Drug Design Strategies: Quantitative Approaches

Drug Design Strategies: Quantitative Approaches......Page 4
Preface......Page 6
Contents......Page 8
1.1 Introduction......Page 20
1.2.1 A Simple Model......Page 21
1.2.2 Drug-Related Data......Page 22
1.2.3 Target-Related Data......Page 23
1.2.5 Drug Design Evolution......Page 24
1.3.1 QSAR-Driven Drug design......Page 25
1.3.2 Drug-Related Data......Page 26
1.3.4 Drug–Target Complex-Related Data......Page 27
1.4.1 The Emergence of Structure-Based Drug Design......Page 28
1.4.2 Drug-Related Data......Page 30
1.4.3 Target-Related Data......Page 32
1.4.4 Drug–Target Complex-Related Data......Page 33
1.5.1 Parallel Medicinal Chemistry......Page 34
1.5.2 Drug-Related Data......Page 35
1.5.3 Target-Related Data......Page 38
1.5.4 Drug–Target Complex-Related Data......Page 39
1.6.1 The Omics Era......Page 40
1.6.2 Drug-Related Data......Page 41
1.6.3 Target-Related Data......Page 42
1.6.4 Drug–Target Complex-Related Data......Page 44
1.7 Drug Design in the Future......Page 45
References......Page 47
2.1 Introduction......Page 54
2.2 Drug-likeness......Page 55
2.2.1 The Impact of the Rule of Five......Page 56
2.2.2 Oral and Non-Oral Drug Properties......Page 57
2.2.3 ADME Profiles and Physical Properties......Page 61
2.2.4 Toxicity Risks and Physical Properties......Page 63
2.3 Lead-likeness......Page 66
2.4 Ligand Efficiency Metrics......Page 69
References......Page 72
3.1.1 Overview of QSAR......Page 79
3.1.2 The Invention of QSAR......Page 80
3.2.1.1 Electronic Effects of Aromatic Substituents......Page 81
3.2.1.2 Electronic Effects of Aliphatic Substituents......Page 84
3.2.2.1 Taft Es Values......Page 85
3.2.2.2 Steric Constants Calculated from the 3D Structures of the Substituents......Page 86
3.2.3.1 What is Hydrophobicity?......Page 87
3.2.3.2 LogP versus LogD......Page 88
3.2.3.3 Octanol as a Reference Solvent......Page 89
3.2.3.6 Approaches to Calculating LogP......Page 90
3.2.5 Other Molecular Descriptors......Page 92
3.2.5.1 Substructure Descriptors......Page 93
3.3 Properties Calculated from the 3D Conformation of the Molecule......Page 94
3.3.2 3D Property Calculation......Page 95
3.4 The Relationship between Calculated 2D and 3D Molecular Properties......Page 96
3.5.1.2 Classification Methods......Page 97
3.5.1.4 Partial Least Squares......Page 99
3.5.3 Methods to Assess the Predictivity of a QSAR......Page 100
3.6 The Contrast Between QSAR and Traditional SAR......Page 101
References......Page 102
4.1 The Use and Limitations of Physical Models......Page 107
4.1.1.1 The Parameter Paradox......Page 108
4.1.3 Molecular Representations......Page 109
4.1.4.1 Field-Based Methods......Page 110
4.1.5 Conformational Analysis and Global Energy Minima Prediction......Page 111
4.1.6.1 Free-Energy Perturbation......Page 112
4.2.1 Virtual Screening and Rational Design......Page 113
4.2.3 Modelling of Water......Page 114
4.4.1 Control Experiments......Page 115
4.4.3 The Hinge Binding Hypothesis for Kinase Inhibitors......Page 116
4.4.4 Pharmacophores......Page 118
4.4.5.1 Neuraminidase Inhibitors – the Story of Relenza (Zanamivir)......Page 119
4.4.5.2 The Discovery of Inhibitors of the Angiotensin 1 Receptor......Page 120
4.5.1 Sociology of Data Presentation and Interpretation......Page 122
References......Page 123
5.1 Introduction......Page 127
5.2.1 Opportunities and Limitations for X-Ray Crystallography......Page 128
5.2.2 Opportunities and Limitations for Nuclear Magnetic Resonance......Page 130
5.2.4 Energetics of Molecular Interactions in Protein–Ligand Complexes......Page 131
5.3 Examples of Structure-Based Drug Design in the Discovery of Marketed Drugs and Advanced Clinical Candidates......Page 135
5.3.2 Carbonic Anhydrase Inhibitors Dorzolamide and Brinzolamide as Drugs for the Treatment of Glaucoma......Page 138
5.3.3 HIV Protease Inhibitors Saquinavir, Ritonavir, Indinavir, Nelfinavir, Amprenavir, Lopinavir, Atazanavir, Fosamprenavir, Tipranavir and Darunavir as Drugs against HIV Infection......Page 140
5.3.4 Non-Peptidic HIV Protease Inhibitor, Tipranavir as a Drug for the Treatment of HIV......Page 141
5.3.5 Nucleoside Analogue HIV Reverse Transcriptase Inhibitors as Drugs for the Treatment of HIV and Hepatitis B: Zidovudine, Didanosine, Stavudine, Lamivudine, Abacavir, Emtricitabine and Entecavir......Page 142
