Drug Metabolism Prediction, Volume 63

Overview

The first professional reference on this highly relevant topic, for drug developers, pharmacologists and toxicologists.
The authors provide more than a systematic overview of computational tools and knowledge bases for drug metabolism research and their underlying principles. They aim to convey their expert knowledge distilled from many years of experience in the field. In addition to the fundamentals, computational approaches and their applications, this volume provides expert ...

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Overview

The first professional reference on this highly relevant topic, for drug developers, pharmacologists and toxicologists.
The authors provide more than a systematic overview of computational tools and knowledge bases for drug metabolism research and their underlying principles. They aim to convey their expert knowledge distilled from many years of experience in the field. In addition to the fundamentals, computational approaches and their applications, this volume provides expert accounts of the latest experimental methods for investigating drug metabolism in four dedicated chapters. The authors discuss the most important caveats and common errors to consider when working with experimental data.
Collating the knowledge gained over the past decade, this practice-oriented guide presents methods not only used in drug development, but also in the development and toxicological assessment of cosmetics, functional foods, agrochemicals, and additives for consumer goods, making it an invaluable reference in a variety of disciplines.

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Product Details

Meet the Author

Dr. Johannes Kirchmair is currently a lead researcher at the Institute of Pharmaceutical Sciences at ETH Zurich, Switzerland. He received his PhD in medicinal chemistry from the University of Innsbruck, Austria, and subsequently worked as an application scientist for Inte:Ligand in Vienna, Austria, before returning to his Alma Mater as an Assistant Professor. In 2009 he joined BASF SE Ludwigshafen, Germany, where he was responsible for the computational optimization of fungicide leads. From 2010 to 2013 he worked as a senior research associate at the Unilever Centre for Molecular Sciences Informatics, University of Cambridge (UK), where he developed computational methods for drug metabolism prediction. His main research interests include molecular informatics, bioinformatics, medicinal chemistry, and drug design.

