Molecular Drug Properties

Measurement and Prediction

Edited by

Raimund Mannhold

WILEY-VCH

WILEY-VCH Verlag GmbH & Co. KGaA

Contents

List of Contributors XIX

Preface XXIIIA Personal Foreword XXV

I Introduction

1 A Fresh Look at Molecular Structure and Properties 3

Bernard Testa, Giulio Vistoli, and Alessandro Pedretti

1.1 Introduction 31.2 Core Features: The Molecular "Genotype" 51.2.1 The Argument 51.2.2 Encoding the Molecular "Genotype" 61.3 Observable and Computable Properties: The Molecular "Phenotype"1.3.1 Overview 61.3.2 Equilibria 81.3.3 Stereoelectronic Features 91.3.4 Recognition Forces and Molecular Interaction Fields (MIFs) 91.3.5 Macroscopic Properties 91.4 Molecular Properties and their Adaptability: The Property Space of

Molecular Entities 101.4.1 Overview 101.4.2 The Versatile Behavior of Acetylcholine 111.4.3 The Carnosine-Carnosinase Complex 151.4.4 Property Space and Dynamic QSAR Analyses 191.5 Conclusions 21

2 Physicochemical Properties in Drug Profiling 25Han van de Waterbeemd

2.1 Introduction 262.2 Physicochemical Properties and PharmacoMnetics 282.2.1 DMPK 282.2.2 Lipophilicity - Permeability - Absorption 28

Molecular Drug Properties. Measurement and Prediction. R. Mannhold (Ed.)Copyright © 2008 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimISBN: 978-3-527-31755-4

VIII Contents

2.2.3 Estimation of Volume of Distribution from Physical Chemistry 30

2.2.4 PPB and Physicochemical Properties 302.3 Dissolution and Solubility 302.3.1 Calculated Solubility 322.4 Ionization (pIQ 322.4.1 Calculated pj<^ 332.5 Molecular Size and Shape 332.5.1 Calculated Size Descriptors 332.6 H-bonding 342.6.1 Calculated H-bonding descriptors 342.7 Lipophilicity 352.7.1 Calculated log P and log D 372.8 Permeability 372.8.1 Artificial Membranes and PAMPA 372.8.1.1 In Sffico PAMPA 392.8.2 IAM, Immobilized Liposome Chromatography (ILC), Micellar

Electrokinetic Chromatography (MEKC) and Biopartitioning MicellarChromatography (BMC) 39

2.8.3 Liposome Partitioning 392.8.4 Biosensors 402.9 Amphiphilicity 402.10 Drug-like Properties 402.11 Computation versus Measurement of Physicochemical Properties 422.11.1 QSAR Modeling 422.11.2 In Combo: Using the Best of two Worlds 422.12 Outlook 43

II Electronic Properties and H-Bonding

3 Drug Ionization and Physicochemical Profiling 55Alex Avdeef

3.1 Introduction 553.1.1 Absorption, the Henderson-Hasselbalch Equation and the pH-partirion

Hypothesis 563.1.2 "Shift-in-the-pKa" 573.2 Accurate Determination of Ionization Constants 583.2.1 Definitions - Activity versus Concentration Thermodynamic Scales 583.2.2 Potentiometric Method 603.2.3 pH Scales 603.2.4 Cosolvent Methods 603.2.5 Recent Improvements in the Potentiometric Method Applied to

Sparingly Soluble Drugs 623.2.6 Spectrophotometric Measurements 613.2.7 Use of Buffers in UV Spectrophotometry 623.2.8 pKa Prediction Methods and Software 63

Contents IX

3.2.9 Tabulations of Ionization Constants 633.3 "Octanol" and "Membrane" pKa in Partition Coefficients

Measurement 633.3.1 Definitions 643.3.2 Shape of the Log D^t-pH Lipophilidty Profiles 653.3.3 The "diff 3-4" Approximation in logDoct-pH Profiles for Monoprotic