5.3.6 Non-Nucleoside Reverse Transcriptase Inhibitor as Drugs for the Treatment of HIV: Efavirenz, Nevirapine, Delavirdine, Apricitabine and Etravirine......Page 144
5.3.7 Thrombin Inhibitors Melagatran/ Ximelagatran and Dabigatran as Drugs for the Treatment of Thromboembolytic Disease......Page 146
5.3.8 Inhibitors of Factor X as Drugs for the Treatment of Thromboembolytic Disease: Rivaroxaban and Apixaban......Page 148
5.3.10 Abl Kinase Inhibitors Imatinib, Nilotinib and Dasatinib as Drugs for the Treatment of Chronic Myeloid Leukemia......Page 151
5.3.11 Influenza Neuraminidase Inhibitors Zanamivir and Oseltamivir as Drugs for the Treatment of Influenza......Page 158
5.4 Has Structure-Based Drug Design Increased the Speed and Productvity of Drug Discovery? A Medicinal Chemist’s Perspective......Page 159
5.5 Outlook......Page 168
References......Page 169
6.1 Introduction......Page 183
6.2.1 Topological Graphs......Page 184
6.2.4 Line Notations......Page 185
6.2.5 Connection Tables......Page 188
6.2.5.1 Connection Table File Formats......Page 189
6.2.6 IUPAC International Chemical Identifier (InChI)......Page 190
6.2.7 Stereochemistry......Page 191
6.2.8 Three-Dimensional Structure Representation......Page 192
6.2.9 Fragment Codes and Structure Fingerprints......Page 193
6.3 Canonicalisation and Normalisation......Page 194
6.3.2 Normalisation and Business Rules......Page 195
6.4 Physiologically Relevant Forms......Page 198
6.5 Registration Systems......Page 199
6.5.2 Stereochemistry......Page 200
6.5.4.1 Stereochemical Uncertainty......Page 202
6.5.5 Unknown Structures......Page 203
6.6.3 Reduced Representations......Page 204
6.6.4 Matched Molecular Pair Analysis......Page 205
6.7 Conclusions and Future Directions......Page 206
References......Page 207
7.1 Introduction......Page 211
7.3 Steric Constants......Page 213
7.4 Hydrophobicity and Biological Activity......Page 214
7.5.3 High Throughput Screening (HTS) Method......Page 215
7.6.2 Reversed Phase High-Performance Liquid Chromatography......Page 216
7.6.3 Artificial Membrane Chromatographic Method......Page 217
7.7 Hydrophobicity and its Calculation......Page 218
7.7.1 CLOGP-Rules of Engagement......Page 219
7.8.1 Hydrophobicity and Central Nervous System Toxicity......Page 225
7.8.2 Hydrophobicity and Absorption......Page 226
7.8.3 Hydrophobicity and Cytochrome P450 Inhibition......Page 227
7.8.4 Hydrophobicity and Toxicity: Mutagenicity and Carcinogenicity......Page 228
7.8.5 Hydrophobicity and Toxicity: hERG Blockage......Page 230
7.8.6 Hydrophobicity and Bioaccumulation......Page 231
7.9 Conclusion......Page 233
References......Page 234
8.1 Introduction......Page 239
8.2.1 Electronic Properties......Page 240
8.2.3 Steric Properties......Page 242
8.3 Topological Descriptors......Page 244
8.4 Experimental......Page 246
8.5 Parameters from Molecular Modelling/Computational Chemistry......Page 247
8.6 1D, 2D and 3D Descriptors and Conformation......Page 249
8.7 Molecular Fields......Page 252
8.8 Fingerprints......Page 254
8.9 Which are the Best Properties to Use?......Page 255
8.10 Conclusions......Page 256
References......Page 257
9.1.1 Physics-Based and Empirical Models......Page 261
9.2.1 Pearsons Product Moment Correlation coefficient (r)......Page 263
9.2.2 The Problem of Using r/r² to Compare Models......Page 264
9.3.1 Parametric Statistics in Linear and Multiple Regression......Page 266
9.3.2 q² and Q² Statistic......Page 267
9.3.3 y-Randomisation......Page 268
9.4.1 Calculation of Q² Using an External Test Set......Page 269
9.4.2 Use of a Temporal Test Set......Page 271
9.5.1 Cooper Statistics......Page 273
9.5.2 Prevalence in Classified Datasets......Page 274
9.5.4 Receiver Operating Characteristic Curves......Page 276
9.6 Domain of Applicability......Page 277
9.7 Statistical vs. Practical Significance in Prediction......Page 281
9.8 Future Outlook......Page 283
References......Page 284
10.1 Introduction......Page 286
10.2.1 Overview......Page 287
10.2.2 Knowledge Representations......Page 288
10.2.3 Data Mining Survey......Page 290
10.2.4 Meta-Model Application......Page 293
10.3 Conclusions......Page 295
References......Page 296
11.1 Introduction......Page 298
11.2 A Description of Derek for Windows......Page 299
11.2.1 Rapid Prototype Alerts......Page 301
