Handbook of Medicinal Chemistry

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Completely revised and updated, the 2nd edition of The Handbook of Medicinal Chemistry draws together contributions from authoritative practitioners to provide a comprehensive overview of the field as well as insight into the latest trends and research. An ideal companion for students in medicinal chemistry, drug discovery and drug development, while also communicating core principles, the book places the discipline within the context of the burgeoning platform of new modalities now available to drug discovery.

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Útgefandi: Royal Society of Chemistry

ISBN: 9781839165887

Print ISBN: 9781788018982

Format: ePub

Útgáfa: 2

Höfundar

Tungumál: English

Útgefið: 2023-02-03

Prent takmörkun á líftíma: 100

Prent takmörkun: 2

Afritunar takmörkun: 2

Flokkar: Heilbrigðisvísindi

Efnisflokkar: Læknisfræði › Lyfjafræði

Kaflar

Cover
Half title
Title
Copyright
Foreword to the 2nd Edition
Preface to the 2nd Edition
Foreword to the First Edition
Preface to the First Edition
Contents
1 Physicochemical Properties
1.1 Introduction
1.2 Physicochemical Properties
1.3 Lipophilicity
1.3.1 Measuring Log P and Log D
1.3.2 Zwitterions
1.3.3 Other Solvent Systems
1.3.4 Membrane–Water Partition Coefficients
1.3.5 Chromatographic Log D Measurement
1.3.6 Calculating Log P and Log D7.4
1.4 Ionisation Constants
1.4.1 Measuring Ionisation Constants
1.4.2 Calculating Ionisation Constants
1.4.3 Manipulating pKa in Medicinal Chemistry Strategy
1.5 Hydrogen Bonding
1.5.1 Polar Surface Area
1.5.2 Quantifying the Contribution of a Hydrogen Bond
1.6 Solubility
1.6.1 Measurement of Solubility
1.6.2 Calculating Solubility
1.7 The Rule of Five
1.7.1 Beyond the Rule of Five
1.8 Ligand Efficiency Metrics
1.9 Compound Quality and Drug-likeness
1.10 Conclusions
1.11 Hints and Tips
Key References
References
2 Synthesis in Medicinal Chemistry
2.1 The State of the Art – Where are We, and How did We Get Here?
2.1.1 The Evolving Landscape of Synthetic Medicinal Chemistry – Changes in Discovery Practices
2.1.2 Why Do We Use the Reactions We Use?
2.1.3 We are Where we are – but Does it Matter?
2.2 How to Improve Molecules Through Synthesis
2.2.1 Some Historic Examples where Synthesis Enabled Discovery
2.2.2 Four Strategies to Improve Molecules: Methylation, Hydroxylation, Fluorination and Heterocycles
2.2.3 Better Late-stage Functionalisation
2.2.4 Biotransformations
2.2.5 Water-based Chemistry
2.2.6 Mimicking Nature
2.2.7 Isosteres
2.2.8 Fragment-Based Drug Discovery (FBDD)
2.2.9 Avoiding Nuisance Compounds, PAINs and Potentially Reactive Species
2.3 Improving the Efficiency of Synthetic Medicinal Chemistry
2.3.1 AI/Machine Learning in Synthesis Planning
2.3.2 Automation and Linking to Reaction Planning
2.3.3 Synthetic Techniques and Technologies: Flow Chemistry
2.3.4 Purification Technologies and Compound Handling
2.4 Case Studies
2.4.1 AMG-176
2.4.2 E7130, C52-Halichondrin-B-amine
2.5 The Future? New Modalities and Synthetic Impact
References
3 Useful Computational Chemistry Tools for Medicinal Chemistry
3.1 Physics-based vs. Empirical Models
