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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Computational Methods for Rational Drug Design
- •Contents
- •1.1.2.2 GROMACS
- •1.1.2.3 Amber
- •1.1.2.4 CHARMM
- •1.1.2.5 AutoDock
- •1.1.2.6 VMD
- •1.1.2.7 PyMOL
- •1.1.2.8 Open Babel
- •List of Contributors
- •Preface
- •1. Molecular Modeling and Drug Design
- •1.1 Introduction
- •1.1.1 What Is Molecular Modeling?
- •1.1.2 Software Used for Molecular Modeling
- •1.1.2.1 Schrodinger
- •1.1.2.9 Avogadro
- •1.1.2.10 Discovery Studio
- •1.1.3 Molecular Mechanics
- •1.1.3.1 Prediction of Binding Affinity
- •1.1.3.2 Conformational Analysis
- •1.1.3.3 Virtual Screening
- •1.1.3.4 Lead Discovery
- •1.1.3.5 Mechanism of Action
- •1.2 Types of Molecular Models
- •1.2.1 Ball-and-Spoke Model
- •1.2.1.1 Future Directions
- •1.2.2 Space-filling Models
- •1.2.2.1 Future Directions
- •1.2.3 Crystal Lattice Models
- •1.2.3.1 Future Directions
- •1.3 Computational Methods in Drug Discovery
- •1.3.1 What Is Drug Discovery?
- •1.3.2 Computational Platforms for Drug Discovery
- •1.3.2.1 NCBI
- •1.3.2.2 Chemical Databases
- •1.3.2.3 PDB
- •1.3.2.5 UniProt
- •1.3.2.6 QSAR
- •1.3.2.8 Desmond
- •1.3.2.9 OpenBabel
- •1.3.2.10 DeepChem and Cheminformatics for Python (RDKit)
- •1.3.2.11 SBML
- •1.3.2.12 Virtual Screening
- •1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery
- •1.4 Potential Use and Application of AI in Drug Designing
- •1.4.1 Target Identification and Validation
- •1.4.2 Drug Screening and Lead Optimization
- •1.4.3 De Novo Drug Design
- •1.4.4 Predictive Toxicology and ADMET
- •1.4.5 Clinical Trial Optimization
- •1.4.6 Drug Repurposing
- •1.4.7 Concept of Personalized Medicine
- •1.4.8 Drug Combination Optimization
- •1.5 Limitations of Current Methods
- •1.5.1 Data Restrictions
- •1.5.2 Interpretability
- •1.5.3 Generalization
- •1.5.4 Resources and Computation
- •1.5.5 Ethical Considerations
- •1.5.6 Validation and Experimentation
- •1.5.7 Regulatory Obstacles
- •1.6 Case Studies
- •1.7 Molecular Docking
- •1.7.1 What Is Molecular Docking?
- •1.7.1.1 Procedure
- •1.7.1.2 Biophysical Laws
- •1.7.1.3 Rigid and Flexible Docking
- •1.7.1.4 Types of Docking
- •1.7.1.5 Challenges and Future Perspectives
- •1.7.2 Applications of Molecular Docking in Drug Designing
- •1.7.3 Success of Molecular Docking Cases in Drug Designing
- •1.8 Conclusion and Future Works
- •References
- •2. Bioactive Small Molecules and Drug Discovery
- •2.1 Introduction
- •2.1.1 Introduction to Drug Design and Discovery
- •2.1.2 Brief History of Small-Molecule Drug Discovery
- •2.1.3 Importance of Bioactive Small Molecules in Drug Discovery
- •2.2.1 Structure-Based Methods
- •2.2.2 Ligand-Based Methods
- •2.2.3 Network-Based Methods
- •2.3 Natural Products in Bioactive Small-Molecule Discovery
- •2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules
- •2.3.2 Anticancer Agents as Bioactive Molecules
- •2.3.3 Antiviral Agents as Bioactive Molecules
- •2.3.4 Antimalarial Agents as Bioactive Molecules
- •2.6.6 Toxicity and Side Effects
- •2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability
- •2.6.8 Structural Diversity and Novelty
- •2.6.9 Patentability and Intellectual Property
- •2.3.5 Marine Bioactive Products
- •2.4.1 Importance of DFT in Small-Molecule Drug Discovery
- •2.5 Application of DFT to Bioactive Small Molecules
- •2.5.1 HOMO–LUMO Calculation
- •2.5.1.1 Molecular Electrostatic Potential (MEP) Map
- •2.5.1.3 Natural Bond Orbital (NBO) Analysis
- •2.5.1.4 Implementations and Tools
- •2.6.1 Target Identification and Validation
- •2.6.2 Target Specificity
- •2.6.3 Bioavailability and Pharmacokinetics
- •2.6.4 Chemical Structure and Drug-likeness
- •2.6.5 Safety and Toxicity
- •2.7 Conclusion
- •References
- •3. Novel Drug Targets for Small Molecule-based Drug Discovery
- •3.1 Introduction
- •3.2 Drug Target Identification
- •3.3 Classification of Novel Drug Targets
- •3.3.1 Transcription Factors
- •3.3.2 Cytokines
- •3.3.3 Chaperones
- •3.3.4 Viral Targets
- •3.3.5 G Protein-coupled Receptors
- •3.3.6 Transporters
- •3.3.7 Enzymes
- •3.3.8 RNA Targets
- •3.4 Small Molecules as Drugs
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Structure-Based Drug Discovery Concept
- •4.2.1 Structure Generation of the Target
- •4.2.1.1 The Detailed Description of Each Tool
- •4.2.2 Active Binding Site Within the Target
- •4.2.2.1 The Detailed Description of Each Tool
- •4.2.2.2 Molecular Docking Analysis
- •4.2.2.3 The Detailed Description of Each Tool
- •4.2.3 Molecular Dynamic Simulations
- •4.2.3.1 The Detailed Description of Each Tool
- •4.3 Ligand-Based Drug Discovery Concept
- •4.3.1.1 The Detailed Description of Each Tool
- •4.4 Structure- and Ligand-Based Assisted Studies
- •4.4.1 The Detailed Description of Each Tool
- •4.4.2 The Detailed Description of Each Tool
- •4.5 Advancement and Challenges in SBDD and LBDD