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Table of Contents

List of Contributors XVII

Preface XXI

A Personal Foreword XXIII

Part One Introduction 1

1 Metabolism in Drug Development 3
Bernard Testa

1.1 What? An Introduction 3

1.2 Why? Metabolism in Drug Development 4

1.2.1 The Pharmacological Context 4

1.2.2 Consequences of Drug Metabolism on Activity 6

1.2.3 Adverse Consequences of Drug Metabolism 7

1.2.4 Impact of Metabolism on Absorption, Distribution, and Excretion 10

1.3 How? From Experimental Results to Databases to Expert Software Packages 11

1.3.1 The Many Factors Influencing Drug Metabolism 11

1.3.2 Acquiring and Interpreting Experimental Results 13

1.3.3 Expert Software Tools and Their Domains of Applicability 14

1.3.4 Roads to Progress 16

1.4 Who? Human Intelligence as a Conclusion 17

References 19

Part Two Software, Web Servers and Data Resources to Study Metabolism 27

2 Software for Metabolism Prediction 29
Lu Tan and Johannes Kirchmair

2.1 Introduction 29

2.2 Ligand-Based and Structure-Based Methods for Predicting Metabolism 30

2.3 Software for Predicting Sites of Metabolism 38

2.3.1 Knowledge-Based Systems 38

2.3.2 Molecular Interaction Fields 39

2.3.3 Docking 39

2.3.4 Reactivity Models 40

2.3.5 Data Mining and Machine Learning Approaches 41

2.3.6 Shape-Focused Approaches 42

2.4 Software for Predicting Metabolites 43

2.4.1 Knowledge-Based Systems 44

2.4.2 Data Mining and Machine Learning Approaches 46

2.4.3 Molecular Interaction Fields 46

2.5 Software for Predicting Interactions of Small Molecules with Metabolizing Enzymes 46

2.6 Conclusions 48

References 49

3 Online Databases and Web Servers for Drug Metabolism Research 53
David S. Wishart

3.1 Introduction 53

3.2 Online Drug Metabolism Databases 54

3.2.1 DrugBank 57

3.2.2 HMDB 59

3.2.3 PharmGKB 59

3.2.4 Wikipedia 60

3.2.5 PubChem 61

3.2.6 Synoptic Databases: ChEBI, ChEMBL, KEGG, and BindingDB 61

3.2.7 Specialized Databases: UM-BBD, SuperCYP, PKKB, and PK/DB 63

3.2.8 Online Database Summary 64

3.3 Online Drug Metabolism Prediction Servers 65

3.3.1 Metabolite Predictors 66

3.3.2 SoM Predictors 66

3.3.3 Specialized Predictors 68

3.3.4 ADMET Predictors 70

3.3.5 Web Server Summary 71

References 71

Part Three Computational Approaches to Study Cytochrome P450 Enzymes 75

4 Structure and Dynamics of Human Drug-Metabolizing Cytochrome P450 Enzymes 77
Ghulam Mustafa, Xiaofeng Yu, and Rebecca C. Wade

4.1 Introduction 77

4.2 Three-Dimensional Structures of Human CYPs 78

4.3 Structural Features of CYPs 78

4.3.1 CYP-Electron Transfer Protein Interactions 81

4.3.2 Substrate Recognition Sites 82

4.3.3 Structural Variability and Substrate Specificity Profiles 83

4.3.3.1 CYP1A2 83

4.3.3.2 CYP2A6 85

4.3.3.3 CYP2C9 85

4.3.3.4 CYP2D6 86

4.3.3.5 CYP2E1 87

4.3.3.6 CYP3A4 87

4.4 Dynamics of CYPs 88

4.4.1 Active Site Flexibility 88

4.4.2 Active Site Solvation 93

4.4.3 Active Site Access and Egress Pathways 93

4.4.4 MD Simulations of CYPs in Lipid Bilayers 96

4.5 Conclusions 96

References 97

5 Cytochrome P450 Substrate Recognition and Binding 103
Andrew G. Leach and Nathan J. Kidley

5.1 Introduction 103

5.2 Substrate Recognition in the Catalytic Cycle of CYPs 103

5.3 Substrate Identity in Various Species 104

5.4 Structural Insight into Substrate Recognition by CYPs 107

5.4.1 CYP1A1, CYP1A2, and CYP1B1 108

5.4.2 CYP2A6 108

5.4.3 CYP2A13 109

5.4.4 CYP2C8 110

5.4.5 CYP2C9 112

5.4.6 CYP2D6 112

5.4.7 CYP2E1 113

5.4.8 CYP2R1 113

5.4.9 CYP3A4 115

5.4.10 CYP8A1 115

5.4.11 CYP11A1 116

5.4.12 CYP11B2 118

5.4.13 CYP19A1 118

5.4.14 CYP46A1 119

5.4.15 General Insights from Protein–Ligand Crystal Structures 119

5.5 The Challenges of Using Docking for Predicting Kinetic Parameters 120

5.6 Substrate Properties for Various Human Isoforms 120

5.6.1 Kinetic Parameters Km and kcat and Their Relationship with Substrate and Protein Structure 124

5.7 Conclusions 128

References 128

6 QM/MM Studies of Structure and Reactivity of Cytochrome P450 Enzymes: Methodology and Selected Applications 133
Sason Shaik, Hui Chen, Dandamudi Usharani, and Walter Thiel

