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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Explains how to perform and analyze the results of the latest physicochemical methods With this book as their guide, readers have access to all the current information needed to thoroughly investigate and accurately determine a compound's pharmaceutical properties and their effects on drug absorption. The book emphasizes oral absorption, explaining all the physicochemical methods used today to analyze drug candidates. Moreover, the author provides expert guidance to help readers analyze the results of their studies in order to select the most promising drug candidates. This Second Edition has been thoroughly updated and revised, incorporating all the latest research findings, methods, and resources, including: * Descriptions and applications of new PAMPA models, drawing on more than thirty papers published by the author's research group * Two new chapters examining permeability and Caco-2/MDCK and permeability and the blood-brain barrier * Expanded information and methods to support pKa determination * New examples explaining the treatment of practically insoluble test compounds * Additional case studies demonstrating the use of the latest physicochemical techniques * New, revised, and expanded database tables throughout the book Well over 200 drawings help readers better understand difficult concepts and provide a visual guide to complex procedures. In addition, over 800 references serve as a gateway to the primary literature in the field, facilitating further research into all the topics covered in the book. This Second Edition is recommended as a reference for researchers in pharmaceutical R&D as well as in agrochemical, environmental, and other related areas of research. It is also recommended as a supplemental text for graduate courses in pharmaceutics.
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Table of Contents
Cover
Title page
Copyright page
DEDICATION
PREFACE
PREFACE TO THE FIRST EDITION
ABBREVIATIONS
NOMENCLATURE
COMMERCIAL TRADEMARKS
1 INTRODUCTION
1.1 BULLDOZER SEARCHING FOR A NEEDLE IN A HAYSTACK?
1.2 AS THE PARADIGM TURNS
1.3 SCREEN FOR THE TARGET OR ADME FIRST?
1.4 ADME AND MULTIMECHANISM SCREENS
1.5 ADME AND THE MEDICINAL CHEMIST
1.6 THE “ABSORPTION” IN ADME
1.7 IT IS NOT JUST A NUMBER, IT IS A MULTIMECHANISM
2 TRANSPORT MODEL
2.1 PERMEABILITY–SOLUBILITY–CHARGE STATE AND PH-PARTITION HYPOTHESIS
2.2 PROPERTIES OF THE GASTROINTESTINAL TRACT (GIT)
2.3 PH MICROCLIMATE
2.4 INTRACELLULAR PH ENVIRONMENT
2.5 TIGHT JUNCTION COMPLEX
2.6 STRUCTURE OF OCTANOL
2.7 BIOPHARMACEUTICS CLASSIFICATION SYSTEM
3 pKa DETERMINATION
3.1 CHARGE STATE AND THE pKa
3.2 METHODS OF CHOICE FOR THE DETERMINATION OF THE pKa
3.3 TITRATION WITH A GLASS-MEMBRANE PH ELECTRODE
3.4 EQUILIBRIUM EQUATIONS AND THE IONIZATION CONSTANT
3.5 “PURE SOLVENT” ACTIVITY SCALE
3.6 IONIC STRENGTH AND DEBYE–HÜCKEL/DAVIES EQUATION
3.7 “CONSTANT IONIC MEDIUM” ACTIVITY SCALE
3.8 TEMPERATURE DEPENDENCE OF pKa VALUES
3.9 ELECTRODE CALIBRATION AND STANDARDIZATION
3.10 BJERRUM PLOT: MOST USEFUL GRAPHICAL TOOL IN pKa ANALYSIS
3.11 COSOLVENT METHODS FOR pKa DETERMINATION OF PRACTICALLY INSOLUBLE SUBSTANCES
3.12 OTHER METHODS FOR pKa MEASUREMENT
3.13 pKa MICROCONSTANTS
3.14 pKa COMPILATIONS
3.15 pKa PREDICTION PROGRAMS
3.16 DATABASE OF pKa (25°C AND 37°C)
APPENDIX 3.1 QUICK START: DETERMINATION OF THE pKa OF CODEINE
APPENDIX 3.2 TUTORIAL FOR MEASUREMENTS WITH GLASS-MEMBRANE PH ELECTRODE
APPENDIX 3.3 PH CONVENTION ADOPTED BY IUPAC AND SUPPORTED BY NIST
APPENDIX 3.4 LIQUID-JUNCTION POTENTIALS (LJP)