- •4.6 Conclusion
- •References
- •5. Virtual Screening and Lead Discovery
- •5.1 Introduction to Virtual Screening and Lead Discovery
- •5.1.1 Overview of Drug Discovery Process
- •5.1.2 Role of Virtual Screening
- •5.1.3 Importance of Lead Discovery
- •5.2 Molecular Targets and Biomolecular Structures
- •5.3 Virtual Screening Approaches
- •5.3.1 Structure-based Virtual Screening
- •5.3.2 Ligand-based Virtual Screening
- •5.3.3 Hybrid Approaches
- •5.4 Databases and Compound Collections
- •5.4.1 Overview of Chemical Databases
- •5.4.2 Compound Filtering and Preparation
- •5.4.3 Diversity and Size of Compound Collections
- •5.5 Molecular Docking
- •5.5.1 Principles of Molecular Docking
- •5.5.2 Docking Algorithms and Scoring Functions
- •5.5.3 Validation of Docking Results
- •5.6 Pharmacophore Modeling
- •5.6.1 Concept of Pharmacophores
- •5.6.2 Generating Pharmacophore Models
- •5.6.3 Applications in Lead Discovery
- •5.7 Quantitative Structure–Activity Relationship (QSAR)
- •5.7.1 Basics of QSAR
- •5.7.2 Model Development and Validation
- •5.7.3 QSAR in Virtual Screening
- •5.8 Machine Learning and AI in Virtual Screening
- •5.8.1 Introduction to Machine Learning and AI
- •5.8.2 Feature Selection and Model Training
- •5.8.3 Applications in Virtual Screening
- •5.9 Hit-to-Lead Optimization
- •5.9.1 Prioritizing Hits from Virtual Screening
- •5.9.2 SAR Analysis and Iterative Design
- •5.9.2.1 SAR Analysis (Structure–Activity Relationship)
- •5.9.2.2 Iterative Design
- •5.9.3 ADME/Tox Considerations
- •5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion)
- •5.9.3.2 Toxicity Considerations
- •5.10 Case Studies and Examples
- •5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors
- •5.11 Challenges and Future Directions
- •5.11.1 Limitations of Virtual Screening
- •5.11.2 Emerging Technologies and Trends
- •5.11.3 Integration with High-throughput Experimentation
- •5.12 Ethical and Regulatory Considerations
- •5.12.1 Intellectual Property and Patents
- •5.12.2 Ethical Use of Computational Tools
- •5.12.3 Regulatory Approval Process
- •5.13 Conclusion
- •5.13.1 Future Prospects in Virtual Screening and Lead Discovery
- •5.13.2 Summary of Key Points
- •References
- •6. ADMET and Physicochemical Assessments in Drug Design
- •6.1 ADMET
- •6.1.1 Absorption
- •6.1.1.1 Solubility and Dissolution
- •6.1.1.2 Lipophilicity
- •6.1.1.3 Permeability
- •6.1.2 Distribution
- •6.1.3 Metabolism
- •6.1.4 Excretion
- •6.1.5 Toxicity
- •6.2 Physicochemical Assessments
- •6.2.1 Partition Coefficient
- •6.2.2 Log D: Ionizable Compound Lipophilicity
- •6.2.2.1 Methods for Calculating Lipophilicity
- •6.2.2.2 Direct Experimental Determination of Lipophilicity
- •6.2.2.3 Indirect Experimental Determination of Lipophilicity
- •6.2.3 Acid–Base Properties and Ionization
- •6.2.4 Solubility
- •6.2.5 Polymorphism
- •6.2.6 Molecular Weight
- •6.2.7 Number of Hydrogen Bond Donors (HDB) and Acceptors (HDA)
- •References
- •7. In Silico Modeling and Drug Design
- •7.1 Introduction
- •7.2 Target Identification
- •7.2.1 Experimental Approaches
- •7.2.2 Computational Target Identification
- •7.2.3 Target Validation
- •7.3 Computer-Aided Drug Design
- •7.3.1 Ligand-based CADD
- •7.3.2 Structure-Based CADD
- •7.4 ADMET Assessment
- •7.5 Conclusion
- •References
- •8. Pharmacophore Modeling in Drug Design
- •8.1 Introduction
- •8.1.1 The Role of Pharmacophore Modeling in Drug Design
- •8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts
- •8.2 Essential Concepts in Pharmacophore Hypothesis Generation
- •8.2.1.1 Partitioning Initial Data into Distinctive Datasets
- •8.3 Diverse Approaches to Pharmacophore Modeling
- •8.3.1 Ligand-Based Pharmacophore Modeling
- •8.3.2 Structure-Based Pharmacophore Modeling
- •8.4 Application of Pharmacophore Modeling
- •8.4.1 Applications of Pharmacophore-Based Virtual Screening
- •8.4.1.1 Drug Discovery
- •8.4.2 Applications in Drug Target Fishing
- •8.4.3 Applications in Ligand Profiling
- •8.4.4 Applications in Docking
- •8.4.5 Applications in ADMET
- •8.4.6 Modulation of the Immune System
- •8.5 Emerging Trends in Pharmacophore Model Development
- •8.5.1 Involvement of Machine Learning
- •8.5.2 Prediction of Pharmacokinetic Properties
- •8.5.3 Structural Biology and Protein Functionality Studies
- •8.5.4 Integration with MDs Simulations
- •8.6 Case Studies
- •8.6.1 Case 1
- •8.6.2 Case 2
- •8.7 Challenges in Pharmacophore Modeling
- •8.8 Conclusion
- •Acknowledgments
- •References
- •9. Scaffold Hopping and De Novo Drug Design
- •9.1 Introduction
- •9.2 Scaffold Hopping
- •9.2.1 Classification of Scaffold Hopping
- •9.2.1.1 1° Hop: Heterocycle Replacement
- •9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures
- •9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics
- •9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping
- •9.2.2 Advantages of Scaffold Hopping