Molecules 663.3.4 Liposome-Water Partitioning and the "diff 1-2" Approximation in

log .DMEM-pH Profiles for Monoprotic Molecules 673.4 "Gibbs" and Other "Apparent" pKa in Solubility Measurement 683.4.1 Interpretation of Measured Solubility of Ionizable Drug-like

Compounds can be Difficult 683.4.2 Simple Henderson-Hasselbalch Equations 683.4.3 Gibbs' pKa and the "sdiff3-4" Approximation 693.4.4 Aggregation Equations and "Shift-in-the-pKa" Analysis 723.5 "Flux" and other "Apparent" pKa in Permeability

Measurement 743.5.1 Correcting Permeability for the ABL Effect by the pKa

LUX

Method 743.5.2 Membrane Rate-Limiting Transport (Hydrophilic Molecules) 763.5.3 Water Layer Rate-Limiting Transport (Lipophilic Molecules) 773.5.4 Ionic-species Transport in PAMPA 773.6 Conclusions 78

4 Electrotopological State Indices 85

Ovidiu Ivanciuc

4.1 Introduction 864.2 E-state Indices 874.2.1 Molecular Graph Representation of Chemical Structures 874.2.2 The Randic—Kier-Hall Molecular Connectivity Indices 884.2.3 The E-state Index 894.2.4 Hydrogen Intrinsic State 904.2.5 Bond E-state Indices 904.2.6 E-state 3D Field 914.2.7 Atom-type E-state Indices 914.2.8 Other E-state Indices 914.3 Application of E-State Indices in Medicinal Chemistry 924.3.1 Prediction of Aqueous Solubility 934.3.2 QSAR Models 934.3.3 Absorption, Distribution, Metabolism, Excretion and Toxicity

(ADMET) 964.3.4 Mutagenicity and Carcinogenicity 2004.3.5 Anticancer Compounds 2024.3.6 Virtual Screening of Chemical Libraries 2034.4 Conclusions and Outlook 205

X Contents

5 Polar Surface Area 212Peter Ertl

5.1 Introduction 2225.2 Application of PSA for Prediction of Drug Transport

Properties 2235.2.1 Intestinal Absorption 1145.2.2 Blood-Brain Barrier Penetration 2155.2.3 Other Drug Characteristics 2275.3 Application of PSA in Virtual Screening 2275.4 Calculation of PSA 2295.5 Correlation of PSA with other Molecular Descriptors 2225.6 Conclusions 223

6 H-bonding Parameterization in Quantitative Structure-ActivityRelationships and Drug Design 227Oleg Raevsky

6.1 Introduction 2286.2 Two-dimensional H-bond Descriptors 2296.2.1 Indirect H-bond Descriptors 1296.2.2 Indicator Variables 1316.2.3 Two-dimensional Thermodynamics Descriptors 2326.3 Three-dimensional H-bond Descriptors 2346.3.1 Surface H-bond Descriptors 2346.3.2 SYBYL H-bond Parameters 2366.3.3 Distance H-bond Potentials 2366.4 Application of H-bond Descriptors in QSAR Studies and Drug

Design 1426.4.1 Solubility and Partitioning of Chemicals in Water-Solvent-Gas

Systems 1436.4.2 Permeability and Absorption in Humans 1456.4.3 Classification of Pharmacokinetic Properties in Computer-aided

Selection of Useful Compounds 2476.4.4 Chemical Interactions with Biological Targets 2486.4.5 Aquatic Toxidty 1496.5 Conclusions 149

III Conformations

7 Three-dimensional Structure Generation 257Jens Sadowski

7.1 Introduction 1577.2 Problem Description 2607.2.1 Computational Requirements 2607.2.2 General Problems 2627.2.3 What 3D Structures Do You Need? 262

Contents XI

7.3 Concepts 2637.3.1 Classification of Strategies 2637.3.2 Standard Values 2647.3.3 Fragments 2667.3.4 Rules 2697.3.5 Quality Control 2737.3.6 Comparison of 3D Structures 2747.4 Practical Aspects 2757.4.1 Brief Overview and Evaluation of Available Software 2757.4.2 Practical Recommendations 1787.5 Conclusions 280