APPENDIX 3.5 pKa REFINEMENT BY WEIGHTED NONLINEAR REGRESSION
APPENDIX 3.6 MOLALITY TO MOLARITY CONVERSION
4 OCTANOL–WATER PARTITIONING
4.1 OVERTON–HANSCH MODEL
4.2 TETRAD OF EQUILIBRIA
4.3 CONDITIONAL CONSTANTS
4.4 log P DATA SOURCES
4.5 log D LIPOPHILICITY PROFILE
4.6 ION-PAIR PARTITIONING
4.7 MICRO-log P
4.8 METHODS FOR log P DETERMINATION
4.8.3 OTHER log P METHODS
4.9 DYRSSEN DUAL-PHASE TITRATION log P METHOD
4.10 IONIC STRENGTH DEPENDENCE OF log P
4.11 TEMPERATURE DEPENDENCE OF log P
4.12 CALCULATED VERSUS MEASURED log P OF RESEARCH COMPOUNDS
4.13 log D VERSUS PH CASE STUDY: PROCAINE STRUCTURAL ANALOGS
4.14 DATABASE OF OCTANOL–WATER log PN, log PI, AND log D7.4
5 LIPOSOME–WATER PARTITIONING
5.1 BIOMIMETIC LIPOPHILICITY
5.2 TETRAD OF EQUILIBRIA AND SURFACE ION-PAIRING (SIP)
5.3 DATA SOURCES
5.4 LOCATION OF DRUGS PARTITIONED INTO BILAYERS
5.5 THERMODYNAMICS OF PARTITIONING: ENTROPY- OR ENTHALPY-DRIVEN?
5.6 ELECTROSTATIC AND HYDROGEN BONDING IN A LOW DIELECTRIC MEDIUM
5.7 WATER WIRES, H+/OH− CURRENTS, AND PERMEABILITY OF AMINO ACIDS AND PEPTIDES
5.8 PREPARATION METHODS: MLV, SUV, FAT, LUV, ET
5.9 EXPERIMENTAL METHODS
5.10 PREDICTION OF log PMEM FROM log POCT
5.11 log DMEM, DIFF log PMEM, AND PREDICTION OF FROM
5.12 THREE INDICES OF LIPOPHILICITY: LIPOSOMES, IAM, AND OCTANOL
5.13 GETTING IT WRONG FROM ONE-POINT log DMEM MEASUREMENT
5.14 PARTITIONING INTO CHARGED LIPOSOMES
5.15 SHIFTS IN CHARGED LIPOSOMES AND MICELLES
5.16 PREDICTION OF ABSORPTION FROM LIPOSOME PARTITION STUDIES?
5.17 DATABASE OF log PMEM AND
6 SOLUBILITY
6.1 IT’S NOT JUST A NUMBER
6.2 WHY IS SOLUBILITY MEASUREMENT DIFFICULT?
6.3 MATHEMATICAL MODELS FOR SOLUBILITY–PH PROFILES
6.4 EXPERIMENTAL METHODS
6.5 CORRECTION FOR THE DMSO EFFECT BY THE “Δ-SHIFT” METHOD
6.6 CASE STUDIES (SOLUBILITY–PH PROFILES)
6.7 LIMITS OF DETECTION—PRECISION VERSUS ACCURACY
6.8 DATA SOURCES AND THE “IONIZABLE-DRUG PROBLEM”
6.9 DATABASE OF LOG S0
7 PERMEABILITY—PAMPA
7.1 PERMEABILITY IN THE GASTROINTESTINAL TRACT
7.2 HISTORICAL DEVELOPMENTS IN PERMEABILITY MODELS
7.3 RISE OF PAMPA—A USEFUL TOOL IN EARLY DRUG DISCOVERY
7.4 PAMPA-HDM, -DOPC, -DS MODELS COMPARED
7.5 MODELING BIOLOGICAL MEMBRANES
7.6 PERMEABILITY–PH RELATIONSHIP AND THE MITIGATING EFFECT OF THE AQUEOUS BOUNDARY LAYER
7.7 -OPTIMIZED DESIGN (POD)
7.8 COSOLVENT PAMPA
7.9 UV VERSUS LC/MS DETECTION
7.10 ASSAY TIME POINTS
7.11 BUFFER EFFECTS
7.12 APPARENT FILTER POROSITY
7.13 PAMPA ERRORS: INTRA-PLATE AND INTER-PLATE REPRODUCIBILITY
7.14 HUMAN INTESTINAL ABSORPTION (HIA) AND PAMPA
7.15 PERMEATION OF PERMANENTLY CHARGED MOLECULES
7.16 PERMEATION OF ZWITTERIONS/AMPHOLYTES—IN COMBO PAMPA
7.17 PAMPA IN FORMULATION: SOLUBILIZING EXCIPIENT EFFECTS
7.18 DATABASE OF DOUBLE-SINK PAMPA log P0, , AND
APPENDIX 7.1 QUICK START: DOUBLE-SINK PAMPA OF METOPROLOL
APPENDIX 7.2 PERMEABILITY EQUATIONS
APPENDIX 7.3 PAMPA PARAMEMBRANE WATER CHANNELS
8 PERMEABILITY: Caco-2/MDCK
8.1 PERMEABILITY IN THE GASTROINTESTINAL TRACT
8.2 CELL-BASED IN VITRO PERMEABILITY MODEL
8.3 IN SITU HUMAN JEJUNUM PERMEABILITY (HJP) MODEL
8.4 PASSIVE INTRINSIC PERMEABILITY COEFFICIENTS OF Caco-2 AND MDCK COMPARED
8.5 THEORY (STAGE 1): PARACELLULAR LEAKINESS AND SIZE EXCLUSION IN Caco-2, MDCK, AND 2/4/A1 CELL LINES
8.6 THEORY (STAGE 2): REGRESSION METHOD FOR IN VITRO CELLULAR PERMEABILITY
8.7 CASE STUDIES OF CELL-BASED PERMEABILITY AS A FUNCTION OF PH
8.8 HUMAN JEJUNAL PERMEABILITY PREDICTED DIRECTLY FROM Caco-2/MDCK
8.9 Caco-2/MDCK DATABASE AND ITS IN COMBO PAMPA PREDICTION
9 PERMEABILITY: BLOOD–BRAIN BARRIER
9.1 THE BLOOD–BRAIN BARRIER: A KEY ELEMENT FOR DRUG ACCESS TO THE CENTRAL NERVOUS SYSTEM
9.2 THE BLOOD–BRAIN BARRIER
9.3 NONCELLULAR BBB MODELS
9.4 IN VITRO BBB CELL-BASED MODELS
9.5 IN VIVO BBB MODELS
9.6 PARADIGM SHIFT
9.7 IN SILICO BBB MODELS
9.8 BIOPHYSICAL ANALYSIS OF IN VITRO ENDOTHELIAL CELL MODELS
9.9 IN SITU BRAIN PERFUSION ANALYSIS OF FLOW
9.10 IN COMBO PAMPA–BBB MODEL FOR PASSIVE BBB PERMEABILITY
10 SUMMARY AND SOME SIMPLE APPROXIMATIONS
Index
Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
Avdeef, Alex.
Absorption and drug development : solubility, permeability, and charge state / Alex Avdeef. – 2nd ed.
p. ; cm.
Includes bibliographical references and index.
ISBN 978-1-118-05745-2 (cloth)
I. Title.
[DNLM: 1. Pharmaceutical Preparations–metabolism. 2. Administration, Oral. 3. Cell Membrane Permeability. 4. Pharmaceutical Preparations–chemistry. 5. Structure-Activity Relationship. QV 38]
615'.19–dc23
2011044252
Carla
Natalie
Michael
Aubrey
PREFACE
In the nine years since the first edition of Absorption and Drug Development, a number of advances have been made, especially in the permeability methods. Several PAMPA models based on targeted lipid formulations have been described by pharmaceutical researchers. New data processing procedures were introduced to interpret permeability–pH dependence (gradient- and iso-pH) in PAMPA, as well as in cultured epithelial cell lines (e.g., Caco-2, MDCK), in primary endothelial cultured cells [e.g., porcine brain microcapillary endothelial cells (BMEC) and human BMEC], and in the rodent in situ brain perfusion model. The first PAMPA models specifically directed at modeling the blood–brain barrier (BBB) permeability have been reported. PAMPA models for skin penetration have been described. Even areas of solubility data analysis have seen some progress.
In the first edition, the pKa and solubility sections were sketchy, more like reviews than book chapters. The original permeability chapter was long and focused on the early stages of the evolution of what came to be known as the Double-Sink PAMPA method. Upon reflection, the need for a more balanced coverage was evident.
In this second edition, most of the original PAMPA material has been scrapped and has been replaced by descriptions and applications of models based on the more recent research described in literature, drawing on over 30 PAMPA-related papers published from the group at pION INC that the author headed. Also, two new chapters have been added: Chapter 8 (Permeability: Caco-2/MDCK) and Chapter 9 (Permeability: Blood–Brain Barrier). The pKa chapter has been vastly expanded. The potentiometric technique is covered comprehensively, but the treatment is still slim on UV and other methods. The new origin-shifted Yasuda–Shedlovsky (OSYS) method revealed some novel insights about how to treat insoluble acids and bases differently. The solubility chapter has been brought up to date with many examples of the treatment of practically insoluble test compounds. It was tempting to add a new chapter on dissolution, but the size of the book would have exceeded the planned limit. It was thought that a separate treatment of solubility–dissolution would best be left to a future project.
All of the database tables have been reviewed and updated with more values. The pKa table now has more than 900 entries, with many determined at 37°C. New tables have been added to each of the permeability chapters, with extensive listings of Double-Sink PAMPA, PAMPA–BBB, Caco-2/MDCK, multispecies BMEC, and in situ brain perfusion (PS) values. The introductory chapter, Chapter 1, has been updated, since the R&D paradigm of pharmaceutical research has undergone important changes since the first edition.