11.2.2 Applicability Domains......Page 303
11.3 A Description of Meteor......Page 304
11.3.1 Integration of Meteor with Other Programs......Page 306
11.4.1 Storage of Toxicological Knowledge......Page 309
11.4.3 Lead Optimisation......Page 310
11.4.5 Occupational Health Assessment......Page 311
11.4.8 The Use of Expert Systems to Assess Genotoxic Impurities......Page 312
11.5 The Expert System for Metabolism Prediction in Drug Design and Discovery......Page 315
11.5.1 Meteor and the ‘Metabolites in Safety Testing’ Guidelines......Page 316
11.5.2 The Use of Meteor to Accelerate Metabolite Identification......Page 319
11.5.2.1 Case Study 1: Indinavir......Page 320
11.5.2.2 Case Study 2: Quetiapine......Page 322
11.5.2.3 Application in Drug Discovery......Page 324
11.6 Conclusion and Future Perspectives......Page 326
References......Page 328
12.1 Introduction......Page 331
12.2.1 hERG Pharmacophores......Page 332
12.2.2 hERG Classification Methods......Page 336
12.3 QSAR for Drug Metabolizing Enzymes......Page 342
12.3.2 Metabolic Stability Models......Page 343
12.3.3 P450 3A4 Metabolic Intermediate Complex Formation and Time Dependent Inhibition......Page 347
12.4 Pregnane X Receptor Models......Page 348
12.5 Drug Induced Liver Injury Models......Page 350
12.6 From Ligands to Pharmacophores to Networks to Understand Toxicity......Page 352
12.7 Discussion......Page 353
References......Page 354
13.1 Introduction......Page 364
13.2 Predicting Human Absorption......Page 367
13.2.1 The Biopharmaceutics Classification System......Page 368
13.2.3 The Biopharmaceutics Drug Disposition Classification System......Page 369
13.3 Predicting Human Drug Distribution......Page 370
13.4.1 The Well-Stirred Model......Page 372
13.4.2 Physiologically Based Pharmacokinetic Models......Page 374
13.4.3 Allometry......Page 375
13.6 Predicting Human Dose and Dose Frequency......Page 376
13.7 Predicting Drug–Drug Interactions......Page 379
13.8 Physico-Chemical Control......Page 381
13.9 Conclusions and Future Outlook......Page 382
References......Page 383
14.1 Introduction......Page 386
14.2 Similarity Methods......Page 388
14.3 Diversity Methods......Page 389
14.4 Protein Family Approaches......Page 395
14.5 Beyond Simple Diversity......Page 397
14.6 Combinatorial Design......Page 400
14.7 Compound Collection Enhancement Workflows......Page 403
14.8 Screening Library Design......Page 406
14.9 Other Considerations......Page 409
14.10.3 Focussed Screening......Page 410
References......Page 411
15.1 Introduction......Page 416
15.1.1 Genomics and its Inheritors......Page 418
15.1.2 An Explosion of -OMEs......Page 419
15.2 Drug Discovery Technology and Genomics......Page 420
15.2.1 Target Discovery in the Genomic Era......Page 421
15.2.2 Transcriptomics and Microarrays......Page 424
15.2.3 Proteomics and Mass Spectrometry......Page 428
15.2.4 Bioinformatics......Page 431
15.2.5 Structural Genomics and Fragment-Based Drug Discovery......Page 434
15.2.6 Computational Bioscience and Systems Biology......Page 436
15.3.1 Drugging Protein–Protein Interactions......Page 440
15.3.2 Systems Biology and Polypharmacology......Page 443
15.3.3 Repurposing......Page 446
15.3.4 Biologics and Vaccines......Page 447
15.3.5 Pharmacogenomics and Personal Medicines......Page 448
15.4 Conclusion......Page 451
Acknowledgements......Page 452
References......Page 453
16.1 Introduction......Page 459
16.2.1 Binding Mode Prediction......Page 461
16.2.2 Virtual Screening......Page 462
16.3.2 Empirical Methods......Page 465
16.3.3 Physics-Based Methods......Page 467
16.3.4 Another Matter......Page 470
16.4 Conclusion......Page 473
References......Page 474
17.1 Lack of Physico-Chemical and Toxicity Data for Chemicals in the Environment......Page 477
17.2 The Need for the New Legislation of Chemicals: REACH......Page 478
17.3.1 A Defined Endpoint......Page 480
17.3.2 An Unambiguous Algorithm......Page 482
17.3.3 A Defined Domain of Applicability......Page 485
17.3.4 Appropriate Measures of Goodness-of-Fit, Robustness and Predictivity......Page 486
17.3.5 A Mechanistic Interpretation, if Possible......Page 489
17.4 Models for Cumulative End-Points (POPs, PBTs, etc.): Utility for the Screening, Prioritization and for the Design of Safer Alternative Chemicals......Page 491
References......Page 494
Index......Page 498