8 Exploiting Ligand Conformations in Drug Design 183

Jonas Bostrom and Andrew Grant8.1 Introduction 2838.1.1 Molecular Geometry and Energy Minimizations 1848.1.2 Conformational Analysis Techniques 2858.1.2.1 The Relevance of the Input Structure 1868.1.3 Software 1868.2 Generating Relevant Conformational Ensembles 1878.2.1 Conformational Energy Cutoffs 2878.2.1.1 Thermodynamics of Ligand Binding 1888.2.1.2 Methods and Computational Procedure 1888.2.1.3 Calculated Conformational Energy Cutoff Values 1908.2.1.4 Importance of Using Solvation Models 1908.2.2 Diverse or Low-Energy Conformational Ensembles? 2928.2.2.1 Methods and Computational Procedure 1938.2.2.2 Reproducing Bioactive Conformations Using Different Duplicate

Removal Values 1948.2.3 Combinatorial Explosion in Conformational Analysis 2958.2.3.1 Representing a Conformational Ensemble by a Single

Conformation 2968.3 Using Conformational Effects in Drug Design 2988.3.1 Conformational Restriction 2988.3.2 Shape-Based Scaffold Hopping 2008.4 Conclusions 202

9 Conformational Analysis of Drugs by Nuclear Magnetic ResonanceSpectroscopy 207

Burkhard Luy, Andreas Frank, and Horst Kessler

9.1 Introduction 2089.2 NMR Parameters for Conformational Analysis 2219.2.1 NOE/ROE 2129.2.2 Residual Dipolar Couplings (RDCs) 2179.2.2.1 Dipolar Interaction 228

XII Contents

9.2.2.2 Alignment Media 2199.2.2.3 Measurement of RDCs 2219.2.2.4 Structural Interpretation of RDCs 2229.2.3 Other Anisotropic NMR Parameters 2259.2.3.1 Residual Quadrupolar Coupling (RQCs) 2259.2.3.2 Residual Chemical Shift Anisotropy (RCSA) 2259.2.3.3 Pseudo-Contact Shift (PCS) 2269.2.4 Scalar Coupling Constants (J-couplings) 2269.2.5 Cross-Correlated Relaxation (CCR) 2299.3 Conformation Bound to the Receptor 2309.3.1 Ligand Conformation 2329.3.1.1 Exchange-transferred NOE (etNOE) 2329.3.1.2 Exchange-transferred RDCs (etRDCs) 2339.3.1.3 Exchange-transferred PCS (etPCS) 2349.3.1.4 Exchange-transferred CCR (etCCR) 2349.3.2 Ligand—receptor Binding Surface 2359.3.2.1 STD Spectroscopy 2359.3.2.2 Paramagnetic Relaxation Enhancement (PRE) 2359.4 Refinement of Conformations by Computational Methods 2369.4.1 Distance Geometry (DG) 2379.4.1.1 Distance Matrices 2389.4.1.2 Metrization 2389.4.1.3 Embedding 2389.4.2 Molecular Dynamics (MD) 2399.4.2.1 Preparation of an MD Simulation 2399.4.2.2 MD Simulations in vacuo 2409.4.2.3 Ensemble- and Time-averaged Distance Restraints 2419.4.2.4 Restrained MD (rMD) 2429.4.2.5 Free MD (fMD) 2429.4.2.6 Simulated Annealing (SA) 2439.4.3 Conclusions 243

IV Solubility

10 Drug Solubility in Water and Dimethylsulfoxide 257Christopher Lipinski

10.1 Introduction 25710.2 Water Solubility 25810.2.1 Where does Drug Poor Water Solubility Come From? 25810.2.2 Water Solubility is Multifactorial 25910.2.3 Water Solubility and Oral Absorption 25910.2.4 Importance and Guidelines 26010.2.5 Intestinal Fluid Solubility 261

