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Absorption and Drug Development: Solubility, Permeability, and Charge State


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...

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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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Editorial Reviews

From the Publisher
“...a useful book for anyone interested in the measurement of physiochemical parameters that relate to drug permeability.”(Journal of Medicinal Chemistry, Vol. 47, No. 7)

"...useful compilations of data...of use to those working in the agrochemical and environmental industries..." (Chemistry & Industry, 19 January 2004)

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Product Details

  • ISBN-13: 9781118057452
  • Publisher: Wiley
  • Publication date: 5/22/2012
  • Edition number: 2
  • Pages: 744
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.70 (d)

Meet the Author

ALEX AVDEEF, PhD, is the founder of in-ADME Research. He has more than thirty years of experience in chemical instrumentation and software development. Internationally recognized as an authority in the field of solution chemistry, Dr. Avdeef has an extensive list of scholarly publications to his credit.

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Read an Excerpt

Absorption and Drug Development

Solubility, Permeability, and Charge State

By Alex Avdeef

John Wiley & Sons

Copyright © 2003

Alex Avdeef
All right reserved.

ISBN: 0-471-42365-3

Chapter One



The search for new drugs is daunting, expensive, and risky.

If chemicals were confined to molecular weights of less than 600 Da and
consisted of common atoms, the chemistry space is estimated to contain [10.sup.40]
to [10.sup.100] 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. Such libraries have been
directed at biological "targets" (proteins) to identify active molecules, with the
hope that some of these "hits" may someday become drugs. The current target
space is very small-less than 500 targets have been used to discover the known
drugs [3]. This number may expand to several thousand in the near future as
genomics-based technologies uncover new target opportunities [4]. For example,
the human genome mapping has identified over 3000 transcription factors, 580 protein
kinases, 560 G-protein coupled receptors, 200 proteases, 130 ion transporters,
120 phosphatases, over 80 cationchannels, and 60 nuclear hormone receptors [5].

Although screening throughputs have massively increased since the early 1990s,
lead discovery productivity has not necessarily increased accordingly [6-8].
Lipinski has concluded 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:
Absorption and Drug Development: Solubility, Permeability, and Charge State. By Alex Avdeef
"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.

The emergence of combinatorial methods in the 1990s has lead to enormous
numbers of new chemical entities (NCEs) [9]. These are the molecules of the
newest screening libraries. A large pharmaceutical company may screen 3 million
molecules for biological activity each year. Some 30,000 hits are made. Most of
these molecules, however potent, do not have the right physical, metabolic, and
safety properties. Large pharmaceutical companies can cope with about 30 molecules
taken into development each year. A good year sees three molecules reach the
product stage. Some years see none. These are just rough numbers, recited at
various conferences.

A drug product may cost as much as $880 M (million) to bring out. It has been
estimated that about 30% of the molecules that reach development are eventually
rejected due to ADME (absorption, distribution, metabolism, excretion) problems.
Much more money is spent on compounds that fail than on those that succeed
[10,11]. The industry has started to respond by attempting to screen out those
molecules with inappropriate ADME properties during discovery, before the
molecules reach development. However, that has led to another challenge: how
to do the additional screening quickly enough, while keeping costs down [6,12].


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 industry's current
answer is "No." 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] suggests that the order
of testing may change in the near future, for economic reasons. 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.
Computational models are constructed on the basis of the results, and the process
is repeated with newly synthesized molecules, perhaps many times, before satisfactory
hits are revealed. With large numbers of molecules, the process can be very
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 vs. ADME) is likely to continue to be
the subject of future debates [1].


In silico property prediction is needed more than ever to cope with the screening
overload. Improved prediction technologies are continuing to emerge [13,14]. 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 reflect 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, intramolecular H bonding,
intermolecular hydrogen and 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. Few of these physicochemical factors
are well understood by medicinal chemists, who are charged with making new
molecules that overcome ADME liabilities without losing potency.

Another example of a multi-mechanistic probe is the Caco-2 permeability assay
(a topic covered in various sections of the book). Molecules can be transported
across the Caco-2 monolayer by several mechanisms operating simultaneously,
but to varying degrees, such as transcellular passive diffusion, paracellular passive
diffusion, lateral passive diffusion, active influx or efflux mediated by transporters,
passive transport mediated by membrane-bound proteins, receptor-mediated endocytosis,
pH gradient, and electrostatic-gradient driven mechanisms. The P-glycoprotein
(P-gp) 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), 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 [8,15,16]. Metabolism by the Caco-2 system 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'
discovery compounds, which most often have extremely low solubilities [16].

Computational models for single mechanism assays (e.g., biological receptor
affinity) improve as more data are accumulated [1]. In contrast, computational models
for multimechanism assays (e.g., solubility, permeability, charge state) worsen
as more measurements are accumulated [1]. Predictions of human oral absorption
using Caco-2 permeabilities 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 meaningless. Lipinski states
that "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].