3.2 Molecular Mechanics and Molecular Orbital Theory
3.2.1 Quantum Mechanics
3.2.2 Molecular Mechanics
3.2.3 Electronic Distribution and Electrostatic Isopotentials
3.2.4 3-Dimensional Molecular Similarity
3.2.5 Energy Minimisation
3.3 Molecular Simulation and Dynamics
3.4 Modelling Solvation
3.5 Conformations, Conformational Energy and Drug Design
3.6 Quantifying Molecular Interactions from Experimental Data
3.7 Docking and Scoring Functions
3.8 Cheminformatics
3.9 Examples of Impactful Computational Chemistry on Drug Design
3.10 Hints and Tips
References
4 Structure-based Design for Medicinal Chemists
4.1 Introduction
4.2 History
4.3 Protein Structures for Structure-based Design
4.3.1 Experimentally Determined Structures
4.3.2 Limitations of the Use of X-ray Crystal and Cryo-EM Structures
4.3.3 Predicting Protein Structures: AlphaFold and RoseTTAFold
4.4 Theory and Practice of Structure-based Design
4.4.1 Visualizing Shape Complementarity
4.4.2 What Drives Binding?
4.4.3 Favorable and Unfavorable Waters and Water Networks
4.4.4 Protein Flexibility
4.4.5 Enthalpy–Entropy Compensation
4.4.6 Electrostatics
4.4.7 Small Molecules Bind in Their Lowest Energy, Preferred Conformations
4.4.8 Preferred and Competitive Protein–Ligand Interactions
4.5 Ligand-induced Conformational Change
4.5.1 Hypothesis-based Design
4.5.2 Predicting Affinity: Free Energy Perturbation
4.5.3 Test the Quality of a Design with Ligand Strain Energy, MD, and Docking
4.5.4 Large-scale Virtual Screening
4.5.5 Visual Docking Pose Assessment and Human Expertise
4.5.6 Covalent Inhibitor Design
4.6 Future Directions
4.6.1 DNA Encoded Libraries
4.6.2 DEL and Machine Learning
4.6.3 PROTAC Degraders and Molecular Glues
4.6.4 Cryo-EM
4.6.5 Machine Learning (ML)
4.7 Summary
4.8 Hints and Tips
Key References
References
5 Fragment-based Ligand Discovery (FBLD)
5.1 Introduction
5.2 The General Features of FBLD
5.3 Fragment Library
5.4 Fragment Screening Approaches
5.4.1 Protein-observed NMR
5.4.2 Ligand-observed NMR
5.4.3 Technologies that Sense Binding to Labelled Protein Through Change in Optical Properties
5.4.4 Thermal Shift Analysis (TSA) or Differential Scanning Fluorimetry
5.4.5 Biochemical Assay
5.4.6 Crystallography
5.4.7 Mass Spectrometry
5.4.8 Isothermal Titration Calorimetry (ITC)
5.4.9 Virtual Screening
5.4.10 Other Ideas and Approaches
5.4.11 Validating Fragment Hits – Comparing Methods
5.5 Fragment Hit Rates
5.5.1 Hits vs. Non-hits
5.5.2 Hits for Different Types of Target
5.5.3 Membrane-bound Proteins
5.6 Fragments for Chemical Biology – Target and Mechanism Assessment and Discovery
5.7 Determining Structures of Fragments Bound
5.8 The Evolution of the Ideas and Methods – a Historical Perspective
5.8.1 Some Early Ideas
5.8.2 The Emergence of De Novo Structure-based Design
5.8.3 The Emergence of Fragment-based Discovery
5.8.4 What's in a Name?
5.8.5 Some Important Underpinning Concepts
5.9 Fragment Evolution
5.10 Fragments and Chemical Space
5.11 Concluding Remarks
5.12 Hints and Tips
Key References
Acknowledgements
References
6 Machine Learning in Drug Design
6.1 Introduction
6.2 Development of Deep Generative Models for Molecular Structure Generation
