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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
Contents xi
8.4 Application of Pharmacophore Modeling 176
8.4.1 Applications of Pharmacophore-Based Virtual Screening 177
8.4.1.1 Drug Discovery 177
8.4.2 Applications in Drug Target Fishing 178
8.4.3 Applications in Ligand Profiling 179
8.4.4 Applications in Docking 179
8.4.5 Applications in ADMET 180
8.4.6 Modulation of the Immune System 180
8.5 Emerging Trends in Pharmacophore Model Development 180
8.5.1 Involvement of Machine Learning 180
8.5.2 Prediction of Pharmacokinetic Properties 181
8.5.3 Structural Biology and Protein Functionality Studies 181
8.5.4 Integration with MDs Simulations 181
8.6 Case Studies 183
8.6.1 Case 1 183
8.6.2 Case 2 183
8.7 Challenges in Pharmacophore Modeling 186
8.8 Conclusion 187
Acknowledgments 188
References 188
9 Scaffold Hopping and De Novo Drug Design 195
Shrimanti Chakraborty, Soumi Chakraborty, Biprajit Sarkar, Rahul Ghosh,
Sharanya Roy, Nisha Kumari Singh, and Gourav Rakshit
9.1 Introduction 195
9.2 Scaffold Hopping 196
9.2.1 Classification of Scaffold Hopping 197
9.2.1.1 1° Hop: Heterocycle Replacement 197
9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures 198
9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics 198
9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping 198
9.2.2 Advantages of Scaffold Hopping 199
9.2.3 Disadvantages of Scaffold Hopping 199
9.2.4 Reasons for Scaffold Hopping 200
9.2.5 Properties and Key Methods of Scaffold Hopping 200
9.3 De Novo Drug Design 201
9.3.1 Classification of De Novo Drug Design 202
9.3.1.1 Structure-based Drug Design 202
9.3.1.2 Ligand-based Drug Design 203
9.3.1.3 De Novo Design Strategies 203
9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design 203
9.3.1.5 Hybrid Approaches 203
9.3.2 Basic Principle of De Novo Drug Design 203
9.3.3 Application of De Novo Drug Design 204
9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design 205
9.3.5 Methodological Approaches in De Novo Drug Design 206
9.3.5.1 Structure-based De Novo Drug Design 207
Contentsxii
9.3.5.2 Ligand-based De Novo Drug Design 207
9.3.5.3 Generation of Drug-Like Molecular Fragments 208
9.3.5.4 Similarity Searching 208
9.3.5.5 Selection of Target Reference Structure 208
9.3.5.6 Similarity Analysis of De Novo-generated Compounds 209
9.3.5.7 Evaluation of Scaffold Diversity 210
9.4 Results and Discussion 211
9.4.1 Generation of Drug-Like Molecular Fragments 211
9.4.2 De Novo Design with a Single Reference Structure 211
9.4.3 De Novo Design with a Focused Set of Five Similar Templates 212
9.4.4 De Novo Design with a Diverse Set of Five Templates 212
9.5 Software Tools for SH (Scaffold Hopping) and De Novo Design Selection 214
9.6 Case Study 214
9.6.1 De Novo Drug Design 214
9.6.2 Scaffold Hopping 215
9.7 Conclusion 215
References 216
10 Fragment-based Drug Design and Drug Discovery 221
André M. Oliveira and Mithun Rudrapal
10.1 Introduction 221
10.2 The Process of Finding Fragments 222
10.3 FBDD Strategies 227
10.4 Case Studies 228
10.5 Conclusion and Future Perspectives 230
References 232
11 AI/ML Approaches in Drug Design 237
Kevser Kübra Kırboğa
11.1 Introduction 237
11.2 Traditional Drug Design Methods 237
11.2.1 The Rise of Computational Methods 238
11.2.2 The Importance of AI/ML in Modern Drug Design 239