Based on the content of the first edition, the author twice taught an informal 10-week course at King’s College, London. There were other, smaller, teaching presentations at the University of Helsinki on two separate occasions. The notion of the book serving an educational purpose was recurrent. Several pharmacy and pharmaceutical sciences university departments have courses in physical pharmacy and pharmaceutics based on Martin’s classical textbook, Physical Pharmacy and Pharmaceutical Sciences (now in its sixth edition). This is an excellent and comprehensive text for a two-semester introductory graduate course. The author taught selected topics from it as a guest lecturer at Northeastern University, Boston, on a couple of occasions. However, one cannot learn how to do physicochemical measurements (e.g., pKa, solubility, and permeability) from Martin’s book alone. Therefore, a more advanced treatment of the physicochemical methods related to drug absorption is needed for pharmaceutics graduate students, especially those headed for careers in the pharmaceutical industry. The author has received comments from several professors who have used parts of the first edition of Absorption and Drug Development to supplement advanced pharmaceutics courses. Slanting the second edition toward an educational textbook was very tempting, but due to time constraints it was decided to leave that for a future separate add-on booklet to accompany the main text. Preparing useful questions and answers is not a minor project. The second edition still can be used to augment advanced graduate courses in pharmaceutics and as a reference for researchers in pharmaceutical R&D (and in some instances in agrochemical, environmental, and related industries). The author welcomes more feedback from academics and other readers about how the book can be improved, both as a teaching guide and as a reference.
The second edition is organized into 10 chapters. Chapter 1 describes the physicochemical measurement needs of pharmaceutical R&D, in a quickly changing environment. Chapter 2 defines the flux model, based on Fick’s laws of diffusion, in terms of solubility, permeability, and charge state (pKa), and lays the foundation for the rest of the book. Chapter 3 covers the topic of ionization constants: how to measure pKa constants well and quickly, and which strategies to use. It has been completely rewritten from the short previous version. Chapter 4 is about experimental methods of measuring partition coefficients, log P and log D. It contains a description of the Dyrssen dual-phase potentiometric method, which remains the “gold standard” technique for measuring log P of ionizable molecules, having the unique 10-orders-of-magnitude range (log P from 2 to +8). Chapter 5 considers the special topic of partition coefficients where the lipid phase is made of liposomes formed from bilayers of phospholipids. This chapter remains largely the same. Chapter 6 covers solubility measurements and has been broadly expanded. Chapter 7 describes PAMPA, the high-throughput artificial membrane permeability method originally introduced by Manfred Kansy and co-workers from Hoffmann–La Roche. The chapter has been substantially revised and remains a deep account of the rapidly developed important topic. Many hundreds of original measurements are tabulated in the chapter. Chapter 8 considers permeability measurements using epithelial cell models, such as Caco-2 and MDCK. Chapter 9 does so with endothelial cultured cell models, and it attempts to correlate these to animal in situ brain perfusion measurements of luminal permeability. Chapter 10 concludes with simple physicochemical property approximations. More than 1350 references and well over 200 drawings and 200 pages of tables substantiate the book as an extensively documented reference work.
I have many colleagues to thank for their thoughts, criticism, guidance, and opportunities for collaborations: Joan Abbott, Mike Abraham, Per Artursson, David Begley, Stephanie Bendels, Christel Bergström, Marival Bermejo, Li Di, Jennifer Dressman, Beate Escher, Bernard Faller, Holger Fischer, Norman Ho, Pranas Japertas, Paulius Jurgutis, Manfred Kansy, Ed Kerns, Stefanie Krämer, Chris Lipinski, Sibylle Neuhoff, Alanas Petrauskas, Tom Raub, Jean-Michel Scherrmann, Abu Serajuddin, Kiyohiko Sugano, Krisztina Takács-Novák, Bernard Testa, Björn Wagner, Han van de Waterbeemd, and Shinji Yamashita. I owe gratitude to many others, including my former colleagues at pION INC and Sirius Analytical Instruments Ltd. I left pION at the beginning of the year, to start in-ADME Research (ADME software and consulting) and to finish writing this book.
Salvatore Cisternino, Markus Fridén, Margareta Hammarlund-Udanaes, Krisztina Takács-Novák, and Kin Tam were most kind to read various chapters as the book was being written and offered many helpful suggestions, for which I am especially grateful.
Joan Abbott is a dear friend and has been a generous host on a number of occasions that I spent time writing and recharging in her group at King’s College, London.
I am especially privileged and grateful to have known Manfred Kansy as a friend for the last 20 years.
I would also like to thank Joyce Saltalamachia for her love and support, as she put up with a lot during my 12 months of writing and other things.
ALEX AVDEEFCambridge, MassachusettsSeptember 2011
PREFACE TO THE FIRST EDITION
This book is written for the practicing pharmaceutical scientist involved in ADME measurements, who needs to communicate with medicinal chemists persuasively, so that new synthesized molecules will be more “drug-like.” ADME is all about “a day in the life of a drug molecule” (absorption, distribution, metabolism, excretion). Specifically, this book attempts to describe the state of the art in measurement of ionization constants (pKa), oil–water partition coefficients (log P/log D), solubility, and permeability (artificial phospholipid membrane barriers). Permeability is covered in considerable detail, based on a newly developed methodology known as PAMPA (Parallel Artificial Membrane Permeability Assay).
These physical parameters form the major components of physicochemical profiling (the “Absorption” in ADME) in the pharmaceutical industry, from drug discovery through drug development. However, there are opportunities to apply the methodologies in other fields, particularly the agrochemical and environmental industries. In addition, new applications to augment animal-based models in the cosmetics industry may be interesting to explore.
It has been the author’s observation that graduate programs in pharmaceutical sciences often neglect adequately to train students in these classical solution chemistry topics. Often young scientists in pharmaceutical companies are assigned the task of measuring some of these properties in their projects. Most find the learning curve somewhat steep. In addition, experienced scientists in mid careers come upon the topic of physicochemical profiling for the first time, and they find few resources to draw upon outside of the primary literature.
The idea for a book on the topic has morphed through various forms, beginning with focus on the subject of metal-binding to biological ligands, when the author was a postdoc in Professor Ken Raymond’s group at University of California, Berkeley. When the author was an Assistant Professor of Chemistry at Syracuse University, every time the special topics course on speciation analysis was taught, more notes were added to the “book.” After five years, more than 300 pages of hand-scribbled notes and derivations accumulated, but no book emerged. Some years later, a subsection of the original notes acquired a binding and saw light in the form of Applications and Theory Guide to pH-Metric pKa and log P Measurement, out of the early effort in the start-up of Sirius Analytical Instruments Ltd., in Forest Row, a charming four-pub village at the edge of Ashdown Forest, south of London. At Sirius, the author was involved in teaching a comprehensive three-day training course to advanced users of pKa and log P measurement equipment manufactured by Sirius. The trainees were from pharmaceutical and agrochemical companies, and they shared many new ideas during the courses. Over the last decade, Sirius has standardized the measurement of pKa values in the pharmaceutical and agrochemical industries. Some 50 courses later, the practice continues at another young company, pION, located along high-tech highway 128, north of Boston, Massachusetts. The list of topics has expanded over the last 12 years, to cover solubility, dissolution, and permeability, as new instruments were developed. Last year, an opportunity to write a review article came up, and a bulky piece appeared in Current Topics in Medicinal Chemistry, entitled Physicochemical Profiling (Solubility, Permeability and Charge State). In reviewing that manuscript, Cynthia Berger (pION) said that with a little extra effort, “this could be a book.” Further encouragement came from Bob Esposito of John Wiley & Sons. My colleagues at pION were kind about my taking a sabbatical in England, to focus on the writing. I was privileged to join Professor Joan Abbott’s neuroscience laboratory at King’s College London for three months, where I conducted an informal 10-week graduate short course on the topics of this book, as the material was freshly written. After hours, it was my pleasure to jog with my West London Hash House Harrier friends. As the chapter on permeability was being written, my very capable colleagues at pION were quickly measuring permeability of membrane models freshly inspired by the book writing. It is due to their efforts that Chapter 7 is loaded with so much original data, out of which emerged the “Double-Sink” PAMPA model for predicting human intestinal permeability. Per Nielsen (pION) reviewed the manuscript as it slowly emerged, with a keen eye. Many late-evening discussions with him led to freshly inspired insights, now imbedded in various parts of the book.