6.2.1 The Emergence of Deep Generative Models
6.2.2 SMILES-based Generative Models
6.2.3 Graph-based Generative Models
6.3 Advances in Deep Learning for ADMET Prediction
6.3.1 Prediction of ADMET Properties
6.3.2 Applications of Deep Learning in ADMET Prediction
6.3.3 Challenges and Opportunities in ADME Modeling
6.4 In Silico Protein Target Deconvolution
6.4.1 Structure- and Ligand-based Approaches Toward Protein–Ligand Prediction
6.4.2 Ligand-based Approaches
6.4.3 Extensions to In Silico Target Deconvolution Methods
6.4.4 Limitations and Future Concerns for Target Prediction Methods
6.5 Synthesis Prediction with Machine Learning
6.5.1 Chemical Reaction Data
6.5.2 Forward Synthesis Prediction
6.5.3 Synthetic Route Prediction
6.6 Conclusions
6.7 Hints and Tips
Key References
References
7 Drug Metabolism
7.1 Introduction
7.2 Drug Metabolism Pathways
7.3 The Key Role of Cytochrome P450 Enzymes
7.4 What Are the Key Organs that Metabolise Drugs?
7.5 Relationship Between Chemical Structure and Rate of CYP Metabolism
7.6 How is Drug Metabolism Studied?
7.7 Metabolites in Assessing Drug Safety and Efficacy
7.7.1 The Industry Perspective
7.7.2 Guidance on Safety Testing of Metabolites
7.7.3 Reactive Metabolites
7.8 What Factors Influence the Metabolism of Drugs?
7.8.1 Dose Level
7.8.2 Route of Administration
7.8.3 Species Difference in Metabolism
7.8.4 Gender-related Differences
7.8.5 Age
7.8.6 Disease
7.8.7 Genetics
7.9 Metabolism-based Drug–Drug Interactions
7.10 How Transporter Proteins Affect Drug Metabolism
7.11 Summary
7.12 Drug Metabolism Hints and Tips
7.13 Recommended Reading
References
8 ADME Optimization in Drug Discovery
8.1 Introduction
8.1.1 Absorption
8.1.2 Distribution
8.1.3 Metabolism
8.1.4 Excretion
8.2 Optimization and Prediction of Key Human Pharmacokinetic Parameters in Drug Discovery
8.2.1 Oral Absorption
8.2.2 Volume of Distribution
8.2.3 Distribution into Central Nervous System
8.2.4 Human Clearance
8.3 ADME Considerations for PROTACs
8.4 Inhaled, Topical and Intravenous Routes of Administration
8.5 Hints and Tips
8.6 Summary
References
9 Molecular Biology for Medicinal Chemists
9.1 A Brief History of Molecular Biology
9.2 Sequencing
9.2.1 History of Sequencing
9.2.2 Sanger Sequencing
9.2.3 Next-generation Sequencing
9.2.4 RNA Sequencing
9.2.5 Third-generation DNA Sequencing
9.3 Gene-editing Methods
9.3.1 Zinc Finger Nucleases (ZFNs)
9.3.2 Transcription Activator-like Effector Nucleases (TALENs)
9.3.3 CRISPR
9.3.4 Gene Expression Modulation by RNA Interference (RNAi)
9.4 Model Systems
9.4.1 Cellular Systems
9.4.2 Immortalised Cells
9.4.3 Primary Cells
9.4.4 Stem Cells
9.5 Gene Expression Studies and Their Application
9.5.1 Application of PCR
9.5.2 Studying Gene Expression by Quantitative Reverse Transcription PCR (qRT-PCR)
9.5.3 Digital PCR
9.5.4 Reporter Cell Lines
9.6 Proteomics in Drug Discovery
9.6.1 Protein Microarrays
9.6.2 Kinase Profiling
9.6.3 Label-free Proteome Profiling
9.7 Hints and Tips
References
10 Assays
10.1 Assay Detection Technologies
10.1.1 Absorbance
10.1.2 Fluorescence
10.1.3 Luminescence
10.1.4 Antibody-based Assays
10.1.5 Mass Spectrometry
10.1.6 Surface Plasmon Resonance