- •9.2.3 Disadvantages of Scaffold Hopping
- •9.2.4 Reasons for Scaffold Hopping
- •9.2.5 Properties and Key Methods of Scaffold Hopping
- •9.3 De Novo Drug Design
- •9.3.1 Classification of De Novo Drug Design
- •9.3.1.1 Structure-based Drug Design
- •9.3.1.2 Ligand-based Drug Design
- •9.3.1.3 De Novo Design Strategies
- •9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design
- •9.3.1.5 Hybrid Approaches
- •9.3.2 Basic Principle of De Novo Drug Design
- •9.3.3 Application of De Novo Drug Design
- •9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design
- •9.3.5 Methodological Approaches in De Novo Drug Design
- •9.3.5.1 Structure-based De Novo Drug Design
- •9.3.5.2 Ligand-based De Novo Drug Design
- •9.3.5.3 Generation of Drug-Like Molecular Fragments
- •9.3.5.4 Similarity Searching
- •9.3.5.5 Selection of Target Reference Structure
- •9.3.5.6 Similarity Analysis of De Novo-generated Compounds
- •9.3.5.7 Evaluation of Scaffold Diversity
- •9.4 Results and Discussion
- •9.4.1 Generation of Drug-Like Molecular Fragments
- •9.4.2 De Novo Design with a Single Reference Structure
- •9.4.3 De Novo Design with a Focused Set of Five Similar Templates
- •9.4.4 De Novo Design with a Diverse Set of Five Templates
- •9.6 Case Study
- •9.6.1 De Novo Drug Design
- •9.6.2 Scaffold Hopping
- •9.7 Conclusion
- •References
- •10. Fragment-based Drug Design and Drug Discovery
- •10.1 Introduction
- •10.2 The Process of Finding Fragments
- •10.3 FBDD Strategies
- •10.4 Case Studies
- •10.5 Conclusion and Future Perspectives
- •References
- •11. AI/ML Approaches in Drug Design
- •11.1 Introduction
- •11.2 Traditional Drug Design Methods
- •11.2.1 The Rise of Computational Methods
- •11.2.2 The Importance of AI/ML in Modern Drug Design
- •11.3 AI/ML Landscape in Drug Design
- •11.3.1 AI/ML Algorithms and Methods
- •11.3.1.1 Machine Learning Models
- •11.3.1.2 Neural Networks
- •11.3.2 Applications in Drug Design
- •11.3.2.1 Peptide Synthesis
- •11.3.2.2 Molecular Design
- •11.3.2.3 Virtual Screening (VS)
- •11.3.2.4 Quantitative Structure–Activity Relationship Models
- •11.3.2.5 Drug Repurposing
- •11.3.3 Challenges and Failures
- •11.4 Ethics, Reliability, and Regulatory Issues
- •11.5 Future Directions
- •11.6 Conclusion
- •References
- •12. Network-based Methods in Drug Discovery
- •12.1 Introduction
- •12.1.1 Background of Drug Discovery Future Challenges
- •12.1.2 Single Target Approach Limitations
- •12.1.3 Emergence of Network Biology and Polypharmacology
- •12.2 Network Pharmacology: Practical Guide
- •12.2.1 Common Network Pharmacology Databases
- •12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools
- •12.2.1.2 Exploring IMPPAT Network Pharmacology Databases
- •12.2.1.3 Target Genes of Phytoconstituents
- •12.2.2 Network Analysis and Visualization
- •12.2.3 Applications of Network Pharmacology in Drug Discovery
- •12.3 Ayurveda and Traditional Indian Medicine
- •12.3.1 Overview of Ayurveda and Its Complex Formulations
- •12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines
- •12.4 Network Pharmacology in Herbal Remedies
- •12.4.1 Application of Network Pharmacology in Herbal Drug Discovery
- •12.4.1.1 Cancer
- •12.4.1.2 Cardiovascular Diseases (CVDs)
- •12.4.1.3 Diabetes Mellitus (DM)
- •12.4.2 Screening Pharmacological Efficacy of Herbal Remedies
- •12.4.3 Utilizing Network Pharmacology to Understand Complex Diseases
- •12.5 Conclusion and Future Prospects
- •References
- •13. Rational Design of Natural Products for Drug Discovery
- •13.1 Introduction
- •13.2 Natural Products for the Development of New Drugs
- •13.3 Criteria for Selecting Natural Products for Drug Design
- •13.4 Importance of Biodiversity in Sourcing Natural Products
- •13.5 Structural Elucidation of Natural Products
- •13.6.3 High-Throughput Screening Methods for Efficient Compound Selection
- •13.6.4 Molecular Dynamics Simulations for Predicting Solubility and Stability
- •13.6.5 ADMET Attributes Predicted In Silico
- •13.7 Formulation Challenges with Natural Products
- •13.8 Quality by Design (QbD) Approaches
- •13.8.1 Use of Computational Models for Formulation Optimization
- •13.9 Conclusion
- •References
- •14. Design of Enzyme Inhibitors in Drug Discovery
- •14.1 Introduction
- •14.3 Classification of Enzyme Inhibitors
- •14.3.1 Reversible Inhibitors
- •14.3.2 Irreversible Inhibitors
- •14.3.3 Competitive Inhibitors
- •14.3.4 Noncompetitive Inhibitors
- •14.3.5 Allosteric Modulators
- •14.4.1 Structure-Based Design
- •14.4.2 Computer-Aided Design
- •14.4.3 Fragment-Based Design
- •14.4.4 Virtual Screening Method
- •14.4.4.1 Ligand Based
- •14.4.4.2 Receptor Based
- •14.4.5 Natural Product-Based Discovery
- •14.4.6 Using Iterative Protein Crystallographic Analysis
- •14.4.7 Utilization of Covalent Inhibitors
- •14.4.8 Encapsulation Techniques
- •14.4.9 Based on Active-Site Specificity