11.3 AI/ML Landscape in Drug Design 239
11.3.1 AI/ML Algorithms and Methods 239
11.3.1.1 Machine Learning Models 239
11.3.1.2 Neural Networks 240
11.3.2 Applications in Drug Design 241
11.3.2.1 Peptide Synthesis 241
11.3.2.2 Molecular Design 242
11.3.2.3 Virtual Screening (VS) 242
11.3.2.4 Quantitative Structure–Activity Relationship Models 243
11.3.2.5 Drug Repurposing 243
11.3.3 Challenges and Failures 244
11.4 Ethics, Reliability, and Regulatory Issues 244
11.5 Future Directions 246
11.6 Conclusion 247
References 247
Contents xiii
12 Network-based Methods in Drug Discovery 255
Ghanshyam Parmar, Ashish Shah, Jay Mukesh Chudasama, Priya Kashav, and Vanesa James
12.1 Introduction 255
12.1.1 Background of Drug Discovery Future Challenges 255
12.1.2 Single Target Approach Limitations 257
12.1.3 Emergence of Network Biology and Polypharmacology 259
12.2 Network Pharmacology: Practical Guide 260
12.2.1 Common Network Pharmacology Databases 260
12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools 261
12.2.1.2 Exploring IMPPAT Network Pharmacology Databases 261
12.2.1.3 Target Genes of Phytoconstituents 266
12.2.2 Network Analysis and Visualization 267
12.2.3 Applications of Network Pharmacology in Drug Discovery 269
12.3 Ayurveda and Traditional Indian Medicine 269
12.3.1 Overview of Ayurveda and Its Complex Formulations 269
12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines 270
12.3.3 Indian Traditional Medicines Recently Breakthroughs in Using Different Approaches
to Treat a Variety of Diseases 271
12.4 Network Pharmacology in Herbal Remedies 273
12.4.1 Application of Network Pharmacology in Herbal Drug Discovery 273
12.4.1.1 Cancer 273
12.4.1.2 Cardiovascular Diseases (CVDs) 274
12.4.1.3 Diabetes Mellitus (DM) 275
12.4.2 Screening Pharmacological Efficacy of Herbal Remedies 275
12.4.3 Utilizing Network Pharmacology to Understand Complex Diseases 276
12.5 Conclusion and Future Prospects 277
References 278
13 Rational Design of Natural Products for Drug Discovery 285
Ankita Kashyap, Anupam Sarma, Bhrigu Kumar Das, and Ashis Kumar Goswami
13.1 Introduction 285
13.2 Natural Products for the Development of New Drugs 286
13.3 Criteria for Selecting Natural Products for Drug Design 288
13.4 Importance of Biodiversity in Sourcing Natural Products 288
13.5 Structural Elucidation of Natural Products 289
13.6 In Silico Computational Tools for Rational Drug Discovery
from Natural Sources 290
13.6.1 Molecular Docking and Virtual Screening Techniques for Predicting Ligand–Receptor
Interactions 290
13.6.2 QSAR (Quantitative Structure–Activity Relationship) Models for Optimizing Biological
Properties 293
13.6.3 High-Throughput Screening Methods for Efficient Compound Selection 294
13.6.4 Molecular Dynamics Simulations for Predicting Solubility and Stability 294
13.6.5 ADMET Attributes Predicted In Silico 295
13.6.6 Computational Tools for Predicting Pharmacokinetics and Pharmacodynamics
(PK/PD) 295
13.6.7 In Silico Prediction of Biosynthetic Pathways and Identification of Potential Bioactive
Compounds 297
Contentsxiv
13.7 Formulation Challenges with Natural Products 298
13.8 Quality by Design (QbD) Approaches 300
13.8.1 Use of Computational Models for Formulation Optimization 301
13.9 Conclusion 303
References 304
14 Design of Enzyme Inhibitors in Drug Discovery 311
Koyel Kar
14.1 Introduction 311