10.1.7 Microscale Thermophoresis (MST)
10.1.8 Protein Thermal Shift and CETSA
10.1.9 Cell Imaging
10.2 Assay Design
10.2.1 Biophysical Assays
10.2.2 Enzyme Assays
10.2.3 Ion Channel Assays
10.2.4 GPCR Assays
10.2.5 Cell Reporter Gene
10.2.6 Cell Phenotypic Assays
10.3 Building the Cascade
10.3.1 Primary Assay
10.3.2 Target Engagement
10.3.3 Mode of Action
10.4 The Cascade in Practise
10.4.1 Data Analysis
10.4.2 Biophysical Assay Data Analysis
10.4.3 Robustness Analysis and Data Comparison
10.4.4 When Assays Fail
10.4.5 The SAR Doesn't Track Across Assays in the Cascade
10.5 Hints and Tips
10.5.1 Assay Design Fundamental Questions
10.5.2 Assay Troubleshooting
10.5.3 What Can You Do When Your Data Looks Odd?
Key References
References
11 In Vitro Biology: Measuring Pharmacological Activity that Will Translate to Clinical Efficacy
11.1 Introduction: Importance of Detailed Mechanistic Understanding
11.2 A Desire to Work With More Physiologically Relevant Systems
11.2.1 Target Integrity
11.2.2 Complex Binding Partners and Cofactors
11.2.3 Binding Interactions and Catalytic Mechanisms
11.3 Pharmacological Profiling
11.3.1 The Challenge with IC50
11.3.2 Mechanism of Enzyme Inhibition
11.3.3 Slow-binding and Tight-binding Enzyme Inhibition
11.4 Translating Through to Dosing: Target Occupancy
11.5 Introduction: Receptors
11.5.1 Agonist Concentration-effect Curves
11.5.2 Full Agonists, Partial Agonists and Inverse Agonists
11.5.3 Optimising Agonists
11.5.4 Antagonists
11.6 Conclusions
11.7 Hints and Tips
Key References
Glossary
References
12 Animal Models: Practical Use and Considerations
12.1 Introduction
12.2 Where Do Animal Models Fit into the Process of Drug Discovery and Development?
12.3 Type of Animal Studies Required in Drug Discovery and Development (see Figure 12.5)
12.3.1 In Vivo PK Screening
12.3.2 In Vivo PD Screening
12.3.3 Disease/Mechanism Models
12.3.4 Toxicology Models
12.4 Validity of Animal Models
12.4.1 Face Validity
12.4.2 Construct Validity
12.4.3 Predictive Validity
12.5 Before the Study
12.5.1 Ethics
12.5.2 Licence Types
12.6 Study Design
12.6.1 Selecting the Species
12.6.2 Selecting the Model
12.6.3 Objectives
12.6.4 Selecting the Endpoint
12.6.5 Powering a Study
12.6.6 Controls
12.6.7 Experimental Design
12.7 During the Study
12.7.1 Animal Suppliers
12.7.2 Animal Housing
12.7.3 Animal Handling
12.7.4 Animal Weight and Age
12.7.5 Dosing
12.7.6 Drug Formulation
12.8 After the Study
12.8.1 Statistical Analysis
12.8.2 Scientific Reporting
12.8.3 Unexplained Data Exclusions
12.9 Benefits and Downfalls of Animal Research Studies
12.9.1 What are the Benefits?
12.9.2 What are the Limitations?
12.10 The Future
12.10.1 Where Animal Research is Going/New Horizons
12.10.2 In What Ways are People Trying to Improve the Research?
12.11 Summary
12.12 Hints and Tips
List of Abbreviations
References
13 Bioinformatics for Medicinal Chemistry
13.1 Introduction
13.2 From Genes to Drugs and Back Again: Past, Current and Future Perspectives on Omics Data in Drug Discovery
13.2.1 Drug Discovery
13.2.2 Drug Development
13.3 Resources for Understanding Targets for Medicinal Chemistry
13.3.1 Genes, Proteins and Functional Genomics