The subject of this book is to examine the components of the multimechanistic
processes related to solubility, permeability, and charge state, with the aim of
advancing improved strategies for in vitro assays related to drug absorption.


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. Peter Taylor [17] states:

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 re-invention 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 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 ingenue the impression that he understands what it is all about.
There are great advances and great opportunities in all this, but nevertheless 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 non-specific 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, 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.

Taylor [17] described physicochemical profiling in a comprehensive and compelling
way, but enough has happened since 1990 to warrant a thorough reexamination.
Then, combichem, high-throughput screening (HTS), Caco-2, IAM, CE were in a
preingenuic state; studies of drug-partitioning into liposomes were arcane; instrument
companies took no visible interest in making p[K.sub.a], log P, or solubility analyzers;
there was no biopharmaceutics classification system (BCS); it did not occur to
anyone to do PAMPA. With all that is new, it is a good time to take stock of what we
can learn from the work since 1990. In this book, measurement of solubility,
permeability, lipophilicity, and charge state of drug molecules will be critically
reexamined (with considerable coverage given to permeability, the property least
explored). Fick's law of diffusion [18] in predicting drug absorption will be


In this book we will focus on physicochemical profiling in support of improved
prediction methods for absorption, the "A" in ADME. Metabolism and other
components of ADME will be beyond the scope of this book. Furthermore, we
will focus on properties related to passive absorption, and not directly consider
active transport mechanisms.


Excerpted from Absorption and Drug Development
by Alex Avdeef
Copyright © 2003 by Alex Avdeef.
Excerpted by permission.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