14.2 Importance of Enzyme Inhibition as a Strategy for Modulating Enzyme Activity 312
14.3 Classification of Enzyme Inhibitors 312
14.3.1 Reversible Inhibitors 312
14.3.2 Irreversible Inhibitors 313
14.3.3 Competitive Inhibitors 313
14.3.4 Noncompetitive Inhibitors 313
14.3.5 Allosteric Modulators 314
14.4 Strategies Employed in the Design and Development of Enzyme Inhibitors 314
14.4.1 Structure-Based Design 314
14.4.2 Computer-Aided Design 315
14.4.3 Fragment-Based Design 316
14.4.4 Virtual Screening Method 316
14.4.4.1 Ligand Based 316
14.4.4.2 Receptor Based 317
14.4.5 Natural Product-Based Discovery 318
14.4.6 Using Iterative Protein Crystallographic Analysis 318
14.4.7 Utilization of Covalent Inhibitors 318
14.4.8 Encapsulation Techniques 319
14.4.9 Based on Active-Site Specificity 319
14.4.10 Machine Learning Inhibitor Design 320
14.4.11 Enzyme-Templated Dynamic Combinatorial Chemistry 320
14.5 Limitations and Challenges 321
14.6 Future Directions 321
14.7 Conclusion 322
References 322
15 Rational Design of Peptides and Protein Molecules in Drug Discovery 327
Ipsa Padhy, Abanish Biswas, Chandan Nayak, and Tripti Sharma
15.1 Introduction 327
15.2 Peptides as Therapeutics 328
15.2.1 Peptide Antibiotics 328
15.2.1.1 Peptides in Bone Diseases 329
15.2.1.2 Peptides in Cancer 329
15.2.1.3 Peptides in Metabolic Diseases 331
15.2.1.4 Peptides in Gastrointestinal Diseases 331
15.2.2 Advantages and Limitations of Peptide Therapeutics 332
15.2.3 FDA-Approved Peptide Therapeutics 332
Contents xv
15.2.4 Peptide-Based Entities in Clinical Trials 332
15.2.5 Peptide Synthesis and Diversification 332
15.2.5.1 Chemical Synthesis of Peptides 338
15.2.5.2 Chemical Modification of Peptide and Peptidomimetics 340
15.2.5.3 Backbone Modification of Peptides 340
15.2.5.4 Side-Chain Modification of Peptides 340
15.2.5.5 Peptide Cyclization 340
15.2.5.6 Peptide Mimicking of α-Helices and Stabilization 342
15.2.5.7 Peptide Mimicking of β-Strands and β-Sheets 342
15.2.5.8 Peptide Production by Recombinant Technology 342
15.2.5.9 Peptides Modification by Genetic Code Expansion 343
15.2.5.10 PEGylation of Peptides and Proteins 344
15.3 New Technologies for Peptide-Based Drug Discovery 344
15.3.1 Phage Display 344
15.3.2 mRNA Display 345
15.3.3 DNA-Encoded Libraries 346
15.3.4 Cell-Penetrating Peptides 346
15.3.5 Macrocyclic Peptides 346
15.4 Computational Approaches in Peptide Drug Discovery 347
15.5 Conclusion 350
References 351
16 Rational Design of Drugs for Neurodegenerative Disorders 363
Priyanka Kamaria
16.1 Introduction 363
16.2 Common Mechanism of Neurodegeneration 364
16.3 Brief Overview of Computational Methods in Drug Design 365
16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder 367
16.4.1 Epidemiology of Parkinson’s Disease 368
16.4.1.1 Factors Influencing PD Epidemiology 368
16.4.2 Pathogenesis of PD 368
16.4.3 Signaling Pathway of Parkinson’s Disease 370
16.4.4 Enzymatic Targets in Parkinson’s Disease 372
16.4.5 Current Therapeutic Approaches to Treat PD 374
16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease 376
16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics 377
16.4.8 Significance of Computational Approaches in Parkinson’s Disease 377
16.4.9 Use of Computational Tools in Identifying Biomarkers 378
16.4.10 Neuroprotective Strategies Through Computational Insights 379
