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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

463
and autophagosome-tethering compound (ATTEC) [33]. HaloPROTACs is a novel PROTAC ligand
produced by exchanging POI ligands with chloroalkanes. The chloroalkane-labeled moieties can
covalently bind to HaloTag protein. If HaloTag is binded to the specific protein, indirectly the
PROTAC molecules bound to them enhance their degradation process. Recently, HaloPROTACs
have been used by a few researchers for the degradation purpose of different types of HaloTag
fusion proteins. The POI ligand in PROTAC along with E3 ligase ligand is replaced by a few groups
that are similar to dimerize E3 ligase ligand. The ternary compounds are commonly referred to as
homo-PROTACs, and here, the E3 ligase enzymes transform into their substrates and are suscepti-
ble to UPS degradation [34]. Apart from the ubiquitin–proteasome pathway, there exists an addi-
tional protein degradation mechanism named the lysosomal degradation pathway, which
comprises the process of autophagy and the endosome/lysosome pathway. Another approach that
researchers have found to counteract the off-target impacts of nanocarriers and encapsulated
chemotherapeutics is the active targeting of PROTAC molecules via simulation of nanoformula-
tions targeted to overexpressed receptors on the surface of cancer cells. The incorporated MZ1
PROTAC into PLA-based polymeric nanoparticles (ACNPs), which have received FDA approval
and were conjugated with trastuzumab to target HER2 receptor-overexpressing breast cancer cells
and improve both the antitumor efficaciousness and pharmacokinetic profile. At low dosages,
these ACNPs had a potential cytotoxic effect that guided the NPs toward cancer cells that expressed
HER2, without causing any additional adverse effects. To target liver tumors specifically, research-
ers developed asialoglycoprotein receptor (ASGPR)-directed nanoliposomes (GALARV) which
comprised a BRD4-PROTAC: ARV-825. The aqueous solubility increased by 140-fold when
ARV-825 encapsulated in GALARV and prevention of its microsomal metabolism was observed to
improve its in vitro half-life by 100-fold compared with only ARV-825 alone. For the purpose of
treating nonsmall-cell lung cancer, Vartak et al. developed nanostructured lipid carriers (AP-
NLCs) that integrate ARV-825 and greatly increased its solubility [19]. Polymeric PROTAC (POLY-
PROTAC) nanomedicine was recently developed by Gao et al. to accomplish tumor-specific
breakdown of BRD4 proteins. In order to enhance intratumoral accumulation and retention of
azide-modified POLY-PROTAC nanoparticles (NPs) through an in situ bioorthogonal click reac-
tion, these POLY-PROTACs are loaded with dibenzocyclooctyne (DBCO). In response to an acidic
pH, a reductive tumor microenvironment and extracellular matrix metalloproteinase-2, POLY-
PROTACs assemble themselves to create polymeric micellar NPs, which then emit the PROTAC
payload. Additionally, acid-activatable bioorthogonal POLY-PROTAC NPs worked in conjunction
with photodynamic treatment to promote tumor-specific BRD4 decomposition, which signifi-
cantly regressed the growth of tumors and increased survival by 40% in a mouse model of MDA-
MB-231 breast cancer [35]. A significant obstacle in the delivery of a large anticancer molecule
such as PROTACs to tumor cells is getting through the dense and fibrotic tumor stroma to the
target tumor cells. In the past, scientists have boosted the delivery of medications, macromole-
cules, and nanomedicine to tumor cells by using stromal-disrupting substances. For vemurafenib-
resistant melanoma, Fu et al. recommended the use of nintedanib as an antifibrotic drug in a
liposomal nanocarrier (ARNIPL) alongside ARV-825. In order to explore the antiangiogenic and
antivasculogenic mimicry impact of palmitoyl--carnitine chloride, a protein kinase C inhibitor,
in combination with ARV-825, co-loaded in PEGylated nanoliposomes (LARPC), by a group of
researchers mixed the two substances [27, 35]. For the delivery of PROTAC through two main
routes (i.e., parenteral and oral) formulation and route of administration are the main focus.