6.1 Introduction 133

6.2 QM/MM Methods 135

6.2.1 Methodological Issues in QM/MM Studies 136

6.2.1.1 QM/MM Partitioning 136

6.2.1.2 QM Methods 137

6.2.1.3 MM Methods 138

6.2.1.4 Subtractive versus Additive QM/MM Schemes 139

6.2.1.5 Electrostatic QM/MM Interactions 139

6.2.1.6 QM/MM Boundary Treatments 139

6.2.1.7 QM/MM Geometry Optimization 140

6.2.1.8 QM/MM Molecular Dynamics and Free Energy Calculations 140

6.2.1.9 QM/MM Energy versus Free Energy Calculations 141

6.2.2 Practical Issues in QM/MM Studies 141

6.2.2.1 QM/MM Software 141

6.2.2.2 QM/MM Setup 142

6.2.2.3 Accuracy of QM/MM Results 143

6.2.2.4 QM/MM Geometry Optimization 143

6.2.2.5 Extracting Insights from QM/MM Calculations 144

6.3 Selected QM/MM Applications to Cytochrome P450 Enzymes 144

6.3.1 Formation of Cpd I from Cpd 0 146

6.3.1.1 Conversion of Cpd 0 into Cpd I in the T252X Mutants 148

6.3.2 Properties of Cpd I 151

6.3.2.1 Cpd I Species of Different Cytochrome P450s 154

6.3.3 The Mechanism of Cytochrome P450 StaP 155

6.3.4 The Mechanism of Dopamine Formation 160

6.3.4.1 The Electrostatic Effect is Not Due to Simple Bulk Polarity 163

6.4 An Overview of Cytochrome P450 Function Requires Reliable MD Calculations 163

6.5 Conclusions 164

References 165

7 Computational Free Energy Methods for Ascertaining Ligand Interaction with Metabolizing Enzymes 179
Mark J. Williamson

7.1 Introduction 179

7.2 Linking Experiment and Simulation: Statistical Mechanics 180

7.2.1 A Note on Chemical Transformations 182

7.3 Taxonomy of Free Energy Methods 183

7.3.1 Pathway Methods 183

7.3.1.1 Pathway Planning: Using the State Nature of the Free Energy Cycle 184

7.3.1.2 Free Energy Perturbation 185

7.3.1.3 Bennett Acceptance Ratio 185

7.3.1.4 Thermodynamic Integration 186

7.3.2 Endpoint Methods 186

7.3.2.1 Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) 186

7.3.2.2 Linear Interaction Energy 187

7.3.2.3 QM Endpoint Methods 187

7.3.3 Summary of Free Energy Methods 187

7.4 Ligand Parameterization 188

7.5 Specific Examples 189

7.5.1 Cytochrome P450 (CYP) 189

7.5.2 Chorismate Mutase 192

7.6 Conclusions 192

References 193

8 Experimental Approaches to Analysis of Reactions of Cytochrome P450 Enzymes 199
Frederick Peter Guengerich

8.1 Introduction 199

8.2 Structural Data and Substrate Binding 199

8.3 Systems for Production of Reaction Products and Analysis of Systems 200

8.3.1 In Vivo Systems 201

8.3.2 Tissue Microsomal Systems 201

8.3.3 Purified CYPs in Reconstituted Systems 201

8.3.4 Membranes from Heterologous Expression Systems 202

8.3.4.1 Mammalian Cells 202

8.3.4.2 Insect Cell Systems (Using Baculovirus Infection for Expression) 202

8.3.4.3 Microbial Membrane Systems 202

8.4 Methods for Analysis of Products of Drugs 203

8.4.1 Separation Methods 203

8.4.1.1 High-Performance Liquid Chromatography 203

8.4.1.2 Other Separation Methods 204

8.4.2 Analysis Methods 204

8.4.2.1 HPLC–UV 204

8.4.2.2 LC–MS 205

8.4.2.3 LC–MS/MS 205

8.4.2.4 LC–HRMS 205

8.4.2.5 NMR 205

8.4.2.6 Other Spectroscopy of Metabolites 206

8.5 Untargeted Searches for CYP Reactions 208

8.6 Complex CYP Products 208

8.7 Structure–Activity Relationships Based on Products 210

8.7.1 SARs Based on Chemical Bond Energy 211

8.7.2 SARs Based on Docking 211

8.7.3 Knowledge-Based SAR 212

8.8 SAR of Reaction Rates 213

8.9 Other Issues in Predictions 213

8.10 Conclusions 214

References 214

Part Four Computational Approaches to Study Sites and Products of Metabolism 221

9 Molecular Interaction Fields for Predicting the Sites and Products of Metabolism 223
Fabio Broccatelli and Nathan Brown