16.4.10.1 Computational Models for Neuroprotection 379
16.4.11 Examples of Computational Successes in Parkinson’s Disease
Drug Development 380
16.4.12 Future Directions and Innovations in Computational Methods
for Parkinson’s Disease 381
16.5 Conclusion 382
References 382
Contentsxvi
17 Rational Design of Anti-inflammatory Therapeutics 389
Kratika Singh, Anmol Gupta, Irum Siddiqui, Ashapurna Sinha, Mukesh Kumar Patwa,
and Urmila Singh
17.1 Introduction 389
17.2 Navigating Inflammation and its Microenvironment 390
17.2.1 Inflammatory Cell Infiltration and Vascular Permeability 390
17.2.2 Acidosis 392
17.2.3 Increased Oxidative Stress in Tissues 392
17.3 The Demand for Advanced Anti-inflammatory Medications 393
17.4 Natural Products Used for Anti-inflammatory Drug Development: Systematic
Approach in Use of Different Animal Models for Evaluations 394
17.5 Rational Design of Anti-inflammatory Agents 394
17.5.1 Creating Anti-inflammatory Polymers Through Phosphoramidite Chemistry Inspired
by Apoptotic Processes 394
17.5.2 New Anti-inflammatory Agent with Indoyl-imidazole Hybrids 394
17.5.3 Rational Design of Novel Aminopiperidinyl Amide 394
17.5.4 Lipid Nanoparticles (LNPs) as Anti-inflammatory Agents 397
17.6 Conclusion and Future Perspectives 397
Authors’ Contribution 397
References 397
18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections 403
Sathish Kumar Konidala, Podila Naresh, Risy Namratha Jamullamudi,
Kamma Harsha Sri, Richie Rashmin Bhandare, and Afzal Basha Shaik
18.1 Introduction 403
18.2 Treatment 404
18.3 Antibacterial Resistance 405
18.3.1 Mutation 406
18.3.2 Horizontal Gene Transfer (HGT) 406
18.3.3 Enzymatic Modification or Degradation 406
18.3.4 Target Site Modification 407
18.3.5 Decreased Permeability 407
18.3.6 Efflux Pumps 407
18.3.7 Plasmids 407
18.3.8 Transposons 407
18.3.9 Gene Amplification 407
18.3.10 Formation of Biofilms 408
18.3.11 Modified Metabolic Pathways 408
18.3.12 Adaptive Evolution 408
18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating
Multidrug-Resistant Bacterial Infections 408
18.4.1 Structure-Based Drug Design 408
18.4.2 Modification of Existing Antibiotics 409
18.4.3 Bioisosterism 409
18.4.4 Prodrug Strategies 410
18.4.5 Similar Bacterial Components Target 411
Contents xvii
18.4.6 Combine or Combination Therapy 412
18.4.7 Drug Repurposing 414
18.4.8 Resistant Mechanism Blocking 414
18.4.9 Improving Drug Delivery by Nanotechnology 415
18.4.10 Phage Intervention 416
18.4.11 Host Targeting 416
18.4.12 CRISPR-Cas Technique 416
18.4.13 Peptides as Antibacterials 416
18.4.14 Immunizations and Immunotherapy 417
18.4.15 Natural Product Derivatives 417
18.4.16 Fragment-Based Drug Discovery (FBDD) 417
18.4.17 Metabolomics and Genetics 417
18.4.18 Cheminformatics 417
18.5 Summary and Conclusion 418
References 418
19 Rational Design of Antiviral Therapeutics 423
Sneha Dokhale, Samiksha Garse, Shine Devarajan, Vaishnavi Thakur, and Shaunak Kolhapure
19.1 Introduction to Antiviral Therapeutics 423
19.1.1 Overview 423
19.1.2 Blueprints for Antiviral Drug Interventions 424
19.1.2.1 Protein Folding and Binding Sites 424
19.1.2.2 Conformational Changes 424
19.1.2.3 Protein–Protein Interactions (PPIs) 426
19.1.2.4 Capsid and Envelope Structures 426
19.1.2.5 Structural Vulnerabilities 426
19.1.2.6 Enzymatic Activities 426
19.1.2.7 Viral Attachment 427
