed research articles in the literature on the evaluation of in vitro and in vivo release of drugs from microspheres; however, these articles did not attempt to show any mathematical correlation of their in vivo and in vitro results (Liu et al., 2003).

11.6

Liposomes

Liposomes consist of one or more phospholipid bilayers with enclosed aqueous phase. Depending on the method of preparation of liposomes, different types of liposomes are formed: large multilamellar (MLVs); small unilamellar, (SUVs); or large unilamellar (LUVs). Liposomal drug delivery has advantages over traditional therapy in cancer treatment due to increased tumor uptake via enhanced permeation and retention (EPR) effect where tumor tissue has leaky vasculature

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Active drug Microsphere products Leuprolide Octreotide Somatropin Triptorelin Abarelix Liposome products Daunorubicin Doxurubicin Doxurubicin in PEG-liposomes Amphotericin B Cytarabine Vincristine Emulsion products Propofol Diazepam

Product name

Indications

Lupron Sandostatin LAR Nutropin depot Triptorelin Plenaxis

Endometriosis Agromegaly Growth therapy Prostate cancer Prostate cancer

DaunoXome Mycet

AmBiosome DepoCyt Onco TCS

Kaposi’s sarcoma Combinational therapy of recurrent breast cancer Refractory Kaposi’s sarcoma; ovarian cancer; recurrent breast cancer Fungal infection Lymphomatous meningitis Non-Hodgkin’s lymphoma

Diprivan Dizac

Anesthetic Epilepsy

Doxil/Caelyx

and poor lymphatic drainage (Maeda et al., 2001). Liposomal products with encapsulated daunorubicin, doxorubicin, and vincristine are currently in the market for the treatment of cancer (Table 11.2). Drug release and cell uptake kinetics may depend on the size, charge, surface properties of the liposomes as well as on the types of lipids used. The incorporation of stabilizing lipids with high phase transition temperatures tends to decrease drug release (Anderson and Omri 2004; Bochot et al., 1998; Ruel-Gariepy et al., 2002). One of the major challenges of liposomal drug delivery is the rapid uptake of liposomes by the RES. Sterically stabilized liposomes with PEG chains with increased circulation half-life have been developed to decrease contact with blood components, and consequently avoid recognition by the RES system. In clinical studies, the blood circulation half-life of these “Stealth” liposomes was extended from a few hours to 45 h consequently, altering tissue distribution of drugs compared to free drug controls due to their prolonged circulation. pH sensitive liposomes have been formulated to undergo phase change in the acidic environment resulting in a disruption of the lysosomes, and consequent release of the liposome contents into the cytoplasm (Simoes et al., 2004). Immunoliposomes where immunoglobulins are attached to liposomes via covalent binding or by hydrophobic insertion to increase their targeting capabilities have also been formulated. Other types of liposomes have been also where ligands such as folate, transferrin mediated liposomes have been utilized to target tumor tissues. More detailed information on recent advancement on the types of liposomes can be found in a review by Torchilin (2005). An FDA Draft Guidance document for industry on liposome products states that the characterization of physicochemical properties of liposomes is critical to

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determine product quality (FDA Draft Guidance, 2002). These tests include determination of morphology, i.e., lamellarity, net charge, volume of entrapment in the vesicles, particle size and size distribution, phase transition temperature, in vitro drug release from the liposomes, osmotic properties, and light scattering index. The guidance document states that information on in vivo integrity of liposomes should be determined prior to measurement of pharmacokinetic parameters of liposomes. In addition to the information on general pharmacokinetic parameters (i.e., Cmax , AUC, clearance, volume of distribution, half-life) in vivo, comparative mass-balance studies of drug substance and its liposomal formulation were recommended to determine systemic exposure (FDA Draft Guidance, 2002). In order to ensure quality control of the product, chemical stability of liposomes such as phospholipid hydrolysis, nonesterified fatty acid concentration, autooxidation, and drug stability should be identified (Crommelin and Storm, 2003). Jain et al. investigated acyclovir release from multivesicular (MVL) and conventional multilamellar (MLV) vesicles for in vitro and in vivo studies. They were able to show sustained release in 96 h with MVL liposomes, while MLV’s exhibited faster release kinetics in 16 h using dialysis as an in vitro release method. Using an in vivo rat model, they were able to show sustained plasma levels of drug from MVL up to 32 h, concluding that MVL offered advantages of high drug loading and sustained release with reduced toxicity (Jain et al., 2005). One of the most commonly used methods to investigate drug release from liposomes is the dialysis method where drug loaded liposomes are placed in dialysis tubes and suspended in a beaker. However, often lack of IVIVC was observed with this method possibly due to violation of sink conditions. Shabbits et al. developed an in vitro release method using excess amounts of multilamellar vesicles (MLV) as “acceptors” for drug release from “donor” liposomes. They were able to mimic in vivo drug release closely using MLV based in vitro method which served as a lipid sink (Shabbits et al., 2002). Level A correlation on MVL liposomes was demonstrated by Zhong et al. where IVIVC was achieved using plasma as an in vitro release medium for drug release (Zhong et al., 2005). However, the use of IVIVC for liposomal products for biowaivers and bioequivalence studies might be difficult since these systems can be very complex. For example, stealth liposomes should remain stable in vivo, without any significant release of drug, until uptake into the cells of interest. As expected, such a release profile would be extremely difficult to mimic in vitro.