13.3.2 Reactions, Pathways and Metabolites
13.3.3 Drugs, Targets and Biological Effects
13.3.4 Systems Biology Resources and Drug Discovery
13.3.5 Ontologies
13.4 Harnessing Omics Data for Drug Discovery – The Open Targets Platform for Drug Target Identification and Prioritisation
13.5 Conclusions and Predictions for the Future
13.6 Hints and Tips
13.6.1 Bioinformatics Skills and Tools
13.6.2 Control of Gene and Protein Synthesis
13.6.3 Types of Genetic Variation
13.6.4 Targets with Limited Data
13.6.5 Significance in GWAS Studies
13.6.6 Large-scale Bioactivity Data
13.6.7 Accessing Data in Repositories
13.6.8 Bioinformatics language (Table 13.2)
13.7 Databases
Key References
Acknowledgements
References
14 Translational Science
14.1 Introduction
14.2 Target Science as a Starting Point for Drug Discovery
14.3 Target Identification
14.4 Processes and Methods of Target Validation
14.5 Biomarker Science
14.6 Digital Biomarkers
14.7 The Role of Data Science and Real-world Evidence in Translational Science
14.8 Conclusions
14.9 Hints and Tips
Key References
References
15 Discovery Toxicology in Lead Optimization
15.1 Introduction
15.2 In Silico Toxicology
15.2.1 In Silico Toxicology Tools
15.2.2 Databases
15.2.3 QSARs and Statistical Modelling
15.2.4 Human Knowledge-based Methods
15.2.5 ADME-Tox Modelling
15.2.6 Application of In silico Tools in Lead Optimization
15.3 Target Selectivity
15.3.1 The Target Panel for In Vitro Selectivity Evaluation
15.3.2 Testing Strategies
15.3.3 Data Interpretation
15.4 Cell Viability Assessment
15.5 In Vitro Liabilities
15.5.1 Drug-induced Liver Injury (DILI)
15.5.2 Cardiac Liability
15.5.3 Central Nervous System (CNS) Liability
15.6 Drug–Drug Interactions
15.6.1 Drug–Drug Interactions Mechanisms
15.6.2 CYP-driven DDI Test Systems
15.6.3 Drug-metabolizing Enzyme Inhibition
15.6.4 Pathway Identification
15.6.5 Drug-metabolizing Enzyme Induction
15.6.6 Transporter-mediated Drug Interactions
15.6.7 PBPK Modelling
15.7 Phospholipidosis
15.8 Phototoxicity
15.9 Genotoxicity
15.9.1 Bacterial Tests
15.9.2 In Vitro Mammalian Tests
15.9.3 Evaluation of Results
15.10 Early In Vivo Toxicology
15.10.1 Preliminary Pharmacokinetics
15.10.2 In Vivo Tox Study
15.10.3 Early Safety Pharmacology Evaluation
15.11 Hints and Tips
Key References
References
16 Toxicology and Drug Development
16.1 Introduction and Background
16.2 Toxicology Testing
16.2.1 Safety Pharmacology
16.2.2 Genetic Toxicology
16.2.3 General Toxicology
16.2.4 Developmental and Reproductive Toxicology
16.2.5 Carcinogenicity
16.2.6 Miscellaneous Studies
16.2.7 Toxicokinetics
16.3 Small Molecule Drugs vs. Biopharmaceuticals
16.4 Regulatory Decision Making
16.5 Hints and Tips
Disclaimer
Key References
Acknowledgements
References
17 Patents for Medicines
17.1 Introduction
17.2 What is a Patent?
17.3 What Conditions Need to be Fulfilled in Order for a Patent to be Granted? Patentability
17.3.1 Novelty
17.3.2 Inventive Step
17.3.3 Industrial Applicability
17.3.4 Exclusions
17.3.5 Clarity and Sufficiency/Reproducibility
17.4 Anatomy of a Patent Specification
17.4.1 The Description
17.4.2 Types of Patent Claim
17.4.3 Case Study (a): Typical Claims in a Pharmaceutical Patent