Without a doubt, injectables offer the fastest route for drug molecules across physiological barriers,
particularly the GIT environment, improving the concentration of drugs in blood circulation. The
parenteral route: intravenous or intraperitoneal – is still used for most of the significant in vivo

21 PROTAC and ProTide Strategies in Drug Design464
studies on PROTACs that have been published in peer-reviewed literature [36]. Parenteral admin-
istration of PROTACs is often achieved by solubilizing these molecules in an appropriate solvent
or surfactant, yet this method may have disadvantages of its own [37]. As a result, oral PROTAC
administration would boost the attraction of the novel approaches. Also, in the majority of chronic
illnesses, both doctors and patients always favor the oral route [38]. The proportion of active escap-
ing intestinal and/or hepatic metabolism, as well as the fraction absorbed where PROTACs have
difficulties at each of these parameters, are closely associated with oral bioavailability [39]. Lipid-
based drug delivery systems provide attractive platforms for delivering complicated compounds
where parenteral delivery is the goal. Chen and his colleagues also published a study showing the
creation of LNPs for parenteral delivery of pre-fused PROTACs, which exhibited better therapeutic
efficacy [19].
21.3.3 Advent of PROTACs as Antiviral
Hepatitis B viral defense using peptide-based PROTACs, hepatitis B virus (HBV) X-protein (154
amino acids, 17 kDa) is necessary for virus infection [36]. It has a role in the development of cir-
rhosis caused by HBV or even hepatocellular carcinoma (HCC) in patients who suffer from persis-
tent HBV infection. In 2014, Montrose et al. gave the first report on the use of PROTAC technology
to reduce the amount of protein associated with a virus [40]. They also demonstrated how the HBV
X-protein was neutralized and eliminated by a new cell-permeable PROTAC [41]. The N-terminal
oligomerization motif of the X-protein was attached to the C-terminal instability domains to form
the PROTAC, which was then made cell-permeable by N-terminal fusing to a polyarginine cell-
penetrating peptide. In a study, the X-protein and the oligomerization motif linked together, and
the unstable portion of the X-protein turned it into a target that was misfolded or damaged and
could be broken down by the UPS. Also, it has been confirmed that the PROTAC’s oligomerization
domain opposes the X-protein’s proapoptotic activity. The applicability of this peptide-based
PROTAC in the field of anti-HBV therapy is limited since it has not been further studied for the
reduction of HBV replication and the progression of chronic hepatitis [36, 42].
21.3.4 NS3/4A-Targeting PROTACs Against HCV
As a result of its protracted latent phase, the positive single-strand RNA virus known as HCV can
cause several progressive infection symptoms, including cirrhosis, liver fibrosis, and even cancer. As
a result, the virus poses a risk to public health. There are 10 structural and nonstructural proteins
produced by the proteolytic breakdown of an HCV polyprotein [43]. The NS3/4A complex protein
is a multifunctional enzyme that has a C-terminal helicase region and an N-terminal serine protease
domain among the viral proteins implicated in HCV replication. Numerous essential mature non-
structural proteins (NSPs), including RNA-dependent RNA polymerases (RdRp), helicases/nucleotide
triphosphatases, and serine proteases, are produced by this enzyme through the cleavage of HCV
polyprotein precursors. Additionally, the NS3/4A protease cleaves host proteins implicated in the
beginning of the cellular immune response, such as TRIF and MAVS [42]. The only protease that
has been effectively employed as a DTA target at this point is NS3/4A. Yang et al. demonstrated in
2019 that small-molecule PROTACs against HCV promoted NS3/4A degradation in vitro. This was
the first instance of combining PROTAC technology as a ligand of a POI with an antiviral small-
molecule inhibitor. The pyrazine group of the HCV NS3/4A-Telaprevir co-crystal structural com-
plex (PDB: 3SV6) proved to be a solvent-exposed fragment in this research, which allowed it to
function as a tethering site [44]. A range of heterobifunctional compounds were created and

21.4 Emergence of ProTide Technology in Drug Design 465
produced by conjugating telaprevir to various CRBN ligands through the usage of alkyl or PEG
polymers. This was done to verify if the PROTACs technology may be utilized as a viable and inno-
vative virus-fighting tactic. These conjugators effectively engaged the CRL4CRBN complex in addi-
tion to retaining the ability to inhibit the NS3/4A protease with IC50 values ranging from 247 to
385 nM. Through a concentration-dependent way, these bifunctional degraders markedly stimu-
lated rapid and sustained proteasome-mediated NS3 protein degradation in a cultured cell line.