9.1 Introduction 223

9.2 CYP from a GRID Perspective 224

9.3 From Lead Optimization to Preclinical Phases: the Challenge of SoM Prediction 226

9.3.1 MetaSite: Accessibility Function 227

9.3.2 MetaSite: Reactivity Function 229

9.3.3 MetaSite: Site of Metabolism Prediction 230

9.3.4 MetaSite: Validation and Case Studies 231

9.3.5 MetaSite: Prediction of CYP Inhibition 234

9.3.6 MassMetaSite: Automated Metabolite Identification 236

9.4 Conclusions 239

References 241

10 Structure-Based Methods for Predicting the Sites and Products of Metabolism 243
Chris Oostenbrink

10.1 Introduction 243

10.2 6 Å Rule 243

10.3 Methodological Approaches 245

10.4 Prediction of Binding Poses 247

10.5 Protein Flexibility 249

10.6 Role of Water Molecules 254

10.7 Effect of Mutations 256

10.8 Conclusions 258

References 259

11 Reactivity-Based Approaches and Machine Learning Methods for Predicting the Sites of Cytochrome P450-Mediated Metabolism 265
Patrik Rydberg

11.1 Introduction 265

11.2 Reactivity Models for CYP Reactions 268

11.2.1 Hydroxylation of Aliphatic Carbon Atoms 268

11.2.2 Hydroxylation and Epoxidation of Aromatic and Double Bonded Carbon Atoms 271

11.2.3 Combined Carbon Atom Models 273

11.2.4 Comprehensive Models 273

11.3 Reactivity-Based Methods Applied to CYP-Mediated Site of Metabolism Prediction 274

11.3.1 Methods Only Applicable to Carbon Atoms 274

11.3.2 Comprehensive Methods 276

11.4 Machine Learning Methods Applied to CYP-Mediated Site of Metabolism Prediction 278

11.4.1 Atomic Descriptors 278

11.4.2 Machine Learning Methods and Optimization Criteria 279

11.5 Applications to SoM Prediction 280

11.5.1 Isoform-Specific Models 281

11.5.2 Isoform-Unspecific Models 283

11.6 Combinations of Structure-Based Models and Reactivity 284

11.7 Conclusions 285

References 286

12 Knowledge-Based Approaches for Predicting the Sites and Products of Metabolism 293
Philip Neville Judson

12.1 Introduction 293

12.2 Building and Maintaining a Knowledge Base 295

12.3 Encoding Rules in a Knowledge Base 299

12.4 Ways of Working with Rules 301

12.5 Using the Logic of Argumentation 303

12.6 Combining Absolute and Relative Reasoning 307

12.7 Combining Predictions from Multiple Sources 310

12.8 Validation and Assessment of Performance 312

12.9 Conclusions 314

References 314

Part Five Computational Approaches to Study Enzyme Inhibition and Induction 319

13 Quantitative Structure–Activity Relationship (QSAR) Methods for the Prediction of Substrates, Inhibitors, and Inducers of Metabolic Enzymes 321
Oraphan Phuangsawai, Supa Hannongbua, and Mathew Paul Gleeson