11.7

Emulsions

Emulsions are formed when two or more immiscible liquids with limited mutual solubility are mixed with a high energy input such as via ultrasonication, homogenization, or microfluidization. Due to their thermodynamic instability, the use of surfactants is required to improve their stability. Emulsion can be categorized as simple emulsions such as water-in-oil (w/o), or oil-in-water (o/w), or multiple emulsions water-in-oil-in-water (w/o/w) or oil-in-water-in-oil (o/w/o). The most

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commonly used clinical application of emulsions is for the delivery of parenteral nutrition for patients who can not absorb nutrients via the GI route. These nutrients include vitamins, minerals, amino acids, and electrolytes. Emulsions may also be formulated to deliver drugs with low water solubility, for example propofol formulated in o/w emulsion with soybean oil, glycerol and egg lecithin is currently on the market as a sedative-hypnotic agent (http://www.astrazenecaus. com/pi/diprivan.pdf). Other currently available emulsion products can be found in the Table 11.2. In vitro release of drugs from emulsion systems can be evaluated using the sample and separate method, dialysis and reversed dialysis methods. Since drug release from emulsions is often relatively rapid, the reversed dialysis is recommended so that sink conditions are not violated. In vivo pharmacokinetic profiles of drugs in emulsion formulations depends on the blood circulation time, the droplet size of the emulsion, the injection volume and the drug lipophilicity (Kurihara et al., 1996; Takino et al., 1994; Ueda et al., 2001). As described above emulsion formulations also suffer rapid uptake by the RES system. In order to improve blood circulation half-life, Reddy et al. investigated pegylation of etoposide emulsion. In vivo studies using a rat model, showed that pegylated emulsion exhibited a 5.5 times higher AUC compared to the etoposide commercial formulation. The effect of different oxyetylene moieties varied by size on the o/w emulsion blood circulation time was investigated. It was reported that blood circulation half-life was prolonged from approximately 10 min to 100 min when oxyetylene varied from 10 to 20, respectively. Reduction in the liver uptake was observed with emulsions prepared with 20 and higher oxyetylene moieties compared to those with ten oxyetylene moieties (Ueda et al., 2003).

11.8

Hydrogels, Implants

The advantages of hydrogels as depot formulations are their biocompatibility, water permeability, and injectability (in situ forming gels) at the site (i.e., tumor site) (Hoffman, 2001; Peppas et al., 2000). One disadvantage of hydrogels is that the drug release rate may not be manipulated, for example: fast release rate of hydrophilic drugs occurs from the hydrogels due to the hydrophilic environment within the hydrogel. Therefore, two phase systems were developed where delivery vehicles (liposomes or microspheres) were entrapped in the hydrogels to control drug release kinetics (Galeska et al., 2005; Moussy et al., 2003; Patil et al., 2004). Patil et al. (2004) were able to achieve in vitro and in vivo controlled release of dexamethasone from microspheres entrapped in a polyvinyl alcohol hydrogel and a linear in vitro–in vivo correlation of release rates. Lalloo et al. (2006) demonstrated in vitro and in vivo controlled release of chemotherapeutic topotecan from two phase systems where drug containing liposomes were entrapped in hydrogels. Longer tumor suppression was achieved using with this approach compared to drug alone.

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A linear IVIVC was established for methadone release from implants (Negrin et al., 2001). In this study drug release in vivo was calculated from the amount of drug remaining inside the implant. However, when in vivo methadone release was estimated by deconvolution from serum levels, deviations from linearity occurred at later time points. The authors confirmed the role of possible metabolic induction in the underestimation of in vivo release as a consequence of increased methadone clearance with time. Therefore, it should be noted that estimation of in vivo release by deconvolution might not be applicable when the drug absorption and disposition function is not linear and not constant with time (Negrin et al., 2004).

