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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

18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating Multidrug-Resistant Bacterial Infections 413
O
NH
NH
2
O
NH
H
2
N
Pentamidine
HO
O
N
H
O O
OH
O
O
O
OH
O
O NH
2
Novobiocin
O
H
N
O
N
H
O
H
N
O
N
H
O NH
O
N
H
O
NH
O
HN
OHN
O
H
N
O
NH
HO
NH
2
NH
2
H
2
N
NH
2
OH
NH
2
Colistin
N
N
N
OH
O
O
O
O
O
OH
OH
OHN
HO
HO
O
Rifampicin
O
H
N
O
N
H
OH
O
H
N
S
S
N
H
O
HN
O
NH
O
NH
O
N
H
O
HN
O
NHO
O
NH
2
NH
HN
NH
2
HN NH
NH
2
HO
NH
2
OHN
O
NH
2
O
NH
NH
NH
H
2
N
O NH
S
O
OH
N
H
O
N
O
NH
O
NO
NH
2
HN
O
H
2
N
NH
O
HO
N
H
O
H
2
N
Thanatin
Similarly, the combination therapy of antibacterial drugs along with efflux pump inhibitors can
make the antimicrobial agent available for a longer period at the target site to exert its potential
action. Loperamide along with minocycline, econazole along with colistin, tobramycin-lysine
hybrid along with novobiocin, etc., work efficiently in this way. In recent years, the combination of
biofilm damages along with antimicrobial agents is also used in combined therapy.

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections414
Cl
N
N
O
OH
Loperamide
N
OO OH
OH
O
NH
2
OH
N
OH
Minocycline
Econazole
O
H
N
O
N
H
O
H
N
O
N
H
O NH
O
N
H
O
NH
O
HN
OHN
O
H
N
O
NH
HO
NH
2
NH
2
H
2
N
NH
2
OH
NH
2
Colistin
Cl
Cl
O
Cl
N
N
NH
2
O
O
OH
OH
NH
2
OHOH
O
O
H
2
N
HO
NH
2
H
2
N
HO
O
N
H
O O
OH
O
O
O
OH
O
O NH
2
Tobramycin Novobiocin
18.4.7 Drug Repurposing
It includes examining current medications for any novel antibacterial qualities. Finding new
applications for medications that were first created for different purposes is known as drug
repurposing. This strategy can hasten the process of developing new drugs. The phosphoinositide
3-kinase inhibitors are repurposed to use as adjuvants in the treatment of bacterial infections;
repurposing of novel β-lactams and polymyxins along with adjuvants (nonantibiotics) like
antibiotic inactivating enzyme inhibitors, membrane permeabilizes, efflux pump inhibitors, and
biofilm damages are some of the reported examples of drug-repurposing approaches in MDR
antibacterial drug development [44].
18.4.8 Resistant Mechanism Blocking
Create medications that block particular resistance pathways, like efflux pumps, decreased perme-
ability, or the enzymes that render a medication inactive. Combining these inhibitors with existing
antibiotics can enhance their effectiveness. Cefiderocol is a siderophore cephalosporin that
improves entry into bacterial cells by taking advantage of the iron transport system in bacteria,
hence circumventing some resistance mechanisms [45].

18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating Multidrug-Resistant Bacterial Infections 415
O
HO
O
N
S
N
H
2
N
O
H
N
N
O
S
N
+
HN
O
HO
HO
Cl
O
O
–
Cefiderocol
18.4.9 Improving Drug Delivery by Nanotechnology
Combining antibiotics with nanomaterial compounds also known as nano-antibiotics is another
innovative way to boost the efficacy of antibiotics. For instance, vancomycin nanoformulation has
increased antibacterial action against multidrug-resistant pathogens such as vancomycin-resistant
Enterococcus and methicillin-resistant Staphylococcus aureus when coupled with silver nanoparticles.
Nanotechnology was employed to reduce adverse effects on host cells. Nanoparticles and nanocarriers
can maximize drug stability,bioavailability, and targeted delivery to bacterial cells. Recently, ampicillin
silver (AgNPs) and gold nanoparticles (AuNPs) were proven as broad-spectrum antibacterial agents to
overcome the resistance against multidrug-resistant strains of bacterial species functionalized with
vancomycinand were developed to combat vancomycin-resistant enterococci. The antibacterial activity
of alginate nanoparticles comprising colistin showed more potent activity against colistin-resistant
bacterial strains. Similarly, linezolid bound to silver/gold bimetallic nanoparticles (Au@Ag@Lz)
showed significant efficacy even against methicillin-resistant Staphylococcus aureus [46].
N
S
O
N
H
O
H
2
N
O
HO
Ag
Ampicillin silver nanoparticles
N
S
O
N
H
O
H
2
N
O
HO
Au
Ampicillin gold nanoparticles
O
O
O O
O
Cl
O
N
H
O H
N
O
N
H
HO
OH
HN
O
Cl
O
OH
H
N
O
O
OH
HO
O
NH
2
H
N
O
NH
HO
HO
HO
OH
H
2
N
OH
+COOH
SAg
Vancomycin-Ag Nanoparticle