13.1 Introduction 321

13.2 In Silico QSAR Methods 322

13.2.1 Experimental Variability 323

13.2.2 Data Curation and Manipulation 324

13.2.3 Molecular Descriptors 324

13.2.4 Training SAR, QSAR, and Machine Learning Models 325

13.2.5 Local versus Global QSAR Models 325

13.2.6 SAR and Classical QSAR Methods 326

13.2.7 Machine Learning QSAR Methods 327

13.2.8 Model Assessment and Validation 327

13.2.8.1 Assessing the Predictive Ability of QSAR Models 327

13.2.8.2 Applicability Domains of QSAR Models 328

13.3 QSAR Models for Cytochrome P450 328

13.3.1 Inhibition QSAR 328

13.3.1.1 SAR 328

13.3.1.2 Classical QSAR Models 329

13.3.1.3 Machine Learning QSAR Models 333

13.3.1.4 Classification Models 334

13.3.1.5 3D QSAR Models 335

13.3.2 Enzyme Induction QSAR 336

13.4 Conjugative Metabolizing Enzymes 337

13.4.1 Uridine Diphosphate Glucosyltransferase (UGT) QSAR 338

13.4.2 Sulfotransferases QSAR 338

13.5 In Vitro Clearance QSAR 339

13.6 Conclusions 340

References 341

14 Pharmacophore-Based Methods for Predicting the Inhibition and Induction of Metabolic Enzymes 351
Teresa Kaserer, Veronika Temml, and Daniela Schuster

14.1 Introduction 351

14.2 Substrate and Inhibitor Pharmacophore Models 354

14.2.1 Cytochrome P450 enzymes 354

14.2.1.1 CYP1A2 354

14.2.1.2 CYP2B6 355

14.2.1.3 CYP2C9 356

14.2.1.4 CYP2C19 357

14.2.1.5 CYP2D6 358

14.2.1.6 CYP3A4 359

14.2.1.7 CYP3A5 and CYP3A7 360

14.2.2 UDP-Glucuronosyltransferases (UGTs) 361

14.2.2.1 UGT1A1 361

14.2.2.2 UGT1A4 361

14.2.2.3 UGT1A9 361

14.2.2.4 UGT2B7 362

14.2.3 Interference with Recently Identified Phase I Metabolic Enzymes 362

14.3 Inducer Models 363

14.3.1 Hetero- and Autoactivation 363

14.3.1.1 CYP2C9 363

14.3.1.2 CYP3A4 364

14.3.2 Nuclear Receptors 364

14.3.2.1 Pregnane X Receptor 364

14.3.2.2 CAR 366

14.4 Conclusions 366

References 368

15 Prediction of Phosphoglycoprotein (P-gp)-Mediated Disposition in Early Drug Discovery 373
Simon Thomas and Richard J. Dimelow