11.9

Dendrimers

Dendrimers are synthetic highly branched polymers with a central core with sizes in the nanometer range. The structure and branched topologies of dendrimers resembles a branched tree hence the name is derived from the Greek name dendra (meaning tree, tree-like structure). Dendrimers can be categorized based on the number of the branches they possess which are called generations (G-1, G-2, G3, etc.). The molecular weight, chemical composition and size of the dendrimers can be tightly controlled during synthesis of these polymers. Most commonly used dendrimers are based on polyamidoamines (PAMAM), polyamines, and polyesters (Frechet and Tomalia, 2002; Newkome et al., 2001). Dendrimers are ideal candidates as drug/gene delivery carriers, biological imaging agent carriers, and as scaffolds in tissue engineering due to their uniform size, monodispersity, water solubility, modifiable surface characteristics and high drug loading efficiencies (Kobayashi and Brechbiel, 2004; Kukowska-Latallo et al., 1996; Patri et al., 2002). In addition, the surface charge of these polymers can be manipulated to increase biocompatibility and decrease toxicity. For example, it has been shown that the cytotoxicity of cationic PAMAM dendrimers decreased when the surface charge was modified with the addition of lauroyl and PEG chains (Jevprasesphant et al., 2003). It has also been shown that dendrimers can be used as a multifunctional delivery platform loaded with therapeutics, targeting and imaging agents. PAMAM dendrimers loaded with methotrexate (MTX) as a chemotherapeutic, folate as a targeting agent, and fluorescein as an imaging agent accumulated preferentially approximately five times higher than the control in a mouse model with subcutaneous tumors (Kukowska-Latallo et al., 2005). Drugs can be either physically entrapped or conjugated with the dendrimer. However, it has been shown that MTX was readily released in saline when physically entrapped in dendrimers. This was attributed to weak interaction between MTX and the dendrimer when inter molecular forces are neutralized in the PBS solution. However, MTX was retained in the dendrimer and did not exhibit any premature release when conjugated to the dendrimer (Patri et al., 2005). Drug release from the dendrimers can be controlled by change in pH, for example ester terminated half generation PAMAM dendrimers did not release any drugs at pH 7.0. However, drug release occurred at pH 2.0 when internal tertiary amines were protonated (Twyman et al., 1999). In chemotherapy, this pH responsive release mechanism is desired since

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drug release occurs only in the acidic microenvironment of the tumor tissue not in the systemic circulation. Another example of controlled drug release from dendrimers was the sustained release of indomethacin from dentritic unimolecular micelles (Liu et al., 2000). Similar to other MR release products mentioned above, different strategies such as modification of the dendrimer surface with polyethyleneoxide, PEG chains, have been successfully applied to decrease their RES uptake (Gillies and Frechet, 2002; Kim et al., 2004; Malik et al., 2000; Wang et al., 2005; Yang and Lopina, 2006). Neutral, generation four (G4) polyester dendrimers did not accumulate in any organ preferentially and they exhibit rapid renal clearance. It was noted that the low molecular weight and compact structure of the neutral G4 dendrimers could pass glomerular filtration (PadillaDeJesus et al., 2002). In a follow-up study, it was shown that highly branched dendrimers (Generation 3) exhibited greater bioavailability and lower renal clearance than that of the compact dendrimers (Generation 2) where the molecular weights of these dendrimers were approximately the same (Gillies et al., 2005). The potential value of dendrimers as a delivery vehicle is promising since biodistribution and pharmacokinetic properties can be manipulated by changing the dendrimer size and conformation (Lee et al., 2005).

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12 In Vitro–In Vivo Correlation in Dosage Form Development: Case Studies Shoufeng Li, Alan E. Royce, and Abu T. M. Serajuddin

12.1

Introduction

In vitro and in vivo correlation (IVIVC) refers to a predictive relationship of the in vitro properties of drug substances or dosage forms with their in vivo performance. For orally administered drug products, it is usually a correlation between the extent or rate of dissolution of a dosage form and its pharmacokinetic parameters, such as rate, duration, and extent of drug absorption. The physicochemical properties of dosage forms influence their in vivo performance in many different ways (Li et al., 2005). Through the establishment of a definitive relationship between certain physicochemical properties of a dosage form with the in vivo appearance of its active component, one can establish in vitro testing criteria which will predict, its in vivo performance. Although IVIVC may be applied to many different types of dosage forms, including topical patches, various injectable forms like microparticulates and depot systems, and different inhalation formulations, the primary purpose of this chapter is to illustrate how the IVIVC concept can be applied to the development of oral dosage forms. IVIVC of oral dosage forms can also help in setting dissolution specifications and in applying in vitro data as surrogates for bioequivalence testing in case of certain pre- and postapproval changes (Center for Drug Evaluation and Research, US FDA, 1997). However, the drug product development is a continuous process with increasing physicochemical and pharmacokinetic data being available as it progresses from the early-stage to the late-stage development including life cycle management (LCM). For this reason, the IVIVC should also be a continuous process with more predictability built into it as the product development progresses. IVIVC has normally been studied for prototype formulations or finished dosage forms, where it involves at least two different formulations and a reference treatment, such as a solution or immediate-release formulation. The formulation properties used must have significantly different in vitro or in vivo profiles (>10%). The correlation established based on such studies may be categorized into Levels A, B, and C (United States Pharmacopeial Convention, Inc., 1988),