- •14.4.10 Machine Learning Inhibitor Design
- •14.4.11 Enzyme-Templated Dynamic Combinatorial Chemistry
- •14.5 Limitations and Challenges
- •14.6 Future Directions
- •14.7 Conclusion
- •References
- •15.1 Introduction
- •15.2 Peptides as Therapeutics
- •15.2.1 Peptide Antibiotics
- •15.2.1.1 Peptides in Bone Diseases
- •15.2.1.2 Peptides in Cancer
- •15.2.1.3 Peptides in Metabolic Diseases
- •15.2.1.4 Peptides in Gastrointestinal Diseases
- •15.2.2 Advantages and Limitations of Peptide Therapeutics
- •15.2.3 FDA-Approved Peptide Therapeutics
- •15.2.4 Peptide-Based Entities in Clinical Trials
- •15.2.5 Peptide Synthesis and Diversification
- •15.2.5.1 Chemical Synthesis of Peptides
- •15.2.5.2 Chemical Modification of Peptide and Peptidomimetics
- •15.2.5.3 Backbone Modification of Peptides
- •15.2.5.4 Side-Chain Modification of Peptides
- •15.2.5.5 Peptide Cyclization
- •15.2.5.6 Peptide Mimicking of α-Helices and Stabilization
- •15.2.5.7 Peptide Mimicking of β-Strands and β-Sheets
- •15.2.5.8 Peptide Production by Recombinant Technology
- •15.2.5.9 Peptides Modification by Genetic Code Expansion
- •15.2.5.10 PEGylation of Peptides and Proteins
- •15.3 New Technologies for Peptide-Based Drug Discovery
- •15.3.1 Phage Display
- •15.3.2 mRNA Display
- •15.3.3 DNA-Encoded Libraries
- •15.3.4 Cell-Penetrating Peptides
- •15.3.5 Macrocyclic Peptides
- •15.4 Computational Approaches in Peptide Drug Discovery
- •15.5 Conclusion
- •References
- •16. Rational Design of Drugs for Neurodegenerative Disorders
- •16.1 Introduction
- •16.2 Common Mechanism of Neurodegeneration
- •16.3 Brief Overview of Computational Methods in Drug Design
- •16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder
- •16.4.1 Epidemiology of Parkinson’s Disease
- •16.4.2 Pathogenesis of PD
- •1) Accumulation of Lewy bodies in substantia nigra
- •2) Mitochondrial dysfunction
- •3) Genetic factors
- •4) Neuroinflammation
- •5) Impaired protein handling
- •6) Oxidative stress
- •7) Environmental toxins
- •16.4.3 Signaling Pathway of Parkinson’s Disease
- •1) DA signaling
- •2) MAPK/ERK pathway
- •3) PI3K/Akt/mTOR pathway
- •4) Wnt/β-catenin pathway
- •5) NF-κB (nuclear factor-κB) pathway
- •6) Autophagy-lysosomal pathway
- •7) JNK (c-Jun N-terminal kinase) pathway
- •8) AMPK (AMP-activated protein kinase) pathway
- •9) Nrf2 (nuclear factor erythroid 2-related factor 2) pathway
- •16.4.4 Enzymatic Targets in Parkinson’s Disease
- •1) MAO-B (monoamine oxidase B)
- •2) COMT (catechol-O-methyltransferase)
- •3) LRRK2
- •4) GCase (glucocerebrosidase)
- •5) PARP-1 [poly(ADP-ribose) polymerase-1]
- •6) PINK1
- •7) DJ-1 (Parkinson protein 7)
- •8) Nrf2
- •16.4.5 Current Therapeutic Approaches to Treat PD
- •1) Drugs to treat motor symptoms of PD
- •2) Drugs to treat non-motor symptoms of PD
- •3) Disease-modifying therapies to treat PD
- •16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease
- •1) Symptomatic relief only
- •2) Motor fluctuations and dyskinesias
- •3) Limited efficacy in nonmotor symptoms
- •4) Disease progression
- •5) Side effects
- •6) Limited treatment options for advanced PD
- •7) Individual variability
- •16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics
- •16.4.8 Significance of Computational Approaches in Parkinson’s Disease
- •16.4.9 Use of Computational Tools in Identifying Biomarkers
- •16.4.10 Neuroprotective Strategies Through Computational Insights
- •16.4.10.1 Computational Models for Neuroprotection
- •1) Target identification and validation
- •2) Drug repurposing
- •3) Alpha-synuclein aggregation inhibitors
- •4) Deep learning in biomarker discovery
- •5) Personalized medicine
- •6) Drug-induced neuroprotection
- •7) Optimizing clinical trials
- •1) ML and AI-based diagnostics
- •2) Wearable technology integration
- •3) Multimodal data fusion
- •4) Predictive modeling of disease progression
- •5) Network analysis of brain connectivity
- •6) Personalized treatment optimization
- •7) Data sharing and collaboration platforms
- •16.5 Conclusion
- •References
- •17. Rational Design of Anti-inflammatory Therapeutics
- •17.1 Introduction
- •17.2 Navigating Inflammation and its Microenvironment
- •17.2.1 Inflammatory Cell Infiltration and Vascular Permeability
- •17.2.2 Acidosis
- •17.2.3 Increased Oxidative Stress in Tissues
- •17.3 The Demand for Advanced Anti-inflammatory Medications
- •17.5 Rational Design of Anti-inflammatory Agents
- •17.5.2 New Anti-inflammatory Agent with Indoyl-imidazole Hybrids
- •17.5.3 Rational Design of Novel Aminopiperidinyl Amide
- •17.5.4 Lipid Nanoparticles (LNPs) as Anti-inflammatory Agents
- •17.6 Conclusion and Future Perspectives
- •Authors’ Contribution
- •References
- •18.1 Introduction
- •18.2 Treatment
- •18.3 Antibacterial Resistance
- •18.3.1 Mutation
- •18.3.2 Horizontal Gene Transfer (HGT)
- •18.3.3 Enzymatic Modification or Degradation
- •18.3.4 Target Site Modification