17.4.4 Case Study (b): The Importance of Experimental Data – Inventive Step
17.4.5 Case Study (c): The Importance of Experimental Data – Sufficiency
17.5 Ownership and Inventorship
17.6 The Process for Obtaining a Patent
17.6.1 The National Nature of Patents
17.6.2 A Typical Application Process
17.6.3 Costs
17.6.4 The National Phase: Examination of Patent Applications
17.6.5 Strategic Aspects
17.7 The Patent after Grant
17.7.1 Maintenance
17.7.2 Extension of Patents
17.7.3 Challenges to Validity
17.8 Use of Patents
17.8.1 Infringement and Enforcement
17.8.2 Defences and Exemptions to Infringement
17.8.3 The Consequences of Patent Infringement
17.8.4 Licensing
17.8.5 The Patent Box
17.9 Generic Medicines and Hurdles to Generic Competition
17.9.1 What is a Generic Medicine?
17.9.2 Regulatory Data Exclusivity
17.9.3 Technical Hurdles
17.10 Patents as a Source of Information
17.11 Summary
Acknowledgements
References
18 Target Validation for Medicinal Chemists
18.1 Introduction
18.2 ‘Hide and Seq’ – Searching for Novel Targets
18.2.1 Genetic and Epigenetic Factors
18.2.2 Exploiting ‘Big Data’
18.2.3 Finding Targets in Tissues
18.2.4 Starting with Quality Material
18.3 Phenotypic Screens – Search and Partial Validation in One System?
18.3.1 Techniques to Modulate Gene Expression
18.3.2 Screening the Secretome
18.4 Functional Cell Systems – Assessing ‘Differential Patient Biology’
18.4.1 Co-culture Systems
18.4.2 Environmental Stimuli
18.4.3 Organoids
18.4.4 Microphysiological Systems
18.4.5 Cell Source
18.5 Ex Vivo Tissue Systems in Target Validation
18.5.1 Precision-cut Tissue Slices
18.5.2 Isolated Organs
18.6 In Vivo Novel Target Validation
18.7 Preparing for Drug Discovery
18.7.1 Assessing Risk in Exploratory Portfolio
18.7.2 Drug Discovery Transition
18.8 Case Study: Platelet Derived Growth Factor as a Target for Pulmonary Arterial Hypertension
18.9 Summary
18.10 Target Validation: Pro Tips
Acknowledgements
References
19 Lead Generation
19.1 Introduction
19.1.1 What Is Lead Generation and Why Do We Need It?
19.1.2 The Lead Generation Process
19.1.3 Hit Identification
19.1.4 Hit to Lead
19.2 Hit-finding Approaches
19.2.1 Knowledge-based Approaches
19.2.2 High-throughput Screening
19.2.3 DNA Encoded Library Screening
19.2.4 Focussed Screening
19.2.5 In Silico (Virtual) Screening
19.2.6 Fragment-based Lead Generation
19.2.7 Phenotypic Drug Discovery
19.2.8 Integrated Lead Generation Approaches
19.3 Hit to Lead
19.4 Conclusion
19.5 Hints and Tips
Key References
References
20 Lead Optimisation: What You Should Know!
20.1 The Role of Lead Optimisation
20.1.1 What is Obtained from Lead Identification: Assessing the Series
20.1.2 Defining the Ultimate Candidate Profile
20.1.3 Progressing into Clinical Development – Dose Selection
20.1.4 The Process of Optimisation
20.1.5 Screening Cascade
20.1.6 Decision-making in the Screening Cycle
20.1.7 Progression Criteria
20.1.8 Predicted Properties
20.1.9 The Use of Colour to Simplify Decision-making
20.2 Lead Optimisation – The Practicalities
20.2.1 Quality of Start Point is of Paramount Importance
20.2.2 Starting Lead Optimisation: Identifying the Weaknesses
20.2.3 Formulating a Strategy for Full Lead Optimisation