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and these categories have been discussed in detail in other chapters in this book. Briefly, a Level A IVIVC is generally linear and represents a point-to-point relationship between in vitro dissolution rate and in vivo input rate (e.g., the in vivo dissolution rate of the drug from the dosage form). A Level A relationship may generate predictable plasma profile, including Cmax , AUC, Tmax , shape of profile and elimination half-life. It is most useful in product development since it may be used for biowaiver based on in vitro dissolution data to qualify changes occurred during manufacture. In a Level B IVIVC, the mean in vitro dissolution time is compared either to the mean residence time or the mean dissolution time in the gastrointestinal fluid. However, the entire plasma concentration profile cannot be predicted based on in vitro dissolution data, and, therefore, the benefit of a Level B correlation is limited and it is not accepted by Health Agencies for biowaivers. A Level C IVIVC establishes a single point relationship between a dissolution parameter and a pharmacokinetic parameter, and it does not reflect the complete shape of the plasma concentration versus time curve. A typical example of a Level C IVIVC is the establishment of the correlation between amount of drug released at a certain time point and Cmax . Level C correlation can be useful to rank order different formulation principles; however, unless a multiple Level C correlation can be established, the usefulness of Level C correlation in predicting full in vivo performance may be limited. Although the IVIVC of drug products based on above guidelines is important to establish specifications for drug products and meet regulatory requirements for product approvals, Li et al. (2005) argued that in order to optimize performance of oral dosage forms, the possible relationship between in vitro physicochemical attributes and in vivo performance must be considered in all phases of drug product development. The product should starting from the identification of new chemical entities for development and continue to the approval of drug products for marketing and even through postapproval changes. Unless the in vitro–in vivo relationships are built into the development of drug products, methods such as dissolution testing may not be relevant to the in vivo performance of drug products. To address this issue, a four-tier approach, as described below, may be applied for during drug product development process.

12.2 IVIVC in Drug Product Development: A Four-Tier Approach The application of IVIVC is an evolving process during product development. The scope of IVIVC changes as a NCE progresses from drug discovery phase to early development, full development, and finally to LCM. As shown in Scheme 12.1, the present authors believe a four-tier approach to IVIVC during different phases of drug product development based on the availability of physicochemical and pharmacokinetic data should be utilized. How these tiers may be related to different

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TIER 1 In Silico Simulation physicochemial data available

TIER 2 In vivo data available from Dog, Rats

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TIER 4 LCM - IR, MR formulation development

TIER 3 Human PK data available

LCM=Life Cycle Management; IR= Immediate Release; MR=Modified Release S CHEME 12.1. A four-tier approach to IVIVC. LCM, life cycle management; IR, immediate release; MR, modified release

Discovery

Early Development

Full Development

Life Cycle Management

In Silico Simulation Animal PK testing Human PK testing Immediate Release Products Modified Release Products & Drug Delivery Systems (DDS) In Vitro – In Vivo Correlation TIER 1

TIER 2

TIER 3

TIER 4

F IGURE 12.1. Discovery and development phases of new chemical entity (NCE) and application of IVIVC in drug development process

stages of drug product development is depicted in Fig. 12.1, and the scopes of IVIVC at different tiers are discussed below. Tier 1. This tier usually encompasses the drug discovery and the early preclinical phases where the PK data for a compound is not available. Although many in vivo studies are carried out in the drug discovery stage, the main focus at this time is to determine pharmacological efficacy and safety of the compound. Since a large number of compounds are studied at this stage to select an NCE for development and only limited physicochemical data are available, the IVIVC at this time is usually conducted through in silico simulation of structural properties of a molecule or high-throughput experimental data generated

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for initial characterization of a compound. The correlation helps in establishing whether a compound is developable or not (Venkatesh and Lipper, 2000; Pudipeddi et al., 2006). Tier 2. Following the selection of a NCE and during preclinical development leading to the initiation of proof of concept (PoC) or Phase I studies, pharmacokinetic studies in animal models are carried out with emphasis on evaluating biopharmaceutical properties of the NCE and dosage form design strategies. In addition, different physical forms, salts and the particle size of the drug substance can be tested in animal models, which provides the first opportunity to correlate the in vitro measurement, i.e., dissolution of the drug substance, with its in vivo performance, such as Cmax , AUC, or in vivo absorption profiles. The available PK data also provide the first opportunity to develop a biorelevant dissolution method for the compound. Li et al. (2005) proposed a decision tree for selecting dissolution media for compounds of different BCS categories. As shown in Scheme 12.2, the decision tree may be simplified for selecting appropriate dissolution media during a Tier 2 IVIVC in early development. This proposal includes testing of different formulations with different in vitro drug release characteristics, such as different salt forms, different particle size distributions, or different formulation principles, in both in vitro and in vivo settings, and then feed all of the in vitro and in vivo data into a simulation software, such as GastroPlusTM (Simulations

Developability Assessment

In silico simulation

Compound X

Different salt forms; Different particle sizes; Different formulations from various process, wet/dry granulation, direct compression or others

In Vitro Testing pH 1.0, 4.5, 6.8 see disso decisior tree5

Choose only meaningful variants to test in vivo

In Vivo Testing different salts, formulations, or particle size

Feed in vitro/in vivo data into simulation model

Simulation validate by in vivo data

Data help to design in vitro testing

Variants with different in vitro profiles different in vivo?