10.3 Early Discovery Water Solubility and Biological Testing 26210.3.1 HTS Application 262

Contents XIII

10.3.2 Improving HTS Assay Quality 26210.4 Water Solubility Measurement Technology 26310.4.1 Discovery-stage Water Solubility Advantages 26310.4.2 Discovery-stage Water Solubility Limitations 26410.4.3 In Vivo Dosing Application 26410.4.4 In Vivo SAR to Guide Chemistry 26410.4.5 Discovery Solubility Assay Endpoint Detection 26510.4.6 Advantages of Out-of-solution Detection 26510.4.7 Limitations of Out-of-solution Detection 26510.5 Compound Ionization Properties 26610.5.1 Acids 26710.5.2 Importance and Measurement 26710.5.3 Bases 26810.5.4 Importance and Measurement 26810.5.5 Neutral Compounds 26910.5.6 Importance and Measurement 26910.5.7 Zwitterions 27010.5.8 Importance and Measurement 27010.6 Compound Solid-state Properties 27010.6.1 Solid-state Properties and Water Solubility 27010.6.2 Amorphous 27210.6.3 Crystalline 27210.6.4 Salt Forms 27210.6.5 Ostwald's Rules 27210.6.6 Isolation Procedure Changes 27310.6.7 Greaseballs 27310.6.8 Properties 27310.6.9 Measuring and Fixing Solubility 27310.6.10 Brickdust 27410.6.11 Properties 27410.6.12 Measuring and Fixing Solubility 27410.6.13 Preformulation Technology in Early Discovery 27510.6.14 Discovery Development Interface Water Solubility 27510.6.15 Thermodynamic Equilibrium Measurements 27510.7 DMSO Solubility 27610.7.1 Where Does Poor DMSO Solubility Come From? 27710.7.2 DMSO Solubility is Multifactorial 27710.7.3 DMSO Compared to Water Solubility 27810.7.4 DMSO Compound Storage Stocks and Compound Integrity 27810.7.5 DMSO Solubility and Precipitation 27910.7.6 DMSO Water Content 27910.7.7 Freeze-Thaw Cycles 28010.7.8 Fixing Precipitation 28010.7.9 Short-term End-user Storage of DMSO Stocks 28210.8 Conclusions 282

XIV Contents

11 Challenge of Drug Solubility Prediction 283

Andreas Klamt and Brian J Smith

11.1 Importance of Aqueous Drug Solubility 28311.2 Thermodynamic States Relevant for Drug Solubility 28511.3 Prediction of AGfts 29011.4 Prediction of Liquid Solubility with COSMO-RS 29211.5 Prediction of Liquid Solubility with Molecular Dynamics (MD) and

Monte Carlo (MC) Methods 29611.6 Group-Group Interaction Methods 29811.7 Nonlinear Character of Log Sw 29811.8 QSPRs 30211.9 Experimental Solubility Datasets 30211.10 Atom Contribution Methods, Electrotopological State (E-state) Indices

and GCMs 30411.11 Three-dimensional Geometry-based Models 30511.12 Conclusions and Outlook 306

V Lipophilicity

12 Lipophilicity: Chemical Nature and Biological Relevance 325

Giulia Caron and Giuseppe Ermondi

12.1 Chemical Nature of Lipophilicity 31512.1.1 Chemical Concepts Required to Understand the Significance of

Lipophilicity 32512.1.1.1 Molecular Charges and Dipoles 32512.1.1.2 Intermolecular Forces 32812.1.1.3 Solvation and Hydrophobic Effect 32812.1.2 Lipophilicity Systems 32012.1.3 Determination of Log P and Log D 32212.1.4 Traditional Factorization of lipophilicity (Only Valid for Neutral