With a DC50 value of 50 nM, compound DGY-08-097 demonstrated the highest efficacy in inducing
NS3 degradation via the UPS pathway. However, a decrease in the neo-substrate abundance of
IMiDs (IKZF1 and IKZF3) has not been observed, likely due to the application of a novel tricyclic
imide molecule as the highly particular CRBN ligand. The compound DGY-08-097 showed inhibi-
tion activity against the NS3-V55A and NS3-A156S protease mutants, with IC50 values of 508 and
1561 nM, respectively, which was three times higher than that of wild-type NS3. This is consistent
with several PROTACs that can overcome mutational variation in cancers [44].
21.3.4.1 Neuraminidase-Targeting PROTACs
A hydrophobic, single-stranded mosaic of 29 amino acids encases influenza neuraminidase (NA),
a tetramer with mushroom-like structure, is a membrane glycoprotein that is present inside the
envelope of the influenza virus. By interfering with the interaction between the glycoprotein HA
on the surface of virion and the sialic acid receptor on the host cell membrane, NA releases prog-
eny virions from the surface of infected cells during influenza virus replication. This enables the
virus to infect additional host cells and multiply faster [45]. However, numerous investigations
have been conducted to find the protein degraders for HCV or SARS-CoV-2, and this has been
motivated by the severe acquired drug resistance of clinical IAV NA inhibitors. Oseltamivir’s
cocrystal structure complex (PDB: 2HU0) with NA showed that the amino and carboxylic acid
groups were suitable locations for additional modification [38]. This is compatible with the find-
ings of earlier medicinal chemistry investigations intended to increase the binding affinity among
NA and oseltamivir through SAR [42].
21.4 Emergence of ProTide Technology in Drug Design
Since its formal introduction in 1958, the applicability of a prodrug approach to drug discovery has
been introduced. Analogs of nucleosides have several disadvantages, including inadequate absorp-
tion by cells because membrane transporters are not expressed enough, immediate breakdown, and
a delayed conversion to triphosphate because of the initial phosphorylation step that limits the rate
of conversion [7]. Prodrugs of NA with a phosphate group or an isosteric and isoelectronic phospho-
nate moiety have been explored to get around these limitations. A few of these methods have proven
effective, such as prodrugs that are now being used as antiviral treatments in clinics. ProTide (Pro
Nucleotide) is a phosphoramidate technique developed over 20 years ago by Cardiff University’s
Professor Chris McGuigan and his colleagues [7]. When nucleoside analogs were initially developed
several decades ago, they proved to be an efficient treatment for a wide range of illnesses, including
cancer and viral infections such as herpes simplex virus (HSV), human cytomegalovirus (HCMV),
HIV, hepatitis B virus (HBV), more recently, hepatitis C virus (HCV). At present, the clinic offers
over 20 distinct nucleoside analogs for the treatment of cancer and viral infections. These chemical
compounds either passively diffuse into the cell or are assisted by transporters like peptide and
concentrative nucleoside transporters [46]. Several nucleoside and nucleotide kinases activate the
nucleoside analogs when they are within the cell. They then phosphorylate the analogs step-by-step,

21 PROTAC and ProTide Strategies in Drug Design466
forming the mono-, di-, and triphosphorylated nucleoside analog metabolites. The therapeutic
effects of activated (phosphorylated) antiviral nucleoside analogs are achieved by inhibition of
intracellular enzymes, which are typically virus-encoded DNA or RNA polymerases, and/or incor-
poration in the viral nucleic acid chains, which ceases the elongation process. Nevertheless, there
are plenty of limitations accompanied by the administration of nucleoside analog drugs. The struc-
tural dissimilarity of nucleoside analogs to genuine nucleosides renders their phosphorylation by
cellular or viral kinases usually inefficient. This eventually restricts the synthesis of the active
triphosphate metabolites, typically nucleoside analog active metabolite. The low intestinal perme-
ability of these compounds, which prevents them from being carried over the cell border via the
paracellular route, also frequently results in low oral bioavailability. Furthermore, resistance
restricts the therapeutic application of many nucleoside analogs with antiviral and anticancer prop-
erties. Resistance arises from a variety of mechanisms, including (i) downregulating the nucleoside
kinases that activate nucleoside analogs; (ii) depleting transporters that impact the effective traffick-
ing of these analogs into target cells; (iii) activating phosphatases, or 5′-nucleotidases, which
dephosphorylate the active metabolites of nucleoside analogs; and (iv) increased catabolic biocon-
version of the nucleoside analogs, including (deoxy)cytidine deamination, which may result in
inactive versions of some of the analogs [47]. It was first proposed that some of these deficiencies
could be addressed by supplying phosphorylated metabolites of the nucleoside analog to get around
these restrictions. Numerous investigations that revealed the slow phosphorylation of nucleoside
analogs into their nucleoside monophosphates as the rate-limiting step in their in situ activation
further supported this idea [10].