The book is organized into eight chapters. Chapter 1 describes the physicochemical needs of pharmaceutical research and development. Chapter 2 defines the flux model, based on Fick’s laws of diffusion, in terms of solubility, permeability, and charge state (pKa), and lays the foundation for the rest of the book. Chapter 3 covers the topic of ionization constants: how to measure pKa values well and quickly, and which methods to use. Bjerrum analysis is revealed as the “secret” weapon behind the most effective approaches. Chapter 4 is about experimental methods of measuring partition coefficients, log P and log D. It contains a description of the Dyrssen dual-phase potentiometric method that truly is the “gold standard” method for measuring log P of ionizable molecules, having the unique 10 orders of magnitude range (log P from 2 to +8). High-throughput methods are also described. Chapter 5 considers the special topic of partition coefficients where the lipid phase is made of liposomes formed from vesicles made of bilayers of phospholipids. Chapter 6 dives into solubility measurements. A unique approach, based on the Dissolution Template Titration method, has demonstrated capabilities to measure solubilities as low as one nanogram per milliliter. In addition, high-throughput microtiter plate UV methods for determining “thermodynamic” solubility constants are described. At the ends of Chapters 3–6, an effort has been made to collect tables of critically selected values of the constants of drug molecules, the best available values. Chapter 7 describes PAMPA, the high-throughput method recently introduced by Manfred Kansy et al. of Hoffmann–La Roche. Chapter 7 is the first thorough account of the topic and takes up almost half of the book. Nearly 4000 original measurements are tabulated in the chapter. Chapter 8 concludes with simple rules. Over 600 references and well over 100 drawings substantiate the book.
Professor Norman Ho (University of Utah) was very kind to critically read the permeability chapter and comment on the various derivations and concepts. His unique expertise on the topic spans many decades. His thoughts and advice (15 pages of handwritten notes) inspired me to rewrite some of the sections in that chapter. I am very grateful to him. I am grateful to other colleagues at pION who expertly performed many of the measurements of solubility and permeability, which are presented in the book: Chau Du, Jeffrey Ruell, Melissa Strafford, Suzanne Tilton, and Oksana Tsinman. In addition, I thank Dmytro Voloboy and Konstantin Tsinman for their help in database, computational, and theoretical matters. The helpful discussion with many colleagues, particularly Manfred Kansy and Holger Fischer at Hoffmann La-Roche, Ed Kerns and Li Di at Wyeth Pharmaceuticals, and those at Sirius Analytical Instruments, especially John Comer and Karl Box, are gratefully acknowledged. Helpful comments from Professors John Dearden (Liverpool John Moores University) and Hugo Kubinyi (Heidelberg University) are greatly appreciated. I also thank Professor Anatoly Belyustin (St. Peterburgh University) for pointing out some very relevant Russian literature. Chris Lipinski (Pfizer) has given me a lot of good advice over the last 10 years about instrumentation and pharmaceutical research, for which I am grateful. Collaborations with Professors Krisztina Takács-Novák (Semmelweis University, Budapest) and Per Artursson (Uppsala University) have been very rewarding. James McFarland (Reckon.Dat) and Alanas Petrauskas (Pharma Algorithms) have been my teachers of in silico methods. I am in debt to Professor Joan Abbott and Dr. David Begley for allowing me to spend three months in their laboratory at King’s College London, where I learned a lot about the blood–brain barrier. Omar at Cafe Minon, Warwick Street in Pimlico, London, was kind to let me spend many hours in his small sandwich shop, as I wrote several papers and drank a lot of coffee. Lasting thanks go to David Dyrssen and the late Jannik Bjerrum for planting the seeds of most interesting and resilient pH-metric methodologies, as well as to Professor Bernard Testa of Lausanne University for tirelessly fostering the white light of physicochemical profiling. My congratulations to him on the occasion of his retirement.
ALEX AVDEEFBoston, MassachusettsSeptember 2002
ABBREVIATIONS
ABL (or UWL)
aqueous boundary layer (or unstirred water layer)
ADME
absorption, distribution, metabolism, excretion
AP
absorption potential
AS
anthroylstearic acid
AUC
area under the curve
BA/BE
bioavailability/bioequivalence
BBB
blood–brain barrier
BBM
brush-border membrane
BCS
Biopharmaceutics Classification System
BLM
black lipid membrane (single bilayer membrane barrier)
BMEC
brain microcapillary endothelial cell (
in vitro
cultured-cell model)
BPC
Brain Penetration Classification
BSA
bovine serum protein
CE
capillary electrophoresis
CGM
Classification Gradient Map
Cho
cholesterol
CL
cardiolipin
CMC
critical micelle concentration
CPC
centrifugal partition chromatography
CRE
Crone–Renkin equation
CV
cyclic voltammetry
DA
dodecylcarboxylic acid
DMPC
dimyristoylphosphatidylcholine
DOPC
dioleoylphosphatidylcholine
DRW
dynamic range window
DS
Double-Sink (PAMPA)
DSHA
N
-Dansylhexadecylamine
DTT
Dissolution Template Titration (solubility method)
ECF
extracellular fluid (in the brain)
EMF
electromotive force (mV)
ER
efflux ratio (
in vitro
polarized transport)
ET
extrusion technique (for making LUV)
FAT
freeze-and-thaw (step in the making of LUV)
FDM
Facilitated Dissolution Method (solubility method)
FFA
free fatty acid
FLW
flow limit window
GIT
gastrointestinal tract
GOF
goodness-of-fit (in regression analysis)
HDM
hexadecane membrane
hERG
human ether-a-go-go related gene
HIA
human intestinal absorption
HJP
human jejunal permeability
HP-β-CD
2-hydroxypropyl-β-cyclodextrin
HTS
high-throughput screening or solubility
IAM
immobilized artificial membrane
ISF
interstitial fluid (in the brain)
IUPAC
International Union of Pure and Applied Chemistry
IVIVC
in vitro–in vivo
correlation
KRB
Krebs–Ringer bicarbonate (buffer)
KO/WT
knockout/wild-type P-glycoprotein (Pgp)-transfected mouse models
LFER
linear free-energy relationship
LJP
liquid-junction potential (mV)
LOD
limit of detection
LUV
large unilamellar vesicle
M6G
morphine-6β-D-glucuronide
MAD
maximum absorbable dose (mg)
MBUA
mouse brain uptake assay
MDCK
Madin–Darby canine kidney (cell line)
MEP
molecular electronic potential