19.1.2.8 Viral Assembly and Replication Machinery 427
19.1.2.9 The Host’s Immune Response 427
19.2 Targets for Antiviral Therapeutics and Inhibition Strategies 427
19.2.1 Enzyme Inhibitors 428
19.2.2 Antiviral Peptides 428
19.2.3 Antiviral Antibodies 430
19.2.4 Lipid-Mimicking Compounds 430
19.2.5 Vaccines 431
19.2.6 Immunomodulation 431
19.3 Rational Strategies for Antiviral Therapeutics 431
19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship) 432
19.3.2 AI and ML 435
19.3.3 Systems Biology and Network Pharmacology 435
19.3.4 CRISPR Systems 436
19.3.5 Nanotechnology-Based Design and Delivery Systems 436
19.3.6 Reverse Vaccinology 437
19.4 Conclusion 437
References 438
Contentsxviii
20 Rational Design of Anticancer Therapeutics 445
Debarupa Dutta Chakraborty and Prithviraj Chakraborty
20.1 Introduction 445
20.2 Rational Design of Nanomedicine for Cancer Treatment 446
20.3 The CAPIR Cascade: A Nanomedicine Strategy for Administering
Cancer Medications 447
20.4 Rational Regulation of Nanoparticle’s Physicochemical Characteristics 447
20.4.1 Particle Size 447
20.4.2 Shape 447
20.4.3 Surface Modification 448
20.5 Some Approaches of Rational Drug Design in Anticancer Theranostics 448
20.6 Artificial Intelligence’s Progress in Anticancer Drug Development 450
20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence 451
20.6.2 Artificial Intelligence for Hit Compound Screening of Anticancer Drugs 451
20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design 451
20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs 451
20.6.5 Reactions to Anticancer Drugs Accurately Predicted with Artificial Intelligence
Support 451
20.7 Conclusion 452
References 452
21 PROTAC and ProTide Strategies in Drug Design 457
Maitreyee Mukherjee
21.1 Introduction 457
21.2 Drug Design: Past to Present 458
21.3 PROTAC Strategy in Drug Design 459
21.3.1 Ubiquitin Proteasome System and PROTACs 461
21.3.2 Chemical Formulations of PROTACs 462
21.3.3 Advent of PROTACs as Antiviral 464
21.3.4 NS3/4A-Targeting PROTACs Against HCV 464
21.3.4.1 Neuraminidase-Targeting PROTACs 465
21.4 Emergence of ProTide Technology in Drug Design 465
21.5 Approaches of ProTides in Drug Development 466
21.6 Implementation of ProTides as Nucleoside Analogs 470
21.6.1 Antiviral Applications of ProTides 470
21.7 Conclusion 471
References 471
22 Advancing Lung Cancer Treatment Through ALK Receptor-targeted Drug Metabolism
and Pharmacokinetics 477
Vivek Yadav, Shikha Goswami, Rajiv Kumar Tonk, and Mithun Rudrapal
22.1 Introduction 477
22.2 ALK Receptor and Its Role 478
22.2.1 ALK Structure 478
22.2.1.1 The ALK Extracellular Domain 478
22.2.1.2 The ALK Kinase Domain 478
22.2.2 Mechanism (EML4-ALK) 479
Contents xix
22.3 Diagnostic Methods for ALK Rearranged NSCLC 479
22.3.1 Fluorescence In Situ Hybridization (FISH) 479
22.3.2 Immunohistochemistry 480
22.3.3 Reverse Transcription-Polymerase Chain Reaction (RT-PCR) 480
22.3.4 Next-Gen Sequencing 481
22.4 ALK Inhibitors Drug Development 481
22.4.1 First-generation ALK Inhibitor 481
22.4.1.1 Crizotinib 481
22.4.2 Second-Generation ALK Inhibitor 482
22.4.2.1 Alectinib 482
22.4.2.2 Brigatinib 483
22.4.2.3 Ceritinib 483
22.4.3 Third-Generation ALK Inhibitor 483
22.4.4 Fourth-generation ALK TKIs Under Investigation 483
22.5 Drug Metabolism of Reported ALK Inhibitor 484
22.5.1 Pharmacokinetic Parameters of ALK Inhibitor 485