YES

Select or modify dissolution method biorelevant

NO

Choose dissolutior method - less discriminating

Recommendation for dissolution method

S CHEME 12.2. Decision tree for implementing IVIVC in early product development

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Plus, Lancaster, CA). Since formulation development and optimization at later stages of drug product development often rely on the dissolution method established at an earlier stage, it is essential that dissolution method selection in Tier 2 is as meaningful and as biorelevant as possible. If the PK results of different formulations with similar dissolution profiles turn out to be similar, a less discriminating dissolution method may be used. On the other hand, a discriminating and biorelevant dissolution method may be developed if the PK results of different formulations are also different. Tier 3. The validity of the dissolution method developed in Tier 2 can be further studied in full development, once human pharmacokinetic data are available. Analysis of all available PK and in vitro data must be carried out to determine whether the method needs modification for a better IVIVC. This involves a crossfunctional team of scientists from areas of formulation development, dissolution testing, clinical pharmacokinetic testing, and PK modeling. Tier 4. In the LCM stage of drug development, a large amount of clinical pharmacokinetic data are already available. An IVIVC based on such data has to be considered as part of the development strategy for LCM dosage forms such as modified release oral products or alternative delivery systems including parenteral depot, transdermal patch, etc. The FDA guideline for modified release oral dosage forms clearly defines acceptance criteria for successful IVIVC (Center for Drug Evaluation and Research, 1997). Case studies at different tiers of in vitro–in vivo correlation are presented in the following section.

12.3

Case Studies

12.3.1 Tier 1 – Discovery and Early Preclinical Development: Assessing Developability and Formulation Principles During the discovery and the early preclinical development stages of a compound intended for oral administration, the major concern is whether it would have acceptable oral absorption or not, since this would determine its developability. The first IVIVC assessment conducted at this time is the classification of the compound according to the Biopharmaceutical Classification System (BCS) proposed by Amidon et al. (1995). This identifies potential hurdles in the drug product development and indicates whether any special dosage form design or drug delivery considerations would be necessary. High-throughput methods are in place in most pharmaceutical companies for the generation of experimental data, such as solubility, partition coefficient, membrane permeability, etc., to enable BCS classification. Stability of compounds to certain enzymes present in gut and plasma that could be indicative of their in vivo performance is often determined at this time. There are numerous reports in the literature on the application of BCS classification in the early phase of drug development.

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Another simplistic but useful approach in predicting potential drug absorption issues is the calculation of maximum absorbable dose (MAD) (Curatolo, 1998) which has been presented elsewhere in this book: MAD = S × K a × SIWV × SITT, where S is solubility (mg/mL) at pH 6.5, K a is transintestinal absorption rate constant (min−1 ) based on rat intestinal perfusion experiment, SIWV is small intestinal water volume (250 mL), and SITT is the small intestinal transit time (4 h). Chiou et al. (Chiou et al. 2000) demonstrated that there is a good correlation between absorption rates in humans and rats with a slope near unity. One limitation of the MAD calculation is that only the aqueous solubility in pH 6.5 buffer is taken into consideration, and no considerations are made for possible solubility enhancement by bile salts, surfactants, lypolytic products, etc., present in GI fluids. Although the generation of rat perfusion data in the early development phase is very helpful for the purpose of IVIVC, another limitation could be that the absorption rate constants in rats may not be determined or reliable values may not be available for the calculation of MAD. As shown by Curatolo (1998), two different estimated K a values, one high and one low, may be used under such a circumstance, where MAD will be a dose range instead of a single value. Both the BCS classification and the MAD calculation rely on the solubility of a drug substance. Although the dissolution rate is dependent on both solubility and surface area, the surface area of drug substance, which is dependent on particle size, is not considered. The effect of particle size on in vivo drug absorption has been discussed by Johnson (1996), where the effect of particle size on absorption over a range of important variables, including dose, solubility and absorption rate constant, was simulated. For example, with a fixed absorption rate constant of 0.001 min−1 , the relationship between dose and solubility as a function of particle size change could be simulated. In general, the relative effect of particle size on the percent of dose absorbed decreases with an increase in solubility, and particle size becomes practically irrelevant for drugs at a solubility of 1 mg/mL for a dose of 1 mg. Again, when reliable values for absorption rate constants are not available, multiple K a values may be applied for a general estimate of the relative influence of particle size. The proper in vivo assessment of oral absorption requires much time and resource. Therefore, the PK data for IVIVC, whether animal or human, are not usually available during early preclinical development. For this reason, it is desirable to utilize in silico modeling approaches for an early assessment of absorbability and to extrapolate results to situations where experimental data are not available. As reported earlier (Li et al., 2005), there are currently two in silico approaches for the prediction of oral absorption, statistical models and mechanism-based models. The former is based on a statistical relationship between inputs, typically molecular descriptors derived from molecular structures, and outputs, which could be estimates of oral absorption. Mechanism-based models rely on a good understanding of absorption processes including physiology, GI dissolution, transit, and permeation. The following are a few case studies based on