The ProTides were first developed in the late 1980s. The goal was to hide the phosphate group’s
oxygen atoms in nucleoside monophosphate analogs so that, at physiological pH, they are neutral
and more readily absorbed by cells. Initially, the phosphate groups were simply hidden by alkyl
groups. However, over time, the technique developed into cleverly hidden phosphate groups using
aryl groups and amino acid esters, which can be broken down inside intact cells. Alkyl and
haloalkyl phosphate esters, alkyloxy and haloalkyloxy phosphoramidates, phosphorodiamidates,
lactyl-derived systems, diaryl phosphates, and aryloxy phosphoramidates, referred to as ProTides,
are the six phases of this development process, which lasted at least two decades [48].
21.5 Approaches of ProTides in Drug Development
Numerous nucleoside monophosphate or monophosphonate ProTides have been synthesized
using this method since in the early 1990s, the McGuigan laboratory developed the ProTide tech-
nology. While pyrimidine nucleosides like AZT (3′-azidothymidine) and d4T (2′,3′-didehydro-2′,
3′-dideoxythymidine) were the initial subjects of structure–activity relationship (SAR) investiga-
tions, a wide range of additional ProTide derivatives have also been produced and tested for
antiviral effectiveness. ProTides with phosphoramidate technology have been observed to have
significantly higher antiviral potencies when applied to purine nucleoside analogs, such as
2′,3′-dideoxyadenosine (ddA) and 2′,3′-didehydro-2′,3′-dideoxyadenosine (D4A), carbocyclic
adenosine derivatives, [49], and carbovir/abacavir [50]. Recently, it was also demonstrated that the
antiherpetic acyclovir ProTides produced antiherpetic and anti-HIV activity allowing the antiviral
range of the medication to be increased. It should be noted in this regard that ProTide technology
not only makes it possible to significantly optimize the currently accessible antiviral medications,
but it also makes it possible to transform molecules that initially appear inactive into active antivi-
rals through enhanced absorption and direct delivery of their activated (monophosphate) species.

467
Some ProTides have made it to clinical trials, and two of them have received approval for usage in
clinic. The FDA-approved ProTides and important ProTides that are reportedly undergoing clini-
cal trials will be discussed in order to demonstrate the value of this prodrug technology in drug
discovery [51].
Due to the emergence of resistance to this anticancer treatment drug, gemcitabine became the
preferred choice for the ProTide technology. The inability of gemcitabine to be converted into its
pharmacologically active diphosphate and triphosphate forms because of deoxycytidine kinase
downregulation, the deamination of gemcitabine into its very less active uridine derivative, and
mutations in the membrane transporter that mediates its active uptake into cells which is respon-
sible for a few of the mechanisms of gemcitabine resistance. The cytostatic properties of certain
gemcitabine ProTides were investigated when they were developed. These investigations produced
the lead structure, which showed remarkable antiproliferative efficacy, major in vivo stability, and
an outstanding PK profile [52]. One significant finding from the ProTide was its ability to success-
fully overcome resistance to gemcitabine. Three key explanations were offered for this: first, gem-
citabine is known to be catabolized by the enzyme cytidine deaminase, which is present in both
serum and cells. Unprotected hydroxyl groups on the 3′- and 5′-positions of the nucleoside analog
are necessary for deamination to take place. Because the phosphoramidate moiety was present on
the ribose moiety’s 5′-O-position, ProTide 1 showed resistance to deamination. Being resistant to
deaminase breakdown also improves safety since less harmful difluorodeoxyuridine is generated.