MLR
multiple linear regression
MLV
multilamellar vesicle
MSF
miniaturized shake-flask (solubility method)
NaTC
sodium taurocholate
NCE
new chemical entity
NIST (NBS)
National Institute of Standards and Technology (formerly known as the National Bureau of Standards, NBS)
NMP
1-methyl-2-pyrrolidone
NMR
nuclear magnetic resonance
OECD
Organization for Economic Cooperation and Development
OIM
open innovation model (pharmaceutical industry collaborations)
OSYS
origin-shifted Yasuda–Shedlovsky (function in cosolvent p
K
a
analysis)
PA
phosphatidic acid
PAMPA
Parallel Artificial Membrane Permeability Assay
PAMPA–BBB
PAMPA used to predict blood–brain barrier permeability, based on PBLE formulation
PASS
partially automated solubility screen
PBLE
porcine brain lipid extract
PBPK
physiologically based PK
PC
phosphatidylcholine
PE
phosphatidylethanolamine
PEG
polyethylene glycol oligomer
PG
phosphatidylglycerol or propylene glycol
PGDP
propylene glycol dipelargonate
PI
phosphatidylinositol
PK
pharmacokinetics
p
OD
—optimized design
PS
phosphatidylserine
PSA
polar surface area (
in silico
descriptor)
PVDF
polyvinylidene fluoride (hydrophobic filter membrane)
QSPR
quantitative structure–permeability relationship
RBC
red blood cell
SCFA
short-chain fatty acids
SIP
surface ion pair (charged-drug membrane surface partitioning)
SLS
sodium lauryl sulfate (anionic detergent)
RLJP
residual LJP
Sph
sphingomyelin
SSF
saturation shake-flask (solubility method)
SUV
small unilamellar vesicle
TEER
transendothelial electrical resistance (Ω·cm
2
)
TJ
tight junction
TMA-DPH
trimethylamino-diphenylhexatriene chloride
NOMENCLATURE
A
area of the PAMPA filter (cm
2
)
C
0
aqueous concentration of the uncharged species (mol·cm
−3
)
C
m
(
x
)
solute concentration inside of a membrane, at position
x
(mol·cm
−3
)
solute concentration inside a membrane, at position
x
(mol cm
−3
)
C
R
,
C
D
receiver and donor aqueous solute concentration, respectively (mol·cm
−3
)
D
Lipid–water distribution pH-dependent function (also called the apparent partition coefficient)
D
aq
(
D
m
)
diffusivity of a solute in aqueous (membrane) solution (cm
2
·s
−1
)
diff
difference between the partition coefficient of the uncharged and the charged species
D
MEM
diffusivity of a solute inside a membrane (cm
2
·s
−1
)
D
MEM/W
pH-dependent membrane–water apparent partition coefficient (dimensionless)
Double-Sink
two sink conditions present: ionization and binding
E
(Δφ)
function due to potential drop across the cell junction (dimensionless)
f
(0)
,
f
(+)
,
f
(−)
molecule concentration fraction in the uncharged, positively charged and negatively charged forms, respectively
F
(
r
HYD
/
R
)
Renkin molecular sieving function, dimensionless fraction in the range of 0 to 1
F
pf
cerebrovascular flow velocity of perfusion fluid (mL·g
−1
·s
−1
brain tissue)
h
membrane thickness (cm)
h
ABL,
,
thickness of the ABL (cm), at the receiver (R), donor (D) side, respectively
total thickness of the ABL, equals
hit
a molecule with (a) confirmed activity from a primary assay, (b) a good profile in secondary assays, and (c) a confirmed structure
h
m
membrane thickness (cm)
, ,
excess lipid layer thicknesses (receiver/donor sides), and filter thickness
total thickness of the lipid layers:
in combo
methodology where a measured property (e.g., PAMPA permeability coefficient) is additively “combined” with a calculated (
in silico
) descriptor (e.g., H-bond potential)
J
flux across a membrane (mol·cm
−2
·s
−1
)
j
H
low-pH junction potential parameter in the Avdeef–Bucher four-parameter electrode standardization equation
j
OH
high-pH junction potential parameter in the Avdeef–Bucher four-parameter electrode standardization equation
k
a
absorption rate constant (min
−1
)
K
e
extraction constant
K
in
unidirectional transfer constant (mL·g
−1
·s
−1
):
K
in
= (
Q
br
/
C
pf
)/
T
, where
Q
br
= test compound parenchymal brain concentration (nmol·g
−1
brain tissue) (corrected for the vascular volume),
C
pf
= perfusion fluid concentration (nmol·mL
−1
),
T
= perfusion time (s).
k
S
slope factor in the Avdeef–Bucher four-parameter electrode standardization equation
K
sp
solubility product, e.g., [Na
+
][A
−
] or [BH
+
][Cl
−
]
n
H
total number of dissociable protons contributed to the solution by the sample substance (in the form it is introduced to solution)
Bjerrum function: average number of bound protons on a molecule at a particular pH
P
lipid–water pH-independent partition coefficient; also designated
P
OCT
,
P
X/W
, where X = ALK, DD, HXD, LIPO, MEM, OCT, O, etc.
P
0
intrinsic permeability (chargeless form of drug) (cm·s
−1
), pH-independent
P
a
apparent artificial-membrane permeability (cm·s
−1
)—like
P
e
, but with some limiting assumption
P
ABL
ABL permeability coefficient (cm·s
−1
):
P
ABL
=
D
aq
/
h
ABL
,
in vitro
or PAMPA model
P
app
apparent
in vitro
transcellular permeability coefficient (cm·s
−1
)
P
C
in vitro
transendothelial (cellular) permeability (cm·s
−1
); derived from
P
app
corrected for hydrodynamic effects (ABL, paracellular, filter); depends on pH for ionizable permeants
p
c
H
pH scale based on hydrogen ion concentration
BBB luminal permeability coefficient (cm·s
−1
) from
in situ
brain perfusion technique: ; corrected-for-flow permeability coefficient; depends on pH for ionizable permeants
P
e
effective permeability coefficient (cm·s
−1
)—the experimentally determined value
effective luminal permeability coefficient (cm·s
−1
),
not
corrected for flow:
P
e
=
K
in
/
S
; depends on pH for ionizable permeants
pH
operational pH scale
p
c
H
concentration-based pH scale
pH-CRE
pH-dependent Crone–Renkin equation (CRE) flow correction method
P
i
(or )
permeability coefficient (cm·s
−1
) of the
ionized
form of permeant
p
K
a
ionization constant (negative log form), based on the concentration scale
apparent p
K
a
in the presence of precipitation in the DTT method
the apparent p
K
a
derived from the log
P
e
–pH profile, the pH at which the resistance to transport is 50% due to the artificial membrane barrier and 50% due to the ABL
pH at which both the chargeless and salt forms of a substance coprecipitate
membrane p
K
a
(limiting apparent p
K
a
in high membrane lipid–water volume ratio titrations)
octanol p
K
a
(limiting apparent p
K
a
in high octanol–water volume ratio titrations)
P
m
PAMPA transmembrane permeability (cm·s
−1
)—
P
e
corrected for ABL and aqueous pore diffusion effects; pH dependence follows Henderson–Hasselbalch equation.