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the experience of present authors on mechanism-based modeling; the compounds used are depicted as numbers since detailed chemical structures are not relevant to the objective of this paper and, in some cases, the results are proprietary in nature. One mechanism-based model that gained popularity in recent years is TM GastroPlus (SimulationsPlus, Lancaster, CA), which simulates and models the gastrointestinal absorption processes based on an “Advanced Compartmental Absorption and Transit” (ACAT) model (Yu et al., 1996a). Inputs to the software include (Li et al., 2005; Agoram et al., 2001): (a) Oral dose (b) Physicochemical properties (solubility-pH profile, intestinal permeability, etc.) (c) Physiological properties (species, GI transit time, GI pH, food status, etc.) (d) Formulation properties (release profile, particle size, etc.) (e) Pharmacokinetic parameters (Vd , CL, microscopic kinetic rate constants, etc.) (optional) All input parameters in the GastroPlusTM simulation are under user control, and the values may change at different stages of the development of a compound. At discovery and preclinical development stages, some of the values could be estimates or based on library data for similar compounds, or from in silico calculation. The output includes: (a) Fraction of oral dose absorbed (concentration–time profiles in all GI compartments; fractions absorbed from each GI compartment) (b) Parameter sensitivities (to answer “what-if” questions) (c) Plasma concentration–time profiles (if the PK parameters are provided) (optional) In one relatively simple application of GastroPlusTM , it was asked whether or not the mean particle size (D50 ) requirement of Compound I (aqueous solubility: >100 mg/mL) may be relaxed from 35 µm to approximately 100 µm without affecting its oral bioavailability. A simulation suggested that the extent of absorption is not sensitive to changes in particle size in the range of 35–250 µm (Fig. 12.2). This facilitated decision making with respect to dosage form design of Compound I without having to resort to in vivo experiments and focused the attention of formulators on the impact of particle size on processibility of dosage forms rather than their bioavailability. Although simulation is not a replacement for definitive scientific experiments, it provides valuable insight on what one would expect in vivo based on the physicochemical properties of a compound. For instance, during the development of a neutral compound (Compound II) with a solubility of 1 µg/mL and high effective permeability (3.0 × 10−4 cm/s), a simulation at different doses and different particle sizes (Fig. 12.3) provided insight into possible strategies for the development of its dosage forms. At a dose of 100 mg, absorption of the compound

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S. Li et al.

120

Percent Absorbed (%)

100

80

60

40

20

50

100

0 150

200

250

300

Particle Size (mm) F IGURE 12.2. Simulation on the impact of particle size (35–250 µm) on oral absorption of compound I using GastroPlusTM . Compound I has high aqueous solubility of >100 mg/mL

100

Percent Absorption (%)

1 mg 25 mg

80

100 mg

60

40

20

0 0.1

1

10

100

1000

Particle Size (um) F IGURE 12.3. Simulation for percent absorption of Compound II at doses of 1, 25, and 100 mg over particle size range of 0.5–100 µm. Compound II is a neutral molecule with a pH-independent aqueous solubility of 1 µg/mL

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is practically negligible over the particle size range of 0.5–100 µm, indicating that solubility is the limiting factor in absorption of the compound at such a dose and particle size reduction would not be helpful in improving absorption. However, at a dose of 1 mg, a dramatic shift in the dependency of absorption from solubility to particle size is evident from the simulation. Here, the use of micronization or nanoparticle system could provide an advantage. Ultimately, a solid dispersion whereby the drug was liberated in aqueous media as submicron particles was selected for its dosage form development in the expected dosage range from 5 to 80 mg (Dannenfelser et al., 2004). Although one may not be able to obtain an accurate estimate of dose of a new chemical entity (NCE) until very late in development, formulation scientists could utilize simulation data in a number of ways. At a relatively low dose range, particle size reduction and improvement of wetting properties of drug substances may be quite effective. However, if the molecule is ionizable (for example, a weak base), one may like to choose a salt form that could provide much higher dissolution rate than the free base.

12.3.2 Tier 2 – Preclinical Product Development: Selection of a Meaningful Dissolution Method One of the questions that are often asked is whether different release properties in vitro will result in different in vivo absorption rates. It is not uncommon that different formulation approaches such as dry blend/direct compression, dry granulation (roller compaction), or wet granulation may result in formulations with different in vitro release characteristics. As shown in Fig. 12.4, Compound III had different in vitro release profiles when dry blend and wet granulation formulations were tested in vitro. In early time points, the difference was as large as 30%. However, when these formulations were tested in vivo in dog, comparable pharmacokinetic profiles were achieved (Fig. 12.5), indicating that the difference in in vitro dissolution would not have any significant impact on the in vivo performance of the drug product. This would save an enormous amount of time and effort by avoiding later development of a method to match the dissolution profile of the original formulation. In addition, this type of information will be very useful in selecting a biorelevant dissolution method; in this case, a much less discriminating method would be optimal. As the development phase moves forward, typically to PoC (Proof of Concept) or Phase I stage, the lead candidate is characterized more thoroughly for its physicochemical properties and developability. A preclinical PK study in animal model is typically performed prior to the Phase I clinical trial with problematic compounds. A well-designed preclinical PK study, with input from formulation and clinical experts, may provide an opportunity to set the parameters for drug substance properties, such as particle size, salt forms as well as for formulations. Combined with simulation effort, it would also provide an opportunity to identify the rate-limiting factors for absorption. Further, in vivo data obtained at this stage

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Cumulative Percent Released

100

80

60 Dry blend, pH 4.0 Acetate buffer 40

Wet granulation, pH 4.0 Acetate buffer Dry blend, pH 2.0 buffer Wet granulation, pH 2.0 buffer