Secondly, it was discovered that the ProTide was lipophilic enough to get inside the cells by passive
diffusion and that the cellular uptake was not dependent on nucleoside transporters. An experi-
ment that involves the downregulation of nucleoside transport in cancer cells was mimicked using
dipyridamole, a known inhibitor of the human equilibrative nucleoside transporter 1 (hENT1) was
deduced by ProTide. When pancreatic PANC1 cancer cells were treated with gemcitabine in the
presence of dipyridamole, the cytotoxic activity of the drug was seen to be dramatically reduced,
and the cytostatic effect decreased from 611 nM to >2000 nM (CC50) [53]. In spite of hENT1 block-
ade, the activity stayed rather constant (CC50 = 162 μM), indicating that cellular absorption is
not dependent on nucleoside transporters. The ProTide’s structure enables it to bypass the initial
phosphorylation, which restricts the rate and is mediated by deoxycytidine kinase, which is
reduced in certain carcinomas. As the competing natural substrate for gemcitabine activation to its
5′-monophosphate by 2′-deoxycytidine kinase, 2′-deoxycytidine kinase (EC50 = 0.7 μM in the
presence of 2′-deoxycytidine compared to 0.2 μM in the absence of dCyd) when incubated with
RT112 cancer cells, indicated a minor change in its activity level 1. On the other hand, when
2′-deoxcytidine was administered concurrently with gemcitabine, its efficacy was reduced as the
two drugs were competing for the same active site on the deoxycytidine kinase [54]. ProTide 1 was
studied in vivo in animals, with a special emphasis on pancreatic tumors that were partially resist-
ant to gemcitabine. The preliminary findings were encouraging. In mice models using human
tumor xenografts, shrank the tumor more rapidly than gemcitabine, and on day seven after ProTide
delivery, the tumors shrank significantly. In order to determine the animals’ tolerance to the sub-
stance, their body weight was also noted. ProTide 1 was related to a decrease in body weight of less
than 4%, and it was determined that, in comparison to mice treated with gemcitabine, the mean
body weight loss was less noticeable in the former group for the duration of the treatment. This
suggested that the parent nucleoside analog was not as well tolerated as the gemcitabine
ProTide [52]. Compared to the pharmacokinetics of ProTide were better than gemcitabines’ as its
half-life was 7.9 hours as opposed to 1.5 hours for gemcitabine. ProTide additionally caused intra-
cellular concentrations of the active metabolite of gemcitabine triphosphate to rise to 13 times
greater levels than those of gemcitabine itself [52]. A total number of 68 patients participated in

21 PROTAC and ProTide Strategies in Drug Design468
phase I/II clinical studies, which demonstrated that 1 was beneficial for several types of cancer,
most notably ovarian, pancreatic, and biliary malignancies. Phase III trials with a specific empha-
sis on certain cancers will be carried out. The stability observed from the in vivo study of ProTide
was with a plasma half-life of 8.3 hours. The active metabolite obtained its maximum intracellular
concentrations fast, peaked at 475 μM, and remained there for 24 hours. And 33 patients had stabi-
lized their condition, while many individuals had seen a tumor reduction of more than 30% [10].
The FDA has approved two ProTide medications for application in clinical settings. The FDA
authorized sofosbuvir (Sovaldi) in December 2013 for the treatment of HCV. The product known
as sofosbuvir is a phosphoramidate derivative of the parent nucleoside 2′-deoxy-2′-fluoro-
2′-C-methyluridine. After surpassing 50 other ProTides in expansion, 35 sofosbuvir was acquired
by Gilead Sciences, where it took on the name sofosbuvir and underwent furthermore, thorough
clinical testing. It has been found that sofosbuvir acts by preventing the HCV NS5B polymerase, an
enzyme necessary for the virus’s genome replication. When this enzyme is inhibited, RNA chain
termination occurs, which eventually stops HCV from replicating [55]. Sofosbuvir is absorbed by
hepatocytes and then hydrolyzed sequentially by CES1 and cathepsin A to produce active nucleo-
side monophosphate, again hydrolysis by HINT1, the pharmacologically active triphosphate
metabolite emerged, which acts on NS5B polymerase through phosphorylations. The results of
sofosbuvir’s initial animal trials were positive. In dogs with portal vein cannulation, ProTide was
successfully absorbed after oral administration, and its bioavailability was 9.9%, or 36% of the total
percentage absorbed. Over 80% of the dose administered to human volunteers was absorbed; how-
ever, the exact bioavailability was not confirmed [53]. The sofosbuvir maximum concentration
(C
max
) was attained 0.5–2 hours after oral administration, while the C
max
of the inactive parent
nucleoside metabolite was reached 2–4 hours afterward administration, with a half-life of