refers to the intrinsic permeability of the bilayer membrane to the uncharged form of an ionizable molecule
P
OCT
octanol–water partition coefficient for an uncharged species
BBB intrinsic luminal permeability coefficient of the uncharged form of permeant; for ionizable compounds, , “+” for acids, “−” for bases
PAMPA–BBB intrinsic permeability coefficient of the uncharged form of permeant; for ionizable compounds, , where “+” is for acids, “−” is for bases
P
para
PAMPA paramembrane permeability coefficient (cm·s
−1
)—diffusion of permeant via aqueous pores formed in the thin PAMPA–BBB membrane:
P
para
= (ε/δ)
2
D
aq
paracellular permeability coefficient (cm·s
−1
), indicating aqueous diffusion of permeant through the
tight
junctions formed by the blood–brain barrier
paracellular permeability coefficient (cm·s
−1
), indicating aqueous diffusion of permeant through the
leaky
junctions formed in endothelial cell models
PAMPA paramembrane permeability coefficient (cm·s
−1
)—diffusion of permeant via aqueous pores formed in the
thin
PAMPA–BBB membrane:
P
para
= (ε/δ)
2
D
aq
PS
capillary permeability–surface area product (mL·g
−1
·s
−1
), traditionally determined from the uptake rate constant (
K
in
) using Crone–Renkin equation (CRE): , where
F
pf
is the regional cerebral flow of perfusion fluid (mL·g
−1
·s
−1
)
R
membrane junction pore radius (Å); also, Abraham–van der Waals LFER descriptor
R
w
weight percent cosolvent
r
HYD
hydrodynamic molecular radius (Å)
R
M
membrane retention—mole fraction of compound retained by the membrane
S
solubility in molar, µg·mL
−1
, or mg·mL
−1
units
S
(or A)
endothelial surface area in a gram of brain tissue (assumed to be 100 cm
2
·g
−1
)
S
0
intrinsic solubility of the uncharged species
SC
selectivity coefficient; slope in the log–log
in vitro–in vivo
correlation plot
S
i
solubility of the ionized species (salt), a conditional constant, depending on the concentration of the counterion in solution
sink
any process that can significantly lower the concentration of the neutral form of the sample molecule in the acceptor compartment; examples include: physical sink (where the buffer solution in the acceptor compartment is frequently refreshed), ionization sink (where the concentration of the neutral form of the drug is diminished due to ionization), and binding sink (where the concentration of the neutral form of the drug is diminished due to binding with serum protein, cyclodextrin, or surfactants in the acceptor compartment)
S
0
intrinsic solubility, that is, the solubility of the uncharged species
S
w
sum of the weighted squares of residuals in regression analysis
V
L
volume of luminal fluid, 250 mL
V
x
Abraham McGowan molecular volume LFER descriptor
%para, %trans, %ABL
relative fraction of permeation effected by the paracellular, transendothelial (cellular), and ABL routes, respectively
±
symbol in equation: “−” is used for
bases
and “+” symbol for
acids
δ
difference between the liposome–water and octanol–water log
P
for the uncharged species
α
empirical hydrodynamic constant, usual values 0.5–1.0; theoretical value is 0.5; also, one of the four Avdeef–Bucher parameters in electrode standardization; also Abraham H-bond donor LFER descriptor
β
Abraham H-bond acceptor LFER descriptor
π
Abraham polarity LFER descriptor
Δ-Shift
the difference between the true p
K
a
and the apparent p
K
a
observed in a solubility–pH profile, due to DMSO–drug binding or drug–drug aggregation binding
Δφ:
potential drop (mV) across the electric field created by negatively charged residues lining the junctional pores of a monolayer of cells
ε
nominal microfilter porosity, as specified by the manufacturer (values 0.05–0.70); also, dielectric constant of solvent
ε
a
apparent filter porosity, based on the volume of PAMPA lipid used, the area and thickness of the filter used, and the nominal filter porosity
ε/δ
porosity of paracellular junction pores divided by the rate-limiting paracellular pathlength (size-restricted, cation-selective)
(ε/δ)
2
secondary porosity–pathlength ratio (unspecified size/charge dependence); porosity of paramembrane aqueous pores divided by the length of the water-filled channels in thin PAMPA–BBB membranes (δ ∼0.01 cm)
ν
stirring speed, RPM (r·min
−1
)
τ
LAG
the time for steady state to be reached in a permeation cell, after sample is introduced into the donor compartment; in the PAMPA model described in the book, this is approximated as the time that sample first appears detected in the acceptor well
COMMERCIAL TRADEMARKS
pCEL-X™ and μDISS-X™ are trademarks of in-ADME Research. Double-Sink™, Prisma™, PAMPA Evolution™, μSOL Evolution™, and STIRWELL™ are commercial trademarks of pION INC. Transwell®, Freedom Evo®, Biomek-FX®, and Excel® are the registered trademarks of Corning, Tecan, Beckman Coulter, and Microsoft, respectively.
1
INTRODUCTION
The search for new drugs is a long process. Attrition is high and the costs keep escalating (now perhaps as high as $2 billion per marketed drug). The traditional discovery–development models are undergoing change, as many pharmaceutical companies reign in the R&D costs, by consolidating research sites, downsizing research staff, engaging in more outside collaborations, and outsourcing.
1.1 BULLDOZER SEARCHING FOR A NEEDLE IN A HAYSTACK?
Although the last decade has led to improvements in attrition due to poor pharmacokinetic profiles of discovery compounds, drug absorption continues to be an important issue in modern pharmaceutical research and development. The search for new drugs is daunting, expensive, and highly risky, but potentially highly rewarding.
If chemicals were confined to molecular weights of less than 600 Da and consisted of common atoms, the chemistry space is estimated to contain 1040 to 10100 molecules, an impossibly large space to search for potential drugs [1]. To address this limitation of vastness, “maximal chemical diversity” [2] was applied in constructing large experimental screening libraries. It’s now widely accepted that the quality of leads is more important than the quantity. Traditionally, large compound libraries have been directed at biological “targets” to identify active molecules, with the hope that some of these “hits” may someday become drugs. The pre-genomic era target space was relatively small: Less than 500 targets had been used to discover the known drugs [3]. This number may expand to several thousand in the next few years as genomics-based technologies and better understanding of protein–protein interactions uncover new target opportunities [4, 5]. Of the estimated 3000 new targets, only about 20% are commercially exploited [5]. Due to unforeseen complexities of the genome and biologic systems, it is taking a lot longer and is more expensive to exploit the new opportunities than originally thought [5–8].
Although screening throughputs have massively increased over the past 20 years (at great cost in set up and run), lead discovery productivity has not necessarily increased accordingly [5–8]. C. Lipinski has suggested that maximal chemical diversity is an inefficient library design strategy, given the enormous size of the chemistry space, and especially that clinically useful drugs appear to exist as small tight clusters in chemistry space: “… one can make the argument that screening truly diverse libraries for drug activity is the fastest way for a company to go bankrupt because the screening yield will be so low” [1]. Hits are made in pharmaceutical companies, but this is because the most effective (not necessarily the largest) screening libraries are highly focused, to reflect the putative tight clustering. Looking for ways to reduce the number of tests, to make the screens “smarter,” has an enormous cost reduction implication.