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections416
18.4.10 Phage Intervention
Examine the potential applications of bacteriophages and viruses that infect bacteria as a medical
strategy. Phage therapy offers a precise and focused method of eliminating bacteria, and phages
can change over time to combat resistance in bacteria. The host range of lytic phages is determined
by the fact that the majority of them are only infectious to bacteria that also carry their
corresponding receptor. Phages differ in their host specificity; some are strain specific, while
others can infect various bacterial strains and even species. Bacteria have developed a multitude
of defense mechanisms against lytic phage infection, while phages have amassed an equally
remarkable array of defense mechanisms against this resistance. This can involve the deletion or
mutation of receptors in bacteria as well as the integration of phage DNA into the CRISPR/Cas
system, which is a clustered regularly interspaced palindromic repeats system. For phages, this
can involve the detection of anti-CRISPR genes and new or altered receptors. For the first time,
bacterial phases were used to treat pediatric dysentery in 1919. Phase therapy was continued in
the 20th century for treating cholera, dysentery, and bubonic plague through the production of
bacteriophage preparations on a commercial scale. Recent works revealed that therapy with
phase preparations significantly improved P. aeruginosa gut-derived sepsis. In animal models,
phage cocktails have also been utilized to treat resistant P. aeruginosa infections that affect the
skin, lungs, and digestive system [47].
18.4.11 Host Targeting
Bacteria such as Mycobacterium tuberculosis and Coxiella burnetii can delay or avoid the fusion of their
vacuoles with lysosomes and develop a replicate niche that helps to escape from phagosomes thereby
entering into the cytosol. Designing potential drugs that specifically target host cell elements like the
resolution of signaling pathways, enhancing the autophagy activity, stabilizing and improvingthe innate
immune pathways, adapting the neutralizing mechanism for reactive species, and reducing the host
proinflammatory response are the ways through which the bacterial life cycle can be disturbed, and the
likelihood of bacterial resistance development is reduced by altering host factors. For instance, when
monophosphoryl lipid A, a chemically altered derivative of the lipid A moiety of lipopolysaccharide, was
inhaled, the amount of Moraxella catarrhalis and Haemophilus influenzae bacteria that were collected
from the nasopharynx was significantly lower . The Escherichia coli infection in the bladder was greatly
reduced (10-fold) by administration of an HIF-1α-stabilizing agent (AKB-4924) [48].
18.4.12 CRISPR-Cas Technique
Make use of CRISPR-Cas technology to target and remove specific DNA from bacteria. This
method can target particular bacterial strains that are prone to ABR precisely because of their high
selectivity. For instance, the viability of E. coli was drastically reduced by targeting the virulence
factor gene eae, which is essential for the pathology and colonization of these bacteria by CRISPR-
Cas technique. Similarly, this technique was employed to potentiate the bactericidal activity of
carbapenem against E. coli by targeting resistance genes such as blaOXA-48 and bla IMP-1 and
colistin against E. coli by targeting resistance genes like mcr-1&2 [49].
18.4.13 Peptides as Antibacterials
The application of antimicrobial peptides (AMPs) can damage bacterial membranes or obstruct vital
bacterial processes. These peptides might be less likely to develop resistance and exhibit