20.2.4 Strategies to Optimise Common Parameters in Early Lead Optimisation
20.2.5 Drop-off in Cellular Potency and Downstream Assays
20.2.6 Selectivity
20.2.7 Solubility
20.2.8 Metabolism
20.2.9 Toxicity and Phospholipidosis
20.2.10 Pfizer MPO
20.3 The End Game: Choosing the Candidate Drug
20.3.1 Shortlisting
20.3.2 Scale-up and Safety Testing
20.3.3 Back-up Approaches
20.4 LTC4S Inhibitor LO Case Study
20.4.1 Pharmacological Profile of AZD9898
20.4.2 Human Dose Predictions
20.5 Hints and Tips
Acknowledgements
References
21 Pharmaceutical Properties – The Importance of Solid Form Selection
21.1 Introduction
21.2 Solid State Chemistry
21.2.1 Crystallography
21.2.2 Crystal Chemistry and Crystal Packing of Drug Molecules
21.2.3 Intermolecular Interactions, Crystal Packing Energies
21.2.4 Crystallisation Solubility, Supersaturation and Metastable Zone
21.2.5 Pharmaceutical Polymorphs and the Solid State
21.2.6 Polymorphism, Thermodynamic Stability and Solubility
21.3 Industry Practises
21.3.1 Salt Screening and Selection
21.3.2 Co-crystal Screening
21.3.3 Polymorph Screening
21.3.4 Hydrate Screening
21.3.5 Amorphous Forms
21.3.6 Manufacturability and Particle Engineering
21.3.7 Computational Solid Form Design
21.4 Integration Within the Early Clinical Phases of Development
21.4.1 The Changing Drug Product Design Paradigm
21.5 Different Requirements for Dosage Form Types
21.6 Integration of Enabling Formulation Strategies
21.7 Future Outlook
References
22 The Medicinal Chemistry and Process Chemistry Interface
22.1 Why is the Medicinal Chemistry–Process Chemistry Interface Important?
22.1.1 Strategic Differences Between Medicinal Chemistry and Process Chemistry Syntheses
22.1.2 What Constitutes a Good Process Chemistry Synthesis?
22.1.3 Early Signs of Potential Issues
22.2 Collaboration Opportunities at the Medicinal Chemistry–Process Chemistry Interface
22.2.1 Provision of Information
22.2.2 Sustainability
22.2.3 Process Safety
22.2.4 Route Design
22.2.5 Process Design
22.3 Emerging 21st Century Challenges
22.4 Concluding Remarks
22.5 Hints and Tips
Key References
References
23 Clinical Drug Development
23.1 Introduction
23.2 Types of Clinical Trials
23.3 Phases of Drug Development
23.4 Targeted Drugs/Companion Diagnostics and Biomarkers
23.5 Platform of Evidence and Probability of Success
23.6 Basic Considerations/Principles Including Statistics
23.7 Target Product Profile
23.8 Study Protocol
23.9 Clinical Trial Database
23.10 Study Result Reporting
23.11 Independent Review Board (IRB)
23.12 Health Authorities and Ethical Considerations
23.13 Investigational Brochure
23.14 Study Teams
23.15 Examples of Development Programmes
23.16 Hints and Tips
Key References
References
24 SMN2 Splicing Modification by Small Molecules – A Blueprint to Tackle the Underlying Genetic Cause of Many Underserved Diseases
24.1 Introduction
24.2 Spinal Muscular Atrophy and Its Treatment Options
24.3 Systemic Requirement of SMN Rescue for the Treatment of SMA
24.4 Small Molecule SMN2 Splicing Modifiers
24.4.1 Discovery of Branaplam
24.5 Discovery of Risdiplam
24.6 Clinical Development of Risdiplam
24.7 Assessing Effects of Small Molecule Splicing Modifiers