Figure 1.1 sketches out the process of drug exploration, discovery, and development followed at several pharmaceutical companies in the early 2000s [9–12]. A large pharmaceutical company may screen 100,000 to 1,000,000 molecules for biological activity each year. Some 3000–10,000 hits are made. Most of these molecules, however potent, do not have the right physicochemical, stability, and safety properties. Large pharmaceutical companies promote about 12 molecules into preclinical development each year. Only about 5 in 12 candidates survive after Phase I (Figure 1.1). A good year sees perhaps just one molecule reach the product stage after 9 molecules enter first-in-man clinical testing [6]. For that molecule, the start-to-finish may have taken 14 years (Figure 1.1).
Figure 1.1 Chart summarizing the various stages in the evolution of a drug product. Included are estimated times at each stage, the cost of each stage, and the relative attrition rates.
Based on studies taken from multiple sources [7–10].
The molecules that fail have “off-target” activity or poor side effects profiles. Unfortunately, animal models have been weak predictors of efficacy and/or safety in humans [7]. The adverse reactions in humans are sometimes not discovered until the drug is on the market in large-scale use in humans.
In 2001, a drug product cost about $880 million to bring out to market—which included the costs of numerous failures (Figure 1.1). In 2010, the cost was closer to $2 billion/approval [7]. It has been estimated that about 33% of the molecules that reach preclinical development are eventually rejected due to ADME (absorption, distribution, metabolism, excretion) problems. Other attrition causes are lack of efficacy (33%) and toxicity (34%). Much more money is spent on compounds that fail than on those that succeed. The industry has started to respond by attempting to screen out those molecules with poor ADME properties during discovery, before the molecules reach development. However, that has led to another challenge: how to do the additional screening quickly enough [13]. An undesirable consequence of cheap and quick assays it that their quality is low [5].
Combinatorial chemistry programs have tended to select for higher-molecular-weight molecules, predictably low in solubility. “Early warning” tools, such as Lipinski’s “Rule of Five” [1] and simple computer programs that predict solubility and other properties from 2-D structure [14, 15], attempt to weed out such molecules early in discovery programs. Still, many solubility-problematic molecules remain unrecognized in early studies, due to the overly simplistic methods used to measure solubility in discovery [16]. More accurate (but still fast) solubility [16–19] (Chapter 6) and artificial membrane permeability [20–24] (Chapter 7) methods in the candidate selection stage in pharmaceutical R&D have proven to be particularly helpful for recognizing at a much earlier time the truly problematic molecules. It had even been suggested that screening for future formulation efficacy (pH and excipient effects on solubility and permeability) of candidates could be justified, if the methods were fast, compound-sparing, cost effective, and reasonably accurate [16, 18].
1.2 AS THE PARADIGM TURNS
As a consequence of the increased and unsustainable cost of bringing out a therapeutic product, many pharmaceutical companies have begun to change the way discovery and development are done [5]:
Size and scope of internal research capabilities are decreasing, as more outsourcing is considered, not only in discovery, but also in development.
Several companies have rearranged internal structures to be smaller “biotech-like” units.
External collaborations with small biotech companies and academia have increased.
Many in the industry predict that more biologic therapies will emerge (which have lower Phase II attrition [6]), and the emphasis on small molecules may decrease.
Strategies of discovery are changing [7]:
Development of multitargeted therapeutics will increase.
Whole pathway approaches, drawing on increasing understanding of protein–protein interactions, will be increasingly explored.
Biology-driven drug discovery, starting with a specific disease model and a pathway, benefitting from external collaborations with academic groups.
Analysis of multigenic complex diseases.
Network pharmacology.
Obtaining early proof of concepts, with small clinical studies and/or applying microdosing.
The “open innovation model” (OIM) [8] involves the progression of discovery and development that’s different from that depicted in Figure 1.1. An attrition “funnel” will start with many test compounds. Even at the early stage, ideas and technologies may be either in-licensed or out-licensed. At later optimization stages, two-way collaborations with academic labs will play an increasing role. Product in-licenses will be considered. Near the product launch stage, line extensions via partners and joint ventures will become increasingly popular. In the OIM, intellectual property would be selectively distributed and proactively managed and shared to create value that otherwise would not surface.
1.3 SCREEN FOR THE TARGET OR ADME FIRST?
Most commercial combinatorial libraries, some of which are very large and may be diverse, have a very small proportion of drug-like molecules [1]. Should only the small drug-like fraction be used to test against the targets? The existing practice is to screen for the receptor activity before “drug-likeness.” The reasoning is that structural features in molecules rejected for poor ADME properties may be critical to biological activity related to the target. It is believed that active molecules with liabilities can be modified later by medicinal chemists, with minimal compromise to potency. Lipinski [1] suggested that the order of testing may change in the near future, for economic reasons. He adds that looking at data already available from previous successes and failures may help to derive a set of guidelines to apply to new compounds. When a truly new biological therapeutic target is examined, nothing may be known about the structural requirements for ligand binding to the target. Screening may start as more or less a random process. A library of compounds is tested for activity. Then computational models are constructed based on the results, and the process is repeated with newly synthesized molecules, perhaps many times, before adequately promising compounds are revealed. With large numbers of molecules, the process can be costly. If the company’s library is first screened for ADME properties, that screening is done only once. The same molecules may be recycled against existing or future targets many times, with knowledge of drug-likeness to fine-tune the optimization process. If some of the molecules with very poor ADME properties are judiciously filtered out, the biological activity testing process would be less costly. But the order of testing (activity versus ADME) is likely to continue to be the subject of future debates [1].
1.4 ADME AND MULTIMECHANISM SCREENS
In silico property prediction is needed more than ever to cope with the screening overload [14, 15]. Improved prediction technologies are continuing to emerge. However, reliably measured physicochemical properties to use as “training sets” for new target applications have not kept pace with the in silico methodologies.
Prediction of ADME properties should be simple, since the number of descriptors underlying the properties is relatively small, compared to the number associated with effective drug-receptor binding space. In fact, prediction of ADME is difficult. The current ADME experimental data reflects a multiplicity of mechanisms, making prediction uncertain. Screening systems for biological activity are typically single mechanisms, where computational models are easier to develop [1].
For example, aqueous solubility is a multimechanism system. It is affected by lipophilicity, H-bonding between solute and solvent, intra- and intermolecular H-bonding, electrostatic bonding (crystal lattice forces), and charge state of the molecule. When the molecule is charged, the counterions in solution may affect the measured solubility of the compound. Solution microequilibria occur in parallel, affecting the solubility. Many of these physicochemical factors are not well understood by medicinal chemists, who are charged with making new molecules that overcome ADME liabilities without losing potency.
Another example of a multimechanistic probe is the Caco-2 permeability assay (Chapter 8). Molecules can be transported across the Caco-2 monolayer by several mechanisms operating simultaneously, but to varying degrees: transcellular passive diffusion, paracellular passive diffusion, lateral passive diffusion, active influx or/and efflux mediated by transporters, passive transport mediated by membrane-bound proteins, receptor-mediated endocytosis, pH-gradient- and electrostatic-gradient-driven mechanisms, and so on (Chapter 2). The P-glycoprotein (Pgp) efflux transporter can be saturated if the solute concentration is high enough during the assay. If the substance concentration is very low (perhaps because not enough of the compound is available during discovery, or due to low solubility), the importance of efflux transporters in gastrointestinal tract (GIT) absorption can be overestimated, providing the medicinal chemist with an overly pessimistic prediction of intestinal permeability [1, 25]. Drug metabolism in some in vitro cellular systems can further complicate the assay outcome.