- •18.3.5 Decreased Permeability
- •18.3.6 Efflux Pumps
- •18.3.7 Plasmids
- •18.3.8 Transposons
- •18.3.9 Gene Amplification
- •18.3.10 Formation of Biofilms
- •18.3.11 Modified Metabolic Pathways
- •18.3.12 Adaptive Evolution
- •18.4.1 Structure- Based Drug Design
- •18.4.2 Modification of Existing Antibiotics
- •18.4.3 Bioisosterism
- •18.4.4 Prodrug Strategies
- •18.4.5 Similar Bacterial Components Target
- •18.4.6 Combine or Combination Therapy
- •18.4.7 Drug Repurposing
- •18.4.8 Resistant Mechanism Blocking
- •18.4.9 Improving Drug Delivery by Nanotechnology
- •18.4.10 Phage Intervention
- •18.4.11 Host Targeting
- •18.4.12 CRISPR-Cas Technique
- •18.4.13 Peptides as Antibacterials
- •18.4.14 Immunizations and Immunotherapy
- •18.4.15 Natural Product Derivatives
- •18.4.16 Fragment- Based Drug Discovery (FBDD)
- •18.4.17 Metabolomics and Genetics
- •18.4.18 Cheminformatics
- •18.5 Summary and Conclusion
- •References
- •19. Rational Design of Antiviral Therapeutics
- •19.1 Introduction to Antiviral Therapeutics
- •19.1.1 Overview
- •19.1.2 Blueprints for Antiviral Drug Interventions
- •19.1.2.1 Protein Folding and Binding Sites
- •19.1.2.2 Conformational Changes
- •19.1.2.3 Protein–Protein Interactions (PPIs)
- •19.1.2.4 Capsid and Envelope Structures
- •19.1.2.5 Structural Vulnerabilities
- •19.1.2.6 Enzymatic Activities
- •19.1.2.7 Viral Attachment
- •19.1.2.8 Viral Assembly and Replication Machinery
- •19.1.2.9 The Host’s Immune Response
- •19.2 Targets for Antiviral Therapeutics and Inhibition Strategies
- •19.2.1 Enzyme Inhibitors
- •19.2.2 Antiviral Peptides
- •19.2.3 Antiviral Antibodies
- •19.2.4 Lipid-Mimicking Compounds
- •19.2.5 Vaccines
- •19.2.6 Immunomodulation
- •19.3 Rational Strategies for Antiviral Therapeutics
- •19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship)
- •19.3.2 AI and ML
- •19.3.3 Systems Biology and Network Pharmacology
- •19.3.4 CRISPR Systems
- •19.3.5 Nanotechnology-Based Design and Delivery Systems
- •19.3.6 Reverse Vaccinology
- •19.4 Conclusion
- •References
- •20. Rational Design of Anticancer Therapeutics
- •20.1 Introduction
- •20.2 Rational Design of Nanomedicine for Cancer Treatment
- •20.4.1 Particle Size
- •20.4.2 Shape
- •20.4.3 Surface Modification
- •20.6 Artificial Intelligence’s Progress in Anticancer Drug Development
- •20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence
- •20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design
- •20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs
- •20.7 Conclusion
- •References
- •21. PROTAC and ProTide Strategies in Drug Design
- •21.1 Introduction
- •21.2 Drug Design: Past to Present
- •21.3 PROTAC Strategy in Drug Design
- •21.3.1 Ubiquitin Proteasome System and PROTACs
- •21.3.2 Chemical Formulations of PROTACs
- •21.3.3 Advent of PROTACs as Antiviral
- •21.3.4 NS3/4A-Targeting PROTACs Against HCV
- •21.3.4.1 Neuraminidase-Targeting PROTACs
- •21.4 Emergence of ProTide Technology in Drug Design
- •21.5 Approaches of ProTides in Drug Development
- •21.6 Implementation of ProTides as Nucleoside Analogs
- •21.6.1 Antiviral Applications of ProTides
- •21.7 Conclusion
- •References

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21.1 Introduction
The origin of the word “drug” was from the French word “drogue,” which means dry herb [1].
Drug is an external molecule that has specific effects on biological systems and is applied to
diagnose, prevent, or cure a disease. The origin of a drug can be natural or can be produced
synthetically. An “ideal” drug must follow some specific features like particular action, should be
nontoxic, without or may have minimal side effects, stable, and soluble in water as well as in lipids.
Drugs interact with specific targets in the human body to exert its effect and these interactions
create two different kinds of effects, i.e., the impact of the drug on the human body and the
impact of the human body on the drug, which are determined as pharmacodynamics and pharma-
cokinetics, respectively. The physicochemical properties of drug molecules influence the kinetics
and dynamics of the body [2]. The discovery and development of drugs are time consuming and
costly processes. To accelerate drug discovery, design, development, and optimization, there is an
increasing endeavor to apply computational resources to the integrated chemical and biological
domain. The ligand-based drug design, structure-based drug design (drug–target docking), and
quantitative structure–activity and quantitative structure–property relationships are some exam-
ples of frequently used computational approaches. Although there are a few drawbacks related
to computer-aided drug design, these are time-taking process of knowing how to run software,
high production and purchase costs for software, and the work can be lost because of the sudden
breakdown of computers [3].