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Table of Contents

Preface xxiii

Preface to the First Edition xxvii

List of Abbreviations xxxi

Nomenclature xxxv

Commercial Trademarks xli

1 Introduction 1

1.1 Bulldozer Searching for a Needle in the Haystack?, 1

1.2 As the Paradigm Turns, 4

1.3 Screen for the Target or ADME First?, 5

1.4 ADME and Multimechanism Screens, 6

1.5 ADME and the Medicinal Chemist, 7

1.6 The “Absorption” in ADME, 8

1.7 It Is Not Just a Number, It Is a Multimechanism, 9

References, 9

2 Transport Model 12

2.1 Permeability–Solubility–Charge State and pH-Partition Hypothesis, 12

2.2 Properties of the Gastrointestinal Tract (GIT), 17

2.3 pH Microclimate, 22

2.4 Intracellular pH Environment, 23

2.5 Tight Junction Complex, 23

2.6 Structure of Octanol, 23

2.7 Biopharmaceutics Classification System, 25

References, 26

3 pKa Determination 31

3.1 Charge State and the pKa, 32

3.2 Methods of Choice for the Determination of the pKa, 34

3.3 Titration with a Glass-Membrane pH Electrode, 34

3.4 Equilibrium Equations and the Ionization Constant, 38

3.5 “Pure Solvent” Activity Scale, 41

3.6 Ionic Strength and Debye–Hückel/Davies Equation, 41

3.7 “Constant Ionic Medium” Activity Scale, 43

3.8 Temperature Dependence of pKa Values, 47

3.9 Electrode Calibration and Standardization, 55

3.10 Bjerrum Plot: Most Useful Graphical Tool in pKa Analysis, 66

3.11 Cosolvent Methods for pKa Determination of Practically Insoluble Substances, 78

3.12 Other Methods for pKa Measurement, 96

3.13 pKa Microconstants, 102

3.14 pKa Compilations, 107

3.15 pKa Prediction Programs, 107

3.16 Database of pKa (25°C and 37°C), 107

Appendix 3.1 Quick Start: Determination of the pKa of Codeine, 127

Appendix 3.2 Tutorial for Measurements with Glass-Membrane pH Electrode, 130

Appendix 3.3 pH Convention Adopted by IUPAC and Supported by NIST, 137

Appendix 3.4 Liquid-Junction Potentials (LJP), 140

Appendix 3.5 pKa Refi nement by Weighted Nonlinear Regression, 146

Appendix 3.6 Molality to Molarity Conversion, 157

References, 158

4 Octanol–Water Partitioning 174

4.1 Overton–Hansch Model, 175

4.2 Tetrad of Equilibria, 175

4.3 Conditional Constants, 177

4.4 log P Data Sources, 178

4.5 log D Lipophilicity Profile, 178

4.6 Ion-Pair Partitioning, 183

4.7 Micro-log P, 187

4.8 Methods for log P Determination, 188

4.9 Dyrssen Dual-Phase Titration log P Method, 189

4.10 Ionic Strength Dependence of log P, 194

4.11 Temperature Dependence of log P, 194

4.12 Calculated versus Measured log P of Research Compounds, 194

4.13 log D versus pH Case Study: Procaine Structural Analogs, 196

4.14 Database of Octanol–Water log PN, log PI, and log D7.4, 201

References, 209

5 Liposome–Water Partitioning 220

5.1 Biomimetic Lipophilicity, 221

5.2 Tetrad of Equilibria and Surface Ion-Pairing (SIP), 221

5.3 Data Sources, 222

5.4 Location of Drugs Partitioned into Bilayers, 222

5.5 Thermodynamics of Partitioning: Entropy- or Enthalpy-Driven?, 223

5.6 Electrostatic and Hydrogen Bonding in a Low Dielectric Medium, 224

5.7 Water Wires, H+/OH− Currents, and Permeability of Amino Acids and Peptides, 227

5.8 Preparation Methods: MLV, SUV, FAT, LUV, ET, 228

5.9 Experimental Methods, 229

5.10 Prediction of log PMEM from log POCT, 229

5.11 log DMEM, diff log PMEM, and Prediction of log PSI M P EM from log PI OCT, 233

5.12 Three Indices of Lipophilicity: Liposomes, IAM, and Octanol, 238

5.13 Getting It Wrong from One-Point log DMEM Measurement, 239

5.14 Partitioning into Charged Liposomes, 240

5.15 pKa MEM Shifts in Charged Liposomes and Micelles, 240

5.16 Prediction of Absorption from Liposome Partition Studies?, 241

5.17 Database of log PMEM and log PSI M P EM, 242

References, 245

6 Solubility 251

6.1 It’s Not Just a Number, 252

6.2 Why Is Solubility Measurement Difficult?, 252

6.3 Mathematical Models for Solubility–pH Profiles, 255

6.4 Experimental Methods, 270

6.5 Correction for the DMSO Effect by the “Δ-Shift” Method, 287

6.6 Case Studies (Solubility–pH Profi les), 289

6.7 Limits of Detection—Precision versus Accuracy, 306

6.8 Data Sources and the “Ionizable-Drug Problem,” 308

6.9 Database of log S0, 308

References, 310

7 Permeability—PAMPA 319

7.1 Permeability in the Gastrointestinal Tract, 320

7.2 Historical Developments in Permeability Models, 323

7.3 Rise of PAMPA—A Useful Tool in Early Drug Discovery, 336

7.4 PAMPA-HDM, -DOPC, -DS Models Compared, 343

7.5 Modeling Biological Membranes, 354

7.6 Permeability–pH Relationship and the Mitigating Effect of the Aqueous Boundary Layer, 362

7.7 pKa FLUX-Optimized Design (pOD), 386

7.8 Cosolvent PAMPA, 389

7.9 UV versus LC/MS Detection, 397

7.10 Assay Time Points, 400

7.11 Buffer Effects, 402

7.12 Apparent Filter Porosity, 404

7.13 PAMPA Errors: Intra-Plate and Inter-Plate Reproducibility, 407

7.14 Human Intestinal Absorption (HIA) and PAMPA, 409

7.15 Permeation of Permanently Charged Molecules, 416

7.16 Permeation of Zwitterions/Ampholytes—In Combo PAMPA, 424

7.17 PAMPA in Formulation: Solubilizing Excipient Effects, 433

7.18 Database of Double-Sink PAMPA log P0, log Pm 6.5, and log Pm 7.4, 448

Appendix 7.1 Quick Start: Double-Sink PAMPA of Metoprolol, 460

Appendix 7.2 Permeability Equations, 465

Appendix 7.3 PAMPA Paramembrane Water Channels, 481

References, 484

8 Permeability: Caco-2/MDCK 499

8.1 Permeability in the Gastrointestinal Tract, 500

8.2 Cell-Based In Vitro Permeability Model, 505

8.3 In Situ Human Jejunum Permeability (HJP) Model, 514

8.4 Passive Intrinsic Permeability Coefficients of Caco-2 and MDCK Compared, 515

8.5 Theory (Stage 1): Paracellular Leakiness and Size Exclusion in Caco-2, MDCK, and 2/4/A1 Cell Lines, 516

8.6 Theory (Stage 2): Regression Method for In Vitro Cellular Permeability, 524

8.7 Case Studies of Cell-Based Permeability as a Function of pH, 525

8.8 Human Jejunal Permeability Predicted Directly from Caco-2/MDCK, 533

8.9 Caco-2/MDCK Database and Its In Combo PAMPA Prediction, 550

References, 563

9 Permeability: Blood–Brain Barrier 575

9.1 The Blood–Brain Barrier: A Key Element for Drug Access to the Central Nervous System, 576

9.2 The Blood–Brain Barrier, 576

9.3 Noncellular BBB Models, 580

9.4 In Vitro BBB Cell-Based Models, 586

9.5 In Vivo BBB Models, 589

9.6 Paradigm Shift, 592

9.7 In Silico BBB Models, 608

9.8 Biophysical Analysis of In Vitro Endothelial Cell Models, 608

9.9 In Situ Brain Perfusion Analysis of Flow, 618

9.10 In Combo PAMPA–BBB Model for Passive BBB Permeability, 631

References, 663

10 Summary and Some Simple Approximations 681

Index 685

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