Compounds from traditional drug space (“common drugs”—readily available from chemical suppliers), often chosen for studies by academic laboratories for assay validation and computational model-building purposes, can lead to misleading conclusions when the results of such models are applied to “real” [12] discovery compounds, which most often have extremely low solubilities [25].
Computational models for single-mechanism assays (e.g., biological receptor affinity) get better as more data are accumulated [1]. Computational models for multimechanism assays (e.g., solubility, permeability, charge state), in contrast, get worse as more measurements are accumulated [1]. Predictions of human oral absorption using Caco-2 permeability values can look very impressive when only a small number of molecules is considered. However, good correlations deteriorate as more molecules are included in the plot, and predictivity soon becomes tenuous. “The solution to this dilemma is to carry out single-mechanism ADME experimental assays and to construct single-mechanism ADME computational models. The ADME area is at least 5 or more years behind the biology therapeutic target area in this respect” [1].
1.5 ADME AND THE MEDICINAL CHEMIST
Although ADME assays are usually performed by analytical chemists, medicinal chemists—the molecule makers—need to have some understanding of the physicochemical processes in which the molecules participate.
It is now almost a century since Overton and Meyer first demonstrated the existence of a relationship between the biological activity of a series of compounds and some simple physical property common to its members. In the intervening years the germ of their discovery has grown into an understanding whose ramifications extend into medicinal chemistry, agrochemical and pesticide research, environmental pollution, and even, by a curious reinvention of familiar territory, some areas basic to the science of chemistry itself. Yet its further exploitation was long delayed. It was 40 years later that Ferguson at ICI [AstraZeneca] applied similar principles to a rationalization of the comparative activity of gaseous anaesthetics, and 20 more were to pass before the next crucial step was formulated in the mind of Hansch. … Without any doubt, one major factor [for delay] was compartmentalism. The various branches of science were much more separate then than now. It has become almost trite to claim that the major advances in science take place along the borders between its disciplines, but in truth this happened in the case of what we now call Hansch analysis, combining as it did aspects of pharmacy, pharmacology, statistics, and physical organic chemistry. Yet there was another feature that is not so often remarked, and one with a much more direct contemporary implication. The physical and physical organic chemistry of equilibrium processes—solubility, partitioning, hydrogen bonding, etc.—is not a glamorous subject. It seems too simple. Even though the specialist may detect an enormous information content in an assemblage of such numbers, to synthetic chemists used to thinking in three-dimensional terms they appear structureless, with no immediate meaning that they can visually grasp. Fifty years ago it was the siren call of Ehrlich’s lock-and-key theory that deflected medicinal chemists from a physical understanding that might otherwise have been attained much earlier. Today it is glamour of the television screen. No matter that what is on display may sometimes possess all the profundity of a five-finger exercise. It is visual and therefore more comfortable and easier to assimilate. Similarly, MO theory in its resurgent phase combines the exotic appeal of a mystery religion with a new-found instinct for three-dimensional colour projection which really can give the ingénue the impression that he understands what it is all about. There are great advances and great opportunities in all this, but nevertheless [there is] a concomitant danger that medicinal chemists may forget or pay insufficient attention to hurdles the drug molecule will face if it is actually to perform the clever docking routine they have just tried out: hurdles of solubilization, penetration, distribution, metabolism and finally of its nonspecific interactions in the vicinity of the active site, all of them the result of physical principles on which computer graphics has nothing to say. Such a tendency has been sharply exacerbated by the recent trend, for reasons of cost as much as of humanity, to throw the emphasis upon in vitro testing. All too often, chemists are disconcerted to discover that the activity they are so pleased with in vitro entirely fails to translate to the in vivo situation. Very often, a simple appreciation of basic physical principles would have spared them this disappointment; better, [it] could have suggested in advance how they might avoid it. We are still not so far down the path of this enlightenment as we ought to be. What is more, there seems a risk that some of it may fade if the balance between a burgeoning receptor science and these more down-to-earth physical principles is not properly kept.—Peter Taylor [26].*
In 1990, Taylor [26] described physicochemical profiling in a comprehensive and richly descriptive way, but much has happened since then. Then, instrument companies took no visible interest in making pKa (Chapter 3), log P (Chapters 4 and 5), or solubility (Chapter 6) analyzers; it did not occur to anyone to do PAMPA (Chapter 7). Combinatorial chemistry, HTS, Caco-2 (Chapter 8), IAM, and CE were largely unknown. Thus it is a good time to take stock of what can be learned from the work of the last two decades.
1.6 THE “ABSORPTION” IN ADME
This book focuses on physicochemical profiling in support of improved prediction methods for the “absorption” in ADME. Metabolism and other components of ADME will be beyond the scope of this book. Furthermore, properties related to passive absorption will be the focus, and active transport mechanisms will be considered only indirectly. The most important physicochemical parameters associated with passive absorption are acid–base character (which determines the charge state of a molecule in a solution of a particular pH), lipophilicity (which determines distribution of a molecule between the aqueous and the lipid environments), solubility (which limits the concentration that a dosage form of a molecule can present to the solution and the rate at which the molecule dissolves from the solid form), and membrane permeability (which determines how quickly molecules can cross membrane barriers). Current state of the art in measurement of these properties, as the ever important function of pH, will be discussed in depth in this book.
1.7 IT IS NOT JUST A NUMBER, IT IS A MULTIMECHANISM
Drugs exert their therapeutic effects through reactions with specific receptors. Drug-receptor binding depends on the concentration of the drug near the receptor. Its form and concentration near the receptor depend on its physical properties. Orally administered drugs need to be dissolved at the site of absorption in the GIT, and need to traverse several membrane barriers before receptor interactions can commence. As the drug distributes into the various compartments of the body, a certain (small) portion finds itself at the receptor site. Transport and distribution of most drugs are affected by passive diffusion, which depends on lipophilicity, since lipid barriers need to be crossed [27]. Passive transport is well described by the principles of physical chemistry [27–29].
The goal of this book is to examine the components of the multimechanistic processes related to charge state: the pKa of molecules (Chapter 3), lipophilicity (Chapters 4 and 5), solubility (Chapter 6), and permeability (Chapters 7–9), with the aim of advancing improved strategies for in vitro assays related to drug absorption. In high-throughput screening (HTS) these parameters are sometimes viewed simply as numbers, quickly and roughly determined, to be used to rank molecules into “good” and “bad” classes. An attempt will be made to examine this important aspect. In addition, how fundamental, molecular-level interpretations of the physical measurements can help to improve the design of the profiling assays will be examined, with the aim of promoting the data fodder of HTS to a higher level of quality, without compromising the need for high speed [16–24]. Quality measurements in large quantities will lead to improved in silico methods. Simple rules (presented in visually appealing ways), in the spirit of Lipinski’s rule of fives, will be sought, of use not only to medicinal chemists but also to preformulators. This book attempts to make easier the dialog between the medicinal chemists charged with modifying test compounds and the pharmaceutical scientists charged with physicochemical profiling, who need to communicate assay results in an optimally effective manner.
Note
* This excerpt was published in Comprehensive Medicinal Chemistry, Vol. 4, Peter J. Taylor, Hydrophobic Properties of Drugs, pp. 241–294, Copyright Elsevier (1990). Reproduced with permission from Elsevier.
REFERENCES
1. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249 (2000).
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