Species) 32212.1.5 General Factorization of Lipophilicity (Valid For

All Species) 32412.2 Biological Relevance of Lipophilicity 32512.2.1 Lipophilicity and Membrane Permeation 32512.2.2 Lipophilicity and Receptor Affinity 32612.2.3 Lipophilicity and the Control of Undesired Human Ether-a-go-go-

related Gene (hERG) Activity 32712.3 Conclusions 328

13 Chromatographic Approaches for Measuring Log P 332

Sophie Martel, Davy Guillarme, Yveline Henchoz, Alexandra Gotland,Jean-Luc Veuthey, Serge Rudaz, and Pierre-Alain Corrupt

13.1 Introduction 33213.2 Lipophilicity Measurements by RPLC: Isocratk Conditions 33213.2.1 Main Features of RPLC Approaches 333

Contents XV

13.2.1.1 Principles of Lipophilicity Determination 33313.2.1.2 Retention Factors Used as RPLC Lipophilicity Indices 33313.2.2 Relation Between Logkw and LogPoct Using Different Conventional

Stationary Phases 33413.2.2.1 Conventional Apolar Stationary Phases 33413.2.2.2 IAMs 33613.2.3 Some Guidelines for the Selection of Adequate Experimental

Conditions 33713.2.3.1 Organic Modifiers 33713.2.3.2 Addition of 1-Octanol in the Mobile Phase 33813.2.3.3 Column Length 33813.2.4 Limitations of the Isocratic Approach for log P Estimation 33913.3 Lipophilicity Measurements by RPLC: Gradient Approaches 33913.3.1 Gradient Elution in RPLC 33913.3.2 Significance of High-performance Liquid Chromatography (HPLC)

Lipophilicity Indices 34013.3.2.1 General Equations of Gradient Elution in HPLC 34013.3.3 Determination of log kw from Gradient Experiments 34213.3.3.1 From a Single Gradient Run 34213.3.3.2 From Two Gradient Runs 34113.3.3.3 With Optimization Software and Two Gradient Runs 34213.3.4 Chromatographic Hydrophobicity Index (CHI) as a Measure of

Hydrophobicity 34213.3.4.1 Experimental Determination of CHI 34213.3.4.2 Advantages/Limitations of CHI 34213.3.5 Experimental Conditions and Analysis of Results 34313.3.5.1 Prediction of log P and Comparison of Lipophilicity Indices 34313.3.6 Approaches to Improve Throughput 34413.3.6.1 Fast Gradient Elution in RPLC 34413.3.6.2 Use of MS Detection 34513.3.7 Some Guidelines for a Typical Application of Gradient RPLC in

Physicochemical Profiling 34613.3.7.1 A Careful Selection of Experimental Conditions 34613.3.7.2 General Procedure for logfcw Determination 34713.3.7.3 General Procedure for CHI Determination 34713.4 Lipophilicity Measurements by Capillary Elecrrophoresis (CE) 34713.4.1 MEKC 34813.4.2 MEEKC 34913.4.3 LEKC/VEKC 34913.5 Supplementary Material 350

14 Prediction of Log P with Substructure-based Methods 357

Raimund Mannhold and Claude Ostermann

14.1 Introduction 35714.2 Fragmental Methods 358

XVI Contents

14.2.1 If System 35914.2.2 KLOGP 36214.2.3 KOWWIN 36314.2.4 CLOGP 36414.2.4.1 Fragmentation Rules 36514.2.4.2 Structural Factors 36514.2.4.3 Interaction Factors: Aliphatic Proximity 36514.2.4.4 Interaction Factors: Electronic Effects through 7i-Bonds 36614.2.4.5 Interaction Factors: Special Ortho Effects 36614.2.5 ACD/LogP 36714.2.6 AB/LogP 36814.3 Atom-based Methods 37114.3.1 Ghose-Crippen Approach 37114.3.2 XLOGP 37314.4 Predictive Power of Substructure-based Approaches 374

15 Prediction of Log P with Property-based Methods 381Igor V. Tetko and Gennadiy I. Poda

15.1 Introduction 38115.2 Methods Based on 3D Structure Representation 38215.2.1 Empirical Approaches 38215.2.1.1 LSER 38215.2.1.2 SLIPPER 38315.2.1.3 SPARC 38415.2.2 Methods Based on Quantum Chemical Semiempirical