0.48–0.75 hours. The absorption was fast. Ex vivo analysis of the anti-HCV agent’s distribution
demonstrated that 82% of healthy subjects and 85% of subjects with end-stage renal disease bound
plasma proteins. According to the typical in vivo degradation of the ProTides, tests involving the
diastereomeric mixture of sofosbuvir in people demonstrated that it was quickly absorbed and
degraded in the liver. The parent nucleoside monophosphate has been created by cleaving off the
P–N bond of the phosphoramidate group after the isopropyl ester motif was hydrolyzed into the
exposed carboxylate group [56]. After the 100 and 800 mg doses of the diastereomeric combination
of sofosbuvir, the parent nucleoside that is an inactive metabolite, found in the plasma, and it is
possible to detect for at least 24 hours. The main component found in the urine (77.7%) was the
inactive parent nucleoside, indicating renal excretion, while sofosbuvir showed little evidence of
renal excretion (3.5% recovered in urine; renal clearance = 0.238 L/min). Further, this was sup-
ported by research done on the pharmacokinetics of sofosbuvir in patients who had decreased
renal function, which proved that the metabolites of sofosbuvir were actively excreted by the kid-
neys. The renal clearance of the parent nucleoside was approximately two times higher than the
glomerular filtration rate. A minimal effect with minimization of any adverse effects was provided
by the combination of pegylated interferon and ribavirin with sofosbuvir in the phase II clinical
trial. Initial study results showed that the ideal sofosbuvir dosages were 200 or 400 mg in combina-
tion with pegylated interferon and ribavirin [57]. That was because the 100 mg dose was found to
be less efficient than the 200 and 400 mg doses in terms of viral load reduction. Fatigue and nausea
were the most common side effects at these dosages, while there was a low prevalence of other
side effects. In addition to sofosbuvir’s brief half-life, once-daily dosing was advised because
there was minimal drug buildup. Phase II clinical trials later verified sofosbuvir’s safety. It was
hypothesized that there might be fewer drug–drug interactions since sofosbuvir is not metabolized
by the cytochrome P450–3A4 enzymes. Additionally, precautions were recommended when

469
co-administering carbazepine and rifabutin, though, as sofosbuvir concentrations could decline
significantly because of the apparent induction of the P-glycoprotein efflux pump [58]. The high
efficacy of sofosbuvir was demonstrated in a single-group, open-label phase III trial of treatment-
naive individuals with HCV genotypes 1, 4, 5, and 6 who received sofosbuvir plus pegylated inter-
feron and ribavirin for 12 weeks, and 12 weeks after the end of HCV treatment, 80% of cirrhotic
patients and 92% of noncirrhotic patients had viral counts below the lower limit of quantifications
(SVR12). These earlier findings showed the effectiveness of sofosbuvir in treating different HCV
genotypes. A sustained viral response was obtained in 78% of treated patients, with a 93% response
rate in genotype 2 patients and a 61% response rate in genotype 3 patients, in a phase III placebo-
controlled study comparing sofosbuvir and ribavirin for 12 weeks with matched interferon ineligi-
ble regulates in patients with genotypes 2 and 3 [59]. The response rates displayed a 73% SVR12 in
the 16-week arm compared to 50% in the 12-week arm in a study that involved patients who had
previously received interferon treatment and were treated with either sofosbuvir and ribavirin for
12 weeks followed by placebo for 4 weeks or sofosbuvir and ribavirin for 16 weeks. Remarkably,
after receiving sofosbuvir and ribavirin for 12 weeks, 94% of genotype 2 patients reached SVR12,
and within 16 weeks 87% achieved it [60]. Among patients with genotype 3, 30% attained SVR12 in
12 weeks as opposed to 62% in 16 weeks. Remarkably, compared to 27% of patients in the 16-week
therapy arm, 46% of patients getting sofosbuvir and ribavirin for 12 weeks experienced a viral
relapse. The effectiveness of giving genotype 3 patients an extended 24-week course of sofosbuvir
along with ribavirin was assessed in another study [61]. The findings suggested that whereas 85%
of the 250 patients who had genotype 3, received sofosbuvir and ribavirin treatment for 24 weeks
reached SVR12, 93% of the 73 patients who had genotype 2, took part in the study achieved
SVR12 within 12 weeks of treatment. This study was the first extensive trial establishing the effec-
tiveness of long-term interferon-free therapy for patients having genotype 3. Depending on the
HCV genotype to be treated, sofosbuvir has been approved for clinical usage in combination with
pegylated interferon, ribavirin, and the NS5A inhibitors ledipasvir and velpatasvir because of its
exceptional safety profile and pan-genotypic efficiency. Each of these factors has combined to
make sofosbuvir a mainstay of HCV treatment (Table 21.1) [7].