20

0 0

10

20

30

40

Time (minutes)

Plasma concentration of Compound III (ng/mL)

F IGURE 12.4. In vitro dissolution profiles of compound III capsule formulations at 0.01N HCl (pH 2.0) and 0.05 M acetate buffer (pH 4.5) using USP I basket method at 100 rpm (37 ◦ C)

800 Intravenous Dry blend Wet granulation

600 400 200 0 0

8

16

24

32

40

48

Time (h)

F IGURE 12.5. In vivo pharmacokinetic profiles (Cp − t) of Compound III in dogs for wet granulation and dry blend formulations at 10 mg/kg. The plasma profile after i.v. administration at 3 mg/kg is also shown

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may be used to better justify the development of a biorelevant dissolution method. Among factors that determine the rate and extent of drug absorption following oral administration, dissolution of the solid drug into solution is of primary importance in the drug release/absorption process. Factors affecting drug dissolution has been extensively reviewed by Horter and Dressman (1997) as well as Li et al. (2005). Prediction of the in vivo performance of weak bases and their salts could be challenging due to the kinetic nature of the dissolution of salts and the potential for precipitation into their free base forms. In another example, formulations of the free base and different salt forms of a weakly basic compound (Compound IV) were tested in dogs for bioavailability. The formulations tested were an intraveneous formulation containing 3 mg/mL DiHCl salt in 20% HP-β-CD, an oral 0.5% CMC suspension of free base at 2 mg/mL formulation, and drug blend capsule formulations of dihydrochloride (diHCl) and tartrate salts (Table 12.1). The results are summarized in Fig. 12.6. Absolute bioavailability of the diHCl salt had TABLE 12.1. Summary of formulations administered to dogs for Compound IV Dosea (mg/kg)

Compound I

Volume/no. capsule

Concentration

Formulation

3

di HCl salt

1 mL/kg

3 mg/mL

Oral

10

Free base

5 mL/kg

2 mg/mL

Oral Oral

10 10

diHCl salt Tartrate salt

1 cap/dog 1 cap/dog

Not applicable Not applicable

Solution in 20% hydroxypropylbetacyclodextrin aqueous solution Suspension in 0.5% CMC aqueous solution Powder-in-capsule Powder-in-capsule

Route Intravenous

Plasma Concentration (ng/mL)

a All doses are expressed as free base equivalent

6000

Free base susp. (10 mg / kg)

5000

Tartrate in cap. (10 mg/ kg)

4000

DiHCl in cap. (10 mg/ kg)

3000 2000 1000 0 0

20

40

60

Time (h) F IGURE 12.6. In vivo pharmacokinetic profiles (Cp − t) of free base and two different salt forms of Compound IV in dogs: free base suspension, capsule formulation of dihydrochloride (diHCl) and tartrate salt at 10 mg/kg

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S. Li et al.

a mean value of 84%, indicating close to complete absorption. Percent bioavailability of the salt forms and free base suspension was in the order of diHCl (84%) > tartrate (48%) > free base (12%). Both salt forms of Compound IV clearly demonstrated their in vivo advantages over that of the free base, while the diHCl salt had higher bioavailability than the tartrate salt. When in vitro dissolution profiles of the three oral formulations of Compound IV, i.e., free base suspension, diHCl salt and tartrate salt capsules were determined at pH 2 (Fig. 12.7a),

(a) Cumulative Percent Released

100 80 di HCl 60

free base suspension tartrate

40 20 0 0

10

20

30

40

50

60

Time (minutes) (b)

Cumulative Percent Released

100 80 free base di HCl tartrate

60 40 20 0 0

10

20

30

40

50

60

Time (minutes)

F IGURE 12.7. In vitro dissolution profiles of Compound IV suspension (free base) and capsule formulations (diHCl and tartrate salts) at (a) 0.01N HCl (pH 2.0) and (b) 0.05 M Acetate buffer (pH 4.0) using USP I basket method at 100 rpm (37 ◦ C)

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comparable profiles are observed for diHCl and tartrate salts, whereas the release profile of the free base suspension was low (4 mg/mL) and high permeability (absolute bioavailability >90%, Caco-2 permeability Papp > propranol) (BCS Class I), as determined by methods defined by FDA biowaiver guidance (Center for Drug Evaluation and Research, US FDA, 2000). Two MR forms of Compound VI were tested, together with an IR solution, in a cross-over Clinical Pharmacology study. The same batches were used for the in vitro dissolution testing. The dosage forms used to establish the in vitro–in vivo correlation and for the internal validation are listed in Table 12.3. The plasma concentration profiles of two disintegrating type of tablets (SR1, SR2) with a target of 80% release after 2–3 (MR1) and 4–6 (MR2) h, respectively, and an orally administered solution were used as a basis for developing a Level A IVIVC. The in vivo plasma concentration profiles of the two MR formulations and the solution are presented in Fig. 12.11.