Calculations 38515.2.2.1 Correlation of Log P with Calculated Quantum Chemical

Parameters 38515.2.2.2 QLOGP: Importance of Molecular Size 38515.2.3 Approaches Based on Continuum Solvation Models 38615.2.3.1 GBLOGP 38615.2.3.2 COSMO-RS (Full) Approach 38715.2.3.3 COSMOfrag (Fragment-based) Approach 38815.2.3.4 Ab Initio Methods 38815.2.3.5 QuantlogP 38915.2.4 Models Based on MD Calculations 38915.2.5 MLP Methods 39015.2.5.1 Early Methods of MLP Calculations 39015.2.5.2 Hydrophobic Interactions (HINT) 39215.2.5.3 Calculated Lipophilicity Potential (CLIP) 39115.2.6 LogP Prediction Using Lattice Energies 39215.3 Methods Based on Topological Descriptors 39215.3.1 MLOGP 39215.3.2 Graph Molecular Connectivity 39215.3.2.1 TLOGP 393

Contents XVII

15.3.3 Methods Based on Electrotopological State (E-state)Descriptors 393

15.3.3.1 VLOGP 39315.3.3.2 ALOGPS 39415.3.3.3 CSlogP 39415.3.3.4 A_S+logP 39415.4 Prediction Power of Property-based Approaches 39415.4.1 Datasets Quality and Consistence 39515.4.2 Background Models 39515.4.3 Benchmarking Results 39715.4.4 Pitfalls of the Benchmarking 39715.4.4.1 Do We Compare Methods or Their Implementations? 39715.4.4.2 Overlap in the Training and Benchmarking Sets 39915.4.4.3 Zwitterions 39915.4.4.4 Tautomers and Aromaticity 40015.5 Conclusions 402

16 The Good, the Bad and the Ugly of Distribution Coefficients: CurrentStatus, Views and Outlook 407

Franco Lombardo, Bernard Faller, Marina Shaiaeva, Igor Tetko, and

Suzanne Tilton

16.1 Log D and Log P 40816.1.1 Definitions and Equations 40816.1.2 Is There Life After Octanol? 41016.1.3 Log? or LogD? 41216.1.4 ADME Applications 41316.2 Issues and Automation in the Determination of Log D 41416.2.1 Shake-Flask Method 41416.2.2 Potentiometric Method 41516.2.3 Chromatographic Methods 41616.2.4 Electrophoretic Methods 42816.2.5 IAMs 41916.2.6 Applications Perspective 42916.3 pH-partition Theory and Ion-pairing 42216.3.1 General Aspects and Foundation of the pH-partition Theory 42216.3.2 Ion-pairing: In Vitro and In Vivo Implications 42216.3.2.1 Ion-pairing In Vitro 42116.3.2.2 Ion-pairing In Vivo 42416.4 Computational Approaches 42516.4.1 Methods to Predict Log D at Arbitrary pH 42516.4.2 Methods to Predict Log D at Fixed pH 42716.4.3 Issues and Needs 42816.4.3.1 LogD Models in ADMET Prediction 42816.4.3.2 Applicability Domain of Models 42916.5 Some Concluding Remarks: The Good, the Ba/i and the Ugly 430

XVIII Contents

VI Drug- and Lead-likeness

17 Properties Guiding Drug- and Lead-likeness 442Sorel Muresan and Jens Sadowski

17.1 Introduction 44217.2 Properties of Leads and Drugs 44217.2.1 Simple Molecular Properties 44217.2.2 Chemical Filters 44517.2.3 Correlated Properties 44617.2.4 Property Trends and Property Ranges 44817.2.5 Ligand Efficiency 45017.3 Drug-likeness as a Classification Problem 45317.4 Application Example: Compound Acquisition 45517.5 Conclusions 457

Index 463