FDA gives approval for the new ProTide-based drug, i.e., tenofovir alafenamide (TAF) for the
treatment of HIV [63]. TAF is the latest ProTide of the acyclic nucleoside phosphonate medica-
tion tenofovir, developed by Gilead Sciences Inc. Another prodrug of tenofovir is Tenofovir diso-
proxil fumarate (TDF), had long been an FDA-approved treatment for HIV; nevertheless, ProTide
Table 21.1 Example of ProTides that are already approved or are in a clinical study.
Chemical compound Nucleoside Application Clinical trial/Approved
Sofosbuvir PSI-6206 Antiviral (HCV) Approved
Remdisivir GS441524 COVID-19 Approved
Stampidine Stavudine Antiviral (HIV) Phase I
TAF Tenofovir Antiviral (HIV and HBV) Approved
Acelarin Gemcitabine Anticancer Phase III
Thymectacin Brivudine Anticancer Phase I
NUC3373 2′-Deoxy-5-fluorouridine Anticancer Phase II
NUC7738 3′-Deoxy-adenosine
Anticancer Phase I
Source: Adapted from [62].

21 PROTAC and ProTide Strategies in Drug Design470
TAF exhibited better anti-HIV efficacy with greater in vivo stability, and fewer side effects com-
pare to both tenofovir and TDF [63]. TAF is the latest ProTide of the acyclic nucleoside phospho-
nate medication tenofovir, developed by Gilead Sciences Inc. Tenofovir disoproxil fumarate
(TDF), another prodrug of tenofovir, had long been an FDA-approved treatment for HIV; never-
theless, ProTide TAF displayed better anti-HIV efficacy with greater in vivo stability, and fewer
side effects than both tenofovir and TDF (Table 21.1). After being absorbed by the cell, the medi-
cation becomes phosphorylated and pharmacologically active in the diphosphate form, inhibit-
ing reverse transcriptase. Whenever this enzyme becomes blocked, the DNA chain breaks, and
viral replication is stopped [10, 63, 64].
21.6 Implementation of ProTides as Nucleoside Analogs
21.6.1 Antiviral Applications of ProTides
The development of the phosphoramidate prodrugs started in the 1990s when McGuigan and his
colleagues assessed several azidothymidine (AZT; zidovudine) phosphate triester derivatives for
their ability to inhibit the human immunodeficiency virus (HIV-1). Substituted dialkyl phos-
phates were effective against HIV, while simple AZT and 2′,3′-dideoxythymidine (d4T, stavu-
dine) dialkyl phosphates were ineffective. These results confirmed that the biological impacts of
strategically changed phosphates were mediated by the intracellular release of their nucleotide
form [60]. This resulted in McGuigan and Professor Jan Balzarini working together to modify
the prodrug’s structure with the aim of maximizing activation and potency [65]. Following
numerous adjustments, aryloxyphosphoramidates containing AZT were created. In CEM cells,
these compounds exhibited strong, selective anti-HIV activity; the biological effect’s strength
varied significantly depending on the type of phosphate-masking groups used. As per the litera-
ture, the first known examples of ProTides are AZT aryloxy phosphoramidates, which provide
the McGuigan group recognition for creating this group of prodrugs. Throughout several years,
the collaboration between the laboratories of McGuigan and Balzarini gifted the progress of
ProTide technology. The ProTide method was successfully utilized for several more antiviral
NAs after the AZT studies. The examples of ProTides for treatment of various types of viral infec-
tions are 2′,3′-dideoxyadenosine and 2′,3′-didehydro-2′,3′-dideoxyadenosine,2′,3′-didehydro-2′,
3′-dideoxythymidine (d4T, Stavudine), 2′,3′-dideoxyadenosine-3′-fluoroadenosine [42],
2′,3′-dideoxydrouridine, and 2′,3′-didehydro-2′3′-dideoxyuridine, carbocyclic nucleoside
Abacavir (ABC, Ziagen), carbocyclic adenosine derivatives, and 2′,3′-dideoxy-2′3′-didehydro-7-
deazaadenosine [66, 67].