18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating Multidrug-Resistant Bacterial Infections 417
broad-spectrum activity. The first AMP discovered and isolated in 1939 from soil Bacillus brevis is
gramicidin; it exhibits its antimicrobial action by affecting the strength or disruption of bacterial cell
walls through different types of interactions. For instance, the biofilms formed in K. pneumonia-
resistant drugs like imipenem, meropenem, and cefepime were efficiently encountered by the
protease-resistant peptide. Similarly, the peptide and its derivatives isolated from the frog skin secre-
tions showed better therapeutic activity against biofilms of Enterococcus faecalis and methicillin-
resistant S. aureus [50].
18.4.14 Immunizations and Immunotherapy
Make investments in the creation of vaccines to combat bacterial strains resistant to antibiotics. As
an addition to traditional antimicrobial treatments, immunotherapies that strengthen the host
immune response against bacterial infections may be investigated. For instance, Raxibacumab
toxin a monoclonal antibody was proven to be effective in the treatment of anthrax caused by
B. anthraci, the monoclonal antibody Bexlotoxumab was developed for treating recurrent
Clostridium difficile infections [51].
18.4.15 Natural Product Derivatives
Since many of the antibiotics currently in use are derived from natural products, exploring and
modifying compounds derived from natural sources might be hopeful in this drug discovery. So
many natural compounds were reported for antibacterial activity against different bacteria,
Piperine is potent against Lactobacillus and Micrococcus, and anthemic acid is potent against
M. tuberculosis, Staphylococcus aureus, etc. [52].
18.4.16 Fragment-Based Drug Discovery (FBDD)
This method entails determining the interactions that low-molecular-weight molecules
(100–300 Da) have with their potential targets, frequently at low affinity (KD ~0.1–1 mM).
Beginning with tiny chemical fragments and working your way up to more complex compounds
can bind effectively to bacterial targets. The fragments with low molecular weight to high molec-
ular weight biologically active compounds were screened for potential antibacterial activity
against bacterial targets like Biotin Carboxylase, DNA Gyrase, Cell Division Protein FtsZ, and
β-lactamase [53, 54].
18.4.17 Metabolomics and Genetics
Integrating information from bacterial genetics with metabolomics uncovers new targets and
pathways that can be exploited for antibiotic development.
18.4.18 Cheminformatics
The study of chemical data analysis and the prediction of novel compounds’ antibacterial activity
through the use of algorithms and data analysis tools such as SwissADMET, Drugmint,
SwissBioisostere, pkCSM, DataWarrior, Galaxy, BioTransformer, and Knime for determining drug-
likeness, and ADMET properties. [55, 56].

18 Rational Design of Antibacterial Agents for Multidrug-Resistant Infections418
18.5 Summary and Conclusion
In summary, bacterial infections, with their various origins, symptoms, and developing treatment
approaches, continue to pose a serious threat to global health. Current outbreaks highlight the
enduring danger presented by bacterial infections and the pressing need to create efficient defenses
against them. Treatment attempts are made more difficult by the emergence of antimicrobial resist-
ance, which calls for a deeper comprehension of the fundamental mechanisms causing resistance.
Antimicrobial resistance has been shown to occur through multiple methods, such as genetic
changes, HGT, and biofilm formation. These mechanisms pose significant challenges to conven-
tional antibiotic therapy. Additionally, the emergence of antibiotic resistance makes clinical man-
agement of bacterial infections even more difficult, restricting available treatments and raising
death rates.
A multidisciplinary strategy that includes creative antibacterial drug creation techniques and
coordinated attempts to tackle resistance mechanisms is needed to address these issues. Novel
therapeutic targets are necessary, and improvements in combination therapy and drug delivery
methods present intriguing paths to overcome resistance.
However, creating antibacterial medications that work against resistant types of bacteria is still
a difficult endeavor with many unknowns in science. The intricate relationship that exists between
host–pathogen interactions, environmental variables, and bacterial physiology highlights the
importance of interdisciplinary collaboration and integrated research efforts.
In light of these factors, a number of approaches, such as target validation, structure-based drug
design, and repurposing of already-existing molecules, have enormous potential in the search for
next-generation antibacterial medicines. Adopting a comprehensive strategy that combines prot-
eomic, genomic, and computational techniques can spur the creation of novel treatments that can
evade resistance mechanisms and protect public health.
In conclusion, the fight against bacterial infections and antibiotic resistance necessitates persis-
tent attention to detail, creative thinking on the part of scientists and cooperative efforts from a wide
range of stakeholders. Through the utilization of state-of-the-art technologies and the promotion of
an innovative culture, it is possible to reduce the effects of pathogen resistance and maintain the
effectiveness of antibacterial treatments for future generations. This chapter explained the bacterial
infections: causes, symptoms, symptoms, and recent outbreaks of bacterial infections, elaborated
treatment strategies, development of resistance against the antimicrobial agents, i.e., antimicrobial
resistance, and mechanisms involved in developing antimicrobial resistance or MDR. This chapter
also briefed about the essentiality and challenges in antibacterial drug design against resistant bac-
teria shown through different mechanisms. Further , it described the various strategies that can be
effective in antibacterial drug design against multidrug-resistant strains. The information provided
in this chapter might be made knowledgeable and enlighten them with different thought processes.
Hence, this information might be helpful in novel antibacterial drug design and development
against vulnerable and endangered MDR bacterial infections in the future.
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