Due to obtained drug resistance, inaccessible targets for drugs, and low selectivity within target
family members, the discovery of potent small-molecule drugs for specific therapies comes with
numerous significant obstacles. Novel small-molecule alternatives have come to light and acquired
clinical importance [4].
Proteolysis targeting chimeras, or PROTACs, is a novel technique that has appeared as a standout
treatment paradigm in recent years. Recently, focusing on the enzymatic activities of 20–25% approxi-
mately of every protein target have been studied. PROTACs inhibit the entire biological function of
specific target proteins by binding to them and generating following proteasomal degradation, unlike
small molecules. Besides all the benefits, the notable limitations of PROTACs are transcription factors,
nuclear proteins, and many other scaffolding proteins, which are difficult to maneuver along with
21
PROTAC and ProTide Strategies in Drug Design
Maitreyee Mukherjee

21 PROTAC and ProTide Strategies in Drug Design458
conventional small-molecule inhibitors. Recently, diverse proteins, such as BTK, BRD4, AR, STAT3,
IRAK4, and tau, are degraded by PROTAC [5]. ARV-110 and ARV-471 proteins showed outstanding
efficacy in the clinical II study. It is unidentified, which targets are preferable for PROTAC technology
to achieve benefits compared to small-molecule inhibitors and for researchers, it is more challenging
to rationally develop the design of effective PROTACs and optimize it for oral effectiveness [6].
ProTide is an approach in which an active chemical entity is masked with the help of a cleavable
group (promoiety) that, in the human body, breaks off under certain conditions, exposing the
active species [7]. ProTide is a phosphate or phosphonate prodrug technology designed to deliver
nucleoside analog monophosphates and monophosphonates efficiently into the cells. In this
method, the hydroxyls of the monophosphate or monophosphonate groups are masked by an aro-
matic group and an amino acid ester moiety. These are then enzymatically cleaved off inside the
cells to liberate free nucleoside monophosphate and monophosphonate entities. From a perspec-
tive of structure, this is the culmination of nearly 30 years of medicinal chemistry research. It
began with simple alkyl groups masking nucleoside monophosphate and monophosphonate
groups, and it developed into the complex ProTide system that is used today. Two FDA-approved
ProTides (antiviral) have already been discovered, thanks to the widespread use of this method in
research. The invention of nucleoside-based ProTides for Parkinson’s disease was made possible by
this method [8]. Furthermore, as a prodrug strategy for the intracellular delivery of monophospho-
rylated non-nucleoside compounds like glucosamine, sphingosine 1-phosphate (S1P) [9],
4-phospho--erythronohydroxamic acid, and 5-phospho erythronohydroxamic acid, the ProTide
approach is gaining more interest [9]. In this chapter we discussed drug design strategies, the
ubiquitin–proteasome system, chemical formulations of PROTAC, ProTide technology for design-
ing drugs, implementation of ProTides as nucleoside analogs, and applications of PROTACs and
ProTide [10].
21.2 Drug Design: Past to Present
Drug design has a rich history in the pharmaceutical field. At the end of the 19th century, scientist
Emil Fischer proposed the significance of drug–receptor interaction that is similar to the interac-
tion between key and lock, an incredible progress has been made in the area of drug design. Drug
design, now the most sophisticated method for drug discovery, has grown into a rational and well-
planned discipline based on strong theoretical aspects and practical implications [2, 11]. In drug
discovery, the generation of novel drugs with potential interactions with specific targets plays an
important role. Discovery of a drug and development (identification of lead molecule to an FDA-
approved marketed drug) is an immensely challenging, cost-effective, and prolonged process [12].
A new as well as advanced approach was introduced to overcome these limitations, which is
referred to as computer-aided drug design (CADD). The rational drug design is cost-effective and
time-saving compared to the conventional drug discovery process. The rational drug design tech-
nique is also recognized as reverse pharmacology as the first step is to detect promising target
proteins and these identified target proteins are further involved in the screening of small-molecule
libraries [13]. These computational methods are also relevant in cutting down the excessive use of
animal models in pharmacological research [14].
CADD, a part of modern drug discovery, plays a crucial role in the selection of drugs with accept-
able bioactivity and side-effect profiles. This is applicable in both designing novel drugs and replac-
ing existing ones. CADD research has led to promising results in the treatment of inflammation
and pain related to musculoskeletal conditions [15].

459
Significant breakthroughs have been achieved in structural and molecular biology, as well as
biomolecular spectroscopic structure identification techniques and the 3D structure of more than
100,000 proteins provided by these methods. In addition to storing and organizing such types of
information, a lot of discussion are there regarding the development of advanced and robust com-
putational techniques. The rate of drug development is enhanced due to the advancement of bio-
informatics. The availability of a large number of target proteins that are responsible for the
completion of the Human Genome is the foundation for structure-based drug design (SBDD). This
is an important aspect of industry drug development programs and academic research [16]. SBDD
is a more precise, effective, and rapid method for lead identification and optimization since it
incorporates the 3D structure of a target protein as well as molecular information about the ill-
ness [17]. The most prevalent computational approaches used in SBDD encompass structure-based
virtual screening (SBVS), molecular docking, and molecular dynamics (MD) simulations. These
approaches have several uses, including the analysis of binding energetics, interaction between
ligand–protein, and evaluation of conformational modifications that occur during the docking
process [18]. In recent years, the software industry has experienced a significant increase in appli-
cations for effective drug discovery procedures.
Computational tools are an effective technology for speeding up the process of drug discovery,
which comprises numerous screening processes, combinatorial chemistry, and calculations of
properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) [13]. SBDD
is an iterative process that goes through multiple steps to get an improved drug to clinical trials.
The identification of a possible therapeutic target as well as active ligands is the first phase of
activity. The initial step is to clone the target gene, which is then extracted, and purified, and the
protein’s 3D structure determined. Many computational methods can be utilized to dock large
databases of small molecules or fractions of compounds into the binding site of the target
protein [13].