15.1 Introduction 373

15.2 QSAR Modeling of Compounds Interacting with Transporters 376

15.2.1 Experimental Data and Assays 376

15.2.2 Descriptors Used in P-gp Substrate Identification 378

15.2.3 QSAR Methods Used in P-gp Substrate Identification 380

15.3 Influence of Compound Structure on P-gp Substrate Identity 380

15.4 QSAR Models for P-gp Substrates 385

15.5 Application to Drug Discovery 388

15.6 Conclusions 391

References 392

16 Predicting Toxic Effects of Metabolites 397
Andreas Bender

16.1 Introduction 397

16.2 Methods for Predicting Toxic Effects 401

16.2.1 Predicting Metabolites 401

16.2.2 Predicting Relative and Absolute Metabolism Likelihoods and Rates 401

16.2.3 Utilizing Pharmacogenetic Data to Anticipate Dose, Rate, and Time Information in an Individual Patient 402

16.2.4 Predicting the Effect of the Resulting Metabolites 402

16.2.4.1 Bioactivity-Based Mechanistic Models 403

16.2.4.2 Incorporating Pathway Information into Toxicity Models 404

16.2.4.3 Toxicogenetic and Pharmacogenomic Approaches 406

16.2.4.4 Knowledge-Based Systems 407

16.2.4.5 Reactive Metabolites 407

16.2.5 Current Scientific and Political Developments Regarding Metabolism and Toxicity Prediction 408

16.3 Conclusions 408

References 409

Part Six Experimental Approaches to Study Metabolism 413

17 In Vitro Models for Metabolism: Applicability for Research on Food Bioactives 415
Natalie D. Glube and Guus Duchateau

17.1 Introduction 415

17.1.1 Bioavailability 416

17.1.2 Intestinal Absorption 416

17.1.3 First-Pass Metabolism 418

17.2 Classification of In Vitro Models for Metabolism 418

17.3 Modifications via Gut (Colon) Microflora 419

17.3.1 Background Information 419

17.3.2 In Vitro Models 420

17.3.2.1 Fecal Slurry 421

17.3.2.2 Isolated Pure Bacterial Cultures 421

17.3.2.3 Complex Intestinal Models (TIM-2) 421

17.4 Intestinal (Gut Wall) Metabolism 421

17.4.1 Background Information 421

17.4.2 In Vitro Models 422

17.4.2.1 Tissue Intact Models 423

17.4.2.2 Subcellular and Cellular Models 423

17.5 Hepatic Metabolism 423

17.5.1 Background Information 423

17.5.2 In Vitro Models 424

17.5.2.1 Supersomes: Recombinant Phase I and Phase II Enzymes 424

17.5.2.2 Microsomes 424

17.5.2.3 S9 Fractions 426

17.5.2.4 Hepatocyte Cell Lines 426

17.5.2.5 Primary Cultures: Cryopreserved Hepatocytes 427

17.5.2.6 Cryopreserved Hepatocytes versus Microsomes 428

17.5.2.7 Hepatocytes in Culture 429

17.6 Pharmacokinetic Data Obtainable from In Vitro Metabolism Models 431

17.6.1 Pharmacokinetic Analysis 431

17.6.1.1 Measurement Methodology: Substrate Depletion versus Metabolite Formation 432

17.6.1.2 Mathematical Models for Metabolism: Well-Stirred, Parallel Tube, and Dispersion Models 432

17.7 Assay Validation 433

17.7.1 Selection and Preparation of Reference Compounds 433

17.7.2 Analytics 434

17.7.3 Theoretical Steps to Establish an In Vitro Model 434

17.8 Conclusions 435

17.8.1 What Can We Summarize from the Literature? 435

17.8.2 What Questions We Wish to Have Answered Will Determine Which Model We Select 436

References 438

18 In Vitro Approaches to Study Drug–Drug Interactions 441
Stephen S. Ferguson and Jessica A. Bonzo

18.1 Introduction 441

18.1.1 Additional Factors Influencing Drug Metabolism 442

18.2 Inhibition of Drug Metabolism 444

18.2.1 In Vitro Models for Predicting Inhibition of Drug Metabolism 444

18.2.1.1 Human Liver Microsomes 445

18.2.1.2 S9 and Cytosol 456

18.2.1.3 Recombinant Enzymes 457

18.2.1.4 Primary Hepatocytes 458

18.3 Transcriptional Regulation of Metabolism 460

18.3.1 Gene Induction Pathways 460

18.3.2 Gene Repression/Suppression 462

18.3.3 In Vitro Models for Predicting Induction of Drug Metabolism Enzymes 463

18.3.3.1 Ligand Binding Assays 463

18.3.3.2 Gene Reporter Assays 465

18.3.3.3 Cellular Models for Induction Studies 466

18.3.3.4 Induction Assays in Cellular Models 468

18.3.3.5 Treatment with Control and Test Compounds 470

18.3.3.6 Gene Expression in Cellular Models for Induction 471

18.3.3.7 Enzymatic Activity in Metabolically Competent Cellular Models of Induction 474

18.4 Next-Generation Models and Concluding Remarks 474

References 477

19 Metabolite Detection and Profiling 485
Ian D. Wilson

19.1 Introduction 485

19.2 Chromatography 486

19.3 Mass Spectrometry 487

19.4 Sample Preparation for LC–MS-Based Metabolite Profiling 490

19.5 Metabolic Profiling by LC–MS 491

19.5.1 Metabolic Stability and Cytochrome P450 Inhibition Assays 491

19.5.2 Metabolite Profiling, Detection, and Identification from In Vivo and In Vitro Studies 492

19.5.3 Reactive Metabolite Detection 496

19.6 Conclusions 496

References 497

Index 499

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