Although the ProTide technique was only partially successful, attempts to target influenza viruses
were attempted. When McGuigan’s team and Inhibitex worked to develop INX189, Pharmasset
worked on developing PSI-7977, another nucleoside phosphoramidate. After that Gilead advanced
this drug, and the FDA approved it as Sofosbuvir, in December 2013 for the therapy of HCV infections.
The antiviral drug 2С-deoxy-2С-α-fluoro-β-C-methyluridine-5′-monophosphate, whose triphos-
phate class is a potent inhibitor of the HCV ribonucleic acid- (RNA) dependent RNA polymerase
(NS5B), is produced by the liver metabolism of sofosbuvir. Phase I trials of sofosbuvir were con-
ducted using the diastereoisomeric mixture; phase II and III trials were conducted using the pure Sp
diastereoisomer. Its Rp isomer was found to be eight times less powerful than it [47]. Eventually,
sofosbuvir emerged as the medicine with the quickest global sales history. Patients with genotype 1,
2, 3, or 4 infections, including those with hepatocellular carcinoma, showed its effectiveness [68].

References 471
Gilead contributed to the development of GS-6620, a further anti-HCV proTide, alongside sofos-
buvir. This drug exhibited significant anti-HCV efficiency in phase I clinical study, but its substan-
tial intra- and interpatient pharmacokinetic and pharmacodynamic variability impeded future
development ProTide GS5734 (Remdesevir), which is being tested in clinical trials for the treat-
ment of Ebola virus infections, was found as a result of ongoing studies into it as a possible treat-
ment for other viral disorders [10].
21.7 Conclusion
Targeted protein degradation might grow to become a crucial therapeutic approach given the
advancements made over the previous 20 years as well as the recent degree of interest and fund-
ing from both academia and industry. It has been rewarding to see how it has evolved from an
intriguing hypothesis to a clinically validated idea, first with IMiDs and molecular glues and,
recently, with heterobifunctional PROTACs in phase I and phase II clinical trials. The effective
use of PROTACs in conjunction with antivirals has the potential to alleviate the current drug
discovery bottleneck and offer a more readily available means of combating the pandemic.
PROTACs do, however, come with a lot of drawbacks, and applying them in the antiviral field
presents a lot of difficulties. According to current reports, 15 different PROTAC individuals are
reportedly undergoing clinical trials at this time; most of them are aimed at treating tumors; how-
ever, one is also looking into treating autoimmune illnesses. PROTACs attach to the target pro-
teins using a reversible noncovalent arrangement, which leads to the target protein being rapidly
and completely eliminated within the cell and ceases all activities related to it. This is because the
primary mechanism of action of PROTACs is the recruitment of the cell’s own ubiquitin–
proteasome protein degradation pathway. Because the protein needs to be resynthesized to restore
its function and distribution after breakdown, the method of protein degradation is more robust
and effective. Through this, the disadvantages of small-molecule drugs that merely inhibit the
active site are reduced [19]. On the other hand, ProTide is an approach in which an active chemi-
cal entity is masked with a cleavable group (promoiety) that, in the human body, breaks off under
certain conditions, exposing the active species [7]. The development and implementation of mul-
tiple nucleoside monophosphate and monophosphate analogs in clinical applications have been
made easier by ProTide technology. The successful ProTide method that we utilize today was
developed over many years of trial and error, especially by the McGuigan group, to maximize the
masking groups of the phosphate. This technology was subsequently utilized for the early discov-
ery of nucleoside-based ProTides for Parkinson’s disease, even though it is used in the discovery
of nucleoside-based ProTides as antiviral and anticancer agents. As a prodrug strategy for intra-
cellular administration of monophosphorylated non-nucleoside compounds such as glucosa-
mine, Sphingosine 1-phosphate (S1P), 4-phospho--erythronohydroxamic acid, and 5-phospho
erythronohydroxamic acid, there is growing interest regarding the ProTide approach. There is
hope these methods could provide patients with novel therapeutic alternatives for a variety of
conditions in the near future.
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