22.5.2 PK DDIs: Metabolism 486
22.5.2.1 Crizotinib 486
22.5.2.2 Ceritinib 486
22.5.2.3 Alectinib 487
22.5.2.4 Brigatinib 487
22.5.2.5 Lorlatinib 487
22.5.2.6 Entrectinib 487
22.6 Resistance and Mutations 487
22.7 Conclusion 488
Conflict of Interest 488
References 489
23 Targeting Intrinsically Disordered Proteins (IDPs) in Drug Discovery:
Opportunities and Challenges 493
Sridhar Vemulapalli
23.1 Introduction 493
23.2 Properties and Significance of IDPs 493
23.2.1 Structural Characteristics of IDPs 493
23.2.2 Relationship Between IDPs and Diseases 494
23.3 Challenges in Targeting IDPs 496
23.3.1 Structural Heterogeneity 496
23.3.1.1 Dynamic Conformations 497
23.3.1.2 Lack of Well-Defined Binding Pockets 497
23.3.1.3 Functional Implications 497
23.3.1.4 Dynamic Conformations 497
23.3.1.5 Lack of Well-Defined Binding Sites 497
23.3.1.6 Functional Importance 497
23.3.1.7 Flexibility and Plasticity 497
23.3.1.8 Promiscuous Interactions 498
23.3.1.9 Challenges in Selective Modulation 498
23.3.1.10 Dynamic Interactions 498
Contentsxx
23.3.1.11 Comprehensive Impact 498
23.4 Computational Tools for IDP Analysis 499
23.4.1 Exploring Computational Methods for IDP Analysis 499
23.4.2 Role of Computational Tools in Rational Drug Design 499
23.4.3 Molecular Dynamics Simulations for Tau Protein in Alzheimer’s Disease 499
23.4.3.1 Relevance to Alzheimer’s Disease 499
23.4.4 Examples of Computational Tools in Action 500
23.5 Rational Design Approaches for IDP Inhibition 500
23.5.1 Examination of Allosteric and Orthosteric Binding Site Identification Methods 500
23.5.1.1 Allosteric Binding Sites 500
23.5.1.2 Orthosteric Binding Sites 500
23.5.2 Case Studies Illustrating Successful Rational Design Campaigns 500
23.5.2.1 Pin1–Par14 Interaction 500
23.5.2.2 MDM2-p53 Interaction 504
23.5.2.3 IDP-Targeting PROTACs 504
23.5.2.4 Examples of IDP Targeting 504
23.5.3 Evaluation of Clinical Impact 504
23.5.3.1 Phosphorylation-mediated Effects 504
23.5.3.2 Consideration of Interactions in Drug Design 505
23.5.4 Cancer Therapeutics 505
23.6 Case Studies 505
23.6.1 Case Study 1: p53-MDM2 Interaction Inhibition 505
23.6.2 Case Study 2: N-terminal Tau Binding Pocket 505
23.6.3 Case Study 3: Disordered Protein–Protein Interactions 505
23.6.4 Case Study 4: AlphaFold in Drug Repurposing 506
23.6.5 Evaluation of Clinical Impact 506
23.6.5.1 Cardiovascular Diseases: Troponin I–Actin Interaction 506
23.6.5.2 Infectious Diseases: IDPs in Pathogen–Host Interactions 506
23.6.6 In-Depth Analysis of Computational Methods in IDP-targeting
Compounds Design 506
23.6.6.1 Case Study 1: Disruption of c-Myc–Max Interaction 506
23.6.6.2 Case Study 2: Tau Protein Aggregation in Alzheimer’s Disease 507
23.6.6.3 Case Study 3: Disordered Proteins in Neurodegenerative Disorders 507
23.6.7 Computational Methods in IDP-targeting Compound Design: Clinical
Impact Evaluation 507
23.6.7.1 Breast Cancer Treatment: HER2–HER3 Disruption 507
23.6.7.2 Alzheimer’s Disease: Aβ Oligomerization Inhibitors 507
23.6.7.3 Metabolic Syndrome Intervention 508
23.6.7.4 Antiviral Strategies: Disruption of Viral–Host Interactions 508
23.7 Future Directions 508
23.7.1 Addressing Ongoing Challenges and Avenues for Improvement 508
23.7.2 Discussion of the Possibility of Combination Therapies Involving
IDP-targeting Drugs 509
23.8 Conclusions 509
References 510
Index 519