24.8 Conclusions and Future Outlook
References
25 The Discovery of Checkpoint Kinase 1 Inhibitors: From Fragments to Clinical Candidate
25.1 Introduction
25.2 CHK1 and Cancer
25.3 Starting Points
25.3.1 CHK1 Inhibitor Scaffolds and Target Profile
25.3.2 Collaborating to Progress CHK1 Fragment Screening
25.4 From Fragments to In Vivo Chemical Tools
25.4.1 Evaluating and Growing the Hit Fragments
25.4.2 Scaffold Morphing to Cell Active Leads
25.4.3 From Cell Active Chemical Tools to In Vivo Proof of Concept with Oral Agents
25.5 Clinical Candidate Discovery
25.5.1 Multi-parameter In Vitro and In Vivo Optimisation
25.5.2 In Vivo Pharmacology and Predictions to Human
25.5.3 Expanding and Defining Potential Clinical Contexts
25.6 Preclinical Development, Early Clinical Trials and New Clinical Contexts
25.7 Conclusions
25.8 Hints and Tips
Conflict of Interest Statement
Acknowledgements
References
26 Medicinal Chemistry for Neglected Diseases – Malaria, Tuberculosis, Sleeping Sickness, Leishmaniasis and River Blindness
26.1 Introduction: Neglected Tropical Diseases and Diseases of Poverty
26.2 Malaria
26.2.1 Disease Overview
26.2.2 Plasmodium Lifecycle
26.2.3 Current Landscape and Strategic Focus
26.2.4 Antimalarial Medicinal Chemistry – Key Learnings
26.2.5 Portfolio – Case Studies
26.3 Tuberculosis
26.3.1 Disease Overview
26.3.2 Mycobacterium tuberculosis Lifecycle
26.3.3 Current Treatments, Portfolio and Gaps
26.3.4 Antitubercular Medicinal Chemistry – Key Learnings
26.3.5 Portfolio – Case Study
26.4 Human African Trypanosomiasis and Leishmaniasis
26.4.1 Disease Overview
26.4.2 Pathogen Lifecycles of T. brucei and Leishmania spp.
26.4.3 Current Treatments for HAT and Leishmaniasis
26.4.4 Challenges in the Discovery of Novel Treatments of HAT and Leishmaniasis
26.4.5 Portfolio Progress and Needs for Novel Treatments of HAT and Leishmaniasis
26.5 Onchocerciasis
26.5.1 Disease Overview, Filarial Diseases
26.5.2 Pathogen Lifecycle Onchocerca volvulus
26.5.3 Current Treatments of Filarial Diseases
26.5.4 Challenges in the Discovery of Novel Treatments of Onchocerciasis
26.5.5 Portfolio Progress and Need for Treatments of Filarial Diseases
26.6 Conclusion
26.7 Hints and Tips
Key References
Acknowledgements
References
27 New Therapeutic Chemical Modalities: Compositions, Modes-of-action, and Drug Discovery
27.1 Introduction
27.2 Chemical Compositions and Architectures
27.2.1 Main Chemical Categories of New Modalities as Single Entities
27.2.2 Mixed Compositions
27.3 Modes-of-action of New Modalities
27.3.1 MOAs at the DNA Level
27.3.2 MOAs at the RNA Level
27.3.3 MOAs at the Protein Level
27.4 Hit Finding Strategies and Drug Design of New Modalities
27.4.1 Oligonucleotides
27.4.2 Peptides
27.4.3 Heterobifunctional Degraders
27.5 Optimisation of New Modalities
27.5.1 Pharmacokinetics
27.5.2 Safety
27.5.3 Delivery/Route of Administration
27.6 Selection of Chemical Modalities
27.6.1 Framework for Modality Selection
27.6.2 Examples of Modality Selection
27.6.3 Further Considerations
27.7 Conclusion: Clinical Progress and Perspectives
27.8 Hints and Tips
Key References
List of Abbreviations
References
Subject Index