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TABLE 12.3. List of formulations tested in clinical pharmacology studies

Plasma Concentration (mg /mL)

Strength/name

Study dose(s) (mg)

500 mg/MR tablet (MR1)

500

500 mg/MR tablet (MR2)

500

Solution

500

Characteristics Target: disintegrating MR variant 80% release after 2–3 h Target: disintegrating MR variant 80% release after 4–6 h Powder in tap water

10 9 8 7 6

MR1

5

MR2

4

IR Solution

3 2 1 0 0

6

12

18

24

30

36

42

48

54

60

66

72

78

Time (h) F IGURE 12.11. Compound VI mean plasma concentrations after 500 mg oral administration of a solution and two modified release formulations, MR1 and MR2 tablets, in healthy volunteers. MR1 is a disintegrating variant with 80% release after 2–3 h, and MR2 is a disintegrating variant with 80% release after 4–6 h

Dissolution rates of MR1 and MR2 (500 mg tablets) of Compound VI were tested using USP 711 Apparatus 2 (Paddle Method) at 50 rpm at 37 ◦ C. The medium used for the test is 0.05 M phosphate buffer pH 6.8 (1,000 mL). All tests were performed with N = 12. The average data of the in vitro dissolution testing were used to develop the IVIVC and internal prediction. A common approach for deconvolution is to use an orally administered solution as a unit impulse function to obtain the input rate for a SR form. The resulting cumulative input rate represents the kinetics of the in vivo dissolution and can be compared with in vitro dissolution profiles to obtain an in vitro–in vivo correlation. A Level A correlation can be achieved when the curves are superimposable or can be made to be superimposable by the use of a constant time scaling factor for all formulations. The use of time scale factor is acceptable by the regulatory agency according to the Guidance issued by FDA (Center for Drug Evaluation and Research (CDER), 1997).

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% Dissolved or Absorbed

120 100 80 MR1, in vitro dissolution

60 MR2, in vitro dissolution

40

MR1 in vivo deconvolution MR2 in vivo deconvolution

20 0 0

5

10

15

20

Time (h) F IGURE 12.12. Mean in vitro dissolution profiles of Compound VI MR1 and MR2 tablets (n = 12) with USP I, paddle method at 50 rpm (37◦ C) and comparison of the in vitro and in vivo dissolution profiles for MR tablets

For Compound VI, a Wagner–Nelson method may be used to obtain the in vivo dissolution profiles since, as shown in Fig. 12.11, it follows simple onecompartment pharmacokinetic model. The in vivo dissolution data thus generated are then plotted against in vitro dissolution profiles in Fig. 12.12. In Fig. 12.12, a rank order correlation exists for the two MR formulations. However, not surprisingly, there appears to be some time difference between the in vivo and in vitro dissolution profiles. This type of time scale difference has been reported by other authors as well (Li et al., 2007; Corrigan et al., 2003; Qiu et al., 2003) and is described in the FDA guidance for IVIVC (Center for Drug Evaluation and Research, 1997). It is an acceptable practice to mathematically scale in vitro dissolution profile to match in vitro dissolution profile or to modify in vitro dissolution condition if necessary. In this case, the in vitro dissolution profiles were time-scaled according equation below: Tin vivo = 1.18Tin vitro. As a result, the correlation between in vitro and in vivo dissolution improved, which is shown in Fig. 12.13. In order to establish an in vitro–in vivo correlation, the fraction dissolved in vivo was plotted against the fraction dissolved in vitro and a linear regression was applied to find a quantitative relationship. As shown in Fig. 12.14, a linear relationship of close to unity can be established for Compound VI MR formulations with a correlation coefficient of 0.99. According to FDA guidance (Center for Drug Evaluation and Research, 1997), the predictability of the developed IVIVC model is an integral part of the IVIVC development. During the internal validation stage, the predicted plasma profiles of the formulations that were used to develop the IVIVC model were evaluated

12. In Vitro–In Vivo Correlation in Dosage Form Development

379

120

% Dissolved or Absorbed

100

80

60

MR1, in vitro dissolution_time shift

40

MR2, in vitro dissolution_time shift

20

MR1, in vivo deconvolution MR2, in vivo deconvolution

0 0

5

10

15

20

Time (h) F IGURE 12.13. Comparison of the time-scaled in vitro dissolution profile and in vivo dissolution profiles of compound VI MR tablets

y = 0.9694x + 1.4569 R2 = 0.9929

Fraction Absorbed In Vivo

120 100 80 60 40 20 0 0

50

100

150

Fraction Dissolved In Vitro F IGURE 12.14. Relationship between fraction of in vitro and in vivo dissolution data of compound VI formulations

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S. Li et al.

TABLE 12.4. Comparison of experimental and predicted Cmax and AUC values for modified released formulations of compound VI Compound VI MR 1 MR 2

AUC (mg h/mL) Experimental Predicted 151.60 121.41

143.20 123.60

% PE 5.54 1.80

Cmax (mg/mL) Experimental Predicted 7.44 4.70

7.20 4.58

% PE

Requirement (%)

3.23 2.55

Biopharmaceutics Applications in Drug Development Lawrence - PDFCOFFEE.COM (2024)

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