21.3 PROTAC Strategy in Drug Design
Numerous diseases are the target of proteins. As a result, different types of strategies for blocking
target proteins associated with disease have been developed [19]. The protein homeostasis is regu-
lated by the ubiquitin–proteasome system with the help of proteasome as the main element of
eukaryotic protein degradation. To mark proteins for proteasomal breakdown, ubiquitin covalently
binds to surface lysines [18]. An abnormal protein functioning is the main cause of inherited as
well as acquired diseases, which is now targeted by applying an occupancy-based pharmaceutical
approach, where the disease-implicated proteins are the target for binding of the inhibitors and
inhibitors can block the longer protein functions and achieve a higher clinical benefit. To achieve
optimal treatment efficacy, high local inhibitory concentrations (IC90–95) must be maintained
regularly. But frequently, this causes off-target binding to follow the side effects. Event-driven tech-
niques offer an alternative, in which drug binding enhances an action that decreases the cellular
levels of the protein implicated in the disease. There are several nucleotide-based approaches with
low in vivo stability and low bioavailability, such as genome editing strategies like CRISPR-Cas9,
antisense oligonucleotides, and small interfering RNA (siRNA). However, several proteins for spe-
cific targets can be selectively degraded by small molecules. This technique is named as PROTACs.
Ligases such as cereblon (CRBN), Von Hippel–Lindau (VHL), IAP, and MDM2 have been the focus
of extensive studies on PROTACs across a wide range of disease domains. PROTACs have been
found effective for attacking cancer targets such as the androgen receptor, estrogen receptor, BTK,

21 PROTAC and ProTide Strategies in Drug Design460
CDK8, BCL2, and c-MET [6e11]. Multiple ligases are used to construct several types of BET family
like BRD2, BRD3, and BRD4-PROTACs. The membrane permeability of dBET6, and dBET57
PROTAC are enhanced by MDM2-based BRD4 PROTAC [20], CRBN-based dBET1 [21], and
BETd-24-6 for triple-negative breast cancer [22]. PROTACs have been tested for viral, immunologi-
cal, and neurodegenerative diseases, respectively, for the protease of the hepatitis C virus (HCV),
IRAK4, and Tau [17e19] [23].
A protein of interest (POI) ligand, E3 ligase ligand, and linker are the components of a PROTAC,
a kind of heterobifunctional molecule that can be developed and synthesized to meet a wide range
of requirements. The availability in cells for POI with a high degree of selectivity is decreased by
the new mechanism of PROTACs which may lower the side effects in comparison to conventional
small-molecular inhibitors [24]. The first PROTAC, i.e., PROTAC-1 was discovered in 2001, since
then the target protein degradation (TPD), a novel approach and innovative suggestion for thera-
peutics is growing rapidly. TPD offers the modern explanation of the concepts of classical drug
discovery and is driven by target activity that is event-based as compared to activity that is
occupancy-driven. The ovalicin and a 10-amino acid phosphopeptide from nuclear factor-κB
inhibitor-α (NF-κBIα; also known as IκBα), which was targeted by Protac-1, has been identified by
the E3 ligase β-transducing repeat-containing E3 ubiquitin–protein ligase (β-TRCP) [24]. The
methionyl aminopeptidase 2 (METAP2) was targeted by a newly developed Protac-1. In between
METAP2 and β-TRCP, the ligase facilitated by Protac-1 to ubiquitylate METAP2 in extracts from
unfertilized eggs of Xenopus laevis. Additionally, with the emergence of these early PROTAC com-
pounds, the immunomodulatory imide drugs (IMiDs) thalidomide and its analogs lenalidomide
and pomalidomide [6] were discovered to target the E3 ligase cereblon (CRBN) in the field of can-
cer therapy [25]. For targeting the IKAROS family zinc finger 1 (IKZF1) and IKZF3 for degrada-
tion [3, 4, 23], these drugs cooperate CRBN and are presently regarded as breakthrough examples
of molecular glue [24]. The small-molecule mimetics of the HIF1α peptide were introduced by the
rational design of PROTACs. Systematic detection of degrader compounds, which are termed as
molecular glues, can be utilized in conjunction with the modular proteolysis-targeting chimera
(PROTAC) technique to promote rational degrader discovery. Molecular glues mediate protein–
protein interfaces to promote the formation of a complex between an E3 ligase and a target. There
are many examples of intramolecular and intermolecular glues which work outside the ubiquitin–
proteasome system (UPS), like the allosteric protein tyrosine phosphatase nonreceptor type 11
(PTPN11; another name SHP2) inhibitor SHP099 [26], which maintains a closed conformation of
SHP2 (intramolecular); and cyclosporin, which causes for the proximity of calcineurin and
cyclophilin [24].
Besides a biological chemistry tool, PROTAC is a widely used technology for the drug design of
new therapies. The E3 ligase binding peptide ALAPYIP to recruit von Hippel–Lindau (VHL) is
carried by PROTAC, which can lead to the degradation of FK506 binding protein 12 (FKBP12)
and AR [6].
As the early PROTACs are not complete small-molecule structures but also include peptide
ligands for the E3 ligase, these are regarded as “bioPROTACs.” The rational design of PROTACs
was introduced by the small-molecule mimetics of HIF1α peptide, e.g., the first PROTACs, which
utilized VHL to degrade bromodomain-containing protein 4 (BRD4), were based on the bromodo-
main protein inhibitor JQ1. One popular biochemical modification involves attaching one or more
ubiquitins through covalent bonds to one or more acceptor lysines located on the substrate protein.
This results in the degradation of the ubiquitinated protein by the proteasome. The UPS breaks
down about 85% of the protein in the body [27].

461
21.3.1 Ubiquitin Proteasome System and PROTACs
The ubiquitin-activating enzymes (E1), ubiquitin-conjugating enzymes (E2), and ubiquitin–
protein ligases (E3) have the sequential action to enhance the binding of ubiquitin to substrate
proteins and lead to ubiquitination. The modification of protein is introduced to the proteasome
for degradation which is caused by K48 polyubiquitination. The E3 ubiquitin–protein ligases bind
directly to the substrate and proceed to ubiquitination. Craig M. Crews and Raymond J. Deshaies
came up with the idea of seizing the ubiquitin-dependent proteolysis pathway, which might be an
effective way to modulate the protein abundance of normal diseased cells as the degradation of
ubiquitinated proteins occurs rapidly in cells [17]. They established a method for targeting proteins
to the ubiquitin/proteasome pathway (Figure 21.1). The development of small molecules that
modulate the interactions has specific medicinal potential as protein–protein interactions (PPIs)
ruling substrate identification by E3 ubiquitin ligases are crucial for cellular function. Occupancy-
driven pharmacology is primarily utilized in therapy, while PROTAC technology relies on the prin-
ciple of event-driven pharmacology. The idea of bringing together two special proteins which
generally do not generate a complex, has a role in the development of the compounds created by
the PROTAC [28, 29]. The protein of interest (POI) ligand first binds to the pathogenic protein;
following that, the E3 ligand attached to the other terminus of the linker recruits an appropriate
E3 ligase to the nearby location of POI, which causes the target protein to become polyubiquit-
inated. Ultimately, the 26S proteasome breaks down the recognized polyubiquitinated protein. A
study report that first peptide-based PROTACs have the potency to degrade methionine amin-
opeptidase 2 (MetAP-2). The effective approach of PROTACs is enabled in various areas of drug
POI ligend
E3 ligend
NH2
PROTAC
POI
Ub
E2
E3
Ub
Ub
Ub
Ub
POI
Polyubiquitination
Recycle
E1
ATP
E1
Ub
Ub
POI degradation ProteasomeTransfer of ubiquitin
Figure 21.1 A systematic diagram stating the mechanism of action of PROTACs.

21 PROTAC and ProTide Strategies in Drug Design462
development by the mechanism of action of complete target degradation and corresponding
PROTAC compounds are targeted more than 130 proteins, which are associated with leukemia,
cancer, cardiovascular disease, virus infection (e.g., hepatitis C virus (HCV) NS3/4A PROTAC
DGY-08-097) and neurodegenerative disease for degradation. Several PROTACs for the treatment
of malignant tumors have been created as candidates for clinical trials in the development of anti-
tumor drugs; these consist of ARV-110 [2], an androgen receptor-targeted PROTAC for prostate
cancer, and ARV-471 [3], an estrogen receptor-targeted PROTAC for breast cancer, both developed
by Arvinas; these are currently in phase II clinical trials; furthermore, DT2216 [4], a BCL-XL
degrader for hematomas and solid tumors, produced by Dialectic Therapeutics, is now in phase I
clinical trial.
The rational design of PROTACs was introduced by the small-molecule mimetics of HIF1α pep-
tide. In the year 2001, the Deshaies laboratory developed and produced Protac-1, a functional mol-
ecule that recruits the UPS to maintain intracellular protein homeostasis. This process helps in the
degradation of methionine aminopeptidase-2 (MetAP-2). Three covalently linked parts make up
Protac-1 as a domain with ovalicin (MetAP-2 inhibitor), which has been identified by the Skp1-
Cullin-F-box complex (SCF, an E3 ligase that starts protein ubiquitination and degradation by
UPS), and a linker that connects these two domains [30].
21.3.2 Chemical Formulations of PROTACs
By mediating PROTACs passive diffusion, active transport processes, or incorporation into nan-
odelivery vehicles to overcome the obstacles for reaching the target site to gain the wanted activity,
researchers have adopted various approaches that improve the cellular uptake and solubility of
PROTACs. The conformational flexibility in the PROTACs structure by approving the creation of
intramolecular H-bonds (IMHB) for the formation of a more polar and soluble form yielding their
increased permeability was introduced by Alex et al. Further the concept of click chemistry for the
production of bifunctional PROTACs with better permeability that described simultaneous degra-
dation of two key oncogenic targets was applied by Lebraud et al. The specific peptides (p-PROTAC)
are the basic component for the development of PROTACs to achieve specific as well as effective
deterioration of undruggable target proteins [31]. Several different pathways in cancer progression
and neurodegenerative disorders and cancer progression have been targeted by applying this tech-
nology including PI3K, ERa, CREPT, AKT, X-protein, and Tau-protein for the treatment of differ-
ent types of cancers like breast cancer, ovarian cancer, pancreatic cancer, and neuroblastoma.
Although this process still has drawbacks as it possesses low stability and poor membrane perme-
ability barrier tough to reach the specific target tissue. A trastuzumab–PROTAC conjugate (Ab-
PROTAC 3) was introduced by Maneiro and colleagues which has target degradation potency only
in HER2-positive breast cancer cell lines, without showing any effect on HER2-negative cells [31].
A number of PROTAC–antibody conjugates comprising two specific Era targeting degrader enti-
ties along with three separate ADC linker modalities for targeted delivery of chimeric ERa degrader
molecules developed by Dragovich et al. They showed that integrated degrader payloads may be
delivered to MCF7-neo/HER2 in a target–antigen-dependent manner. On the other hand, creating
antibody–PROTAC combines for targeted PROTAC delivery may be an expensive and time-
consuming process. Nanotechnology is possibly used to avoid these hurdles along with achieving
particular accumulation of PROTACs in tumor target sites and nonspecific deterioration of target
proteins [32].
There are several new concepts have emerged regarding PROTAC, such as HaloPROTACs,
HomoPROTACs, autophagy-targeted chimera (AUTAC), lysosome-targeting chimera (LYTAC),
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