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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

eferences 383
12 De Felice, F.G. and Lourenco, M.V. (2015). Brain metabolic stress and neuroinflammation at the
basis of cognitive impairment in Alzheimer’s disease. Frontiers in Aging Neuroscience 7: https://doi.
org/10.3389/fnagi.2015.00094.
13 Pihlstrøm, L., Wiethoff, S., and Houlden, H. (2018). Genetics of neurodegenerative diseases: an
overview. Handbook of Clinical Neurology 309–323. https://doi.org/10.1016/B978-0-12-802395-
2.00022-5.
14 Meng, X.Y., Zhang, H.X., Mezei, M., and Cui, M. (2011). Molecular docking: a powerful approach
for structure-based drug discovery. Current Computer Aided-Drug Design 7: 146–157. https://doi.
org/10.2174/157340911795677602.
15 Muhammad, U., Uzairu, A., and Ebuka Arthur, D. (2018). Review on: quantitative structure
activity relationship (QSAR) modeling. Journal of Analytical & Pharmaceutical Research 7:
https://doi.org/10.15406/japlr.2018.07.00232.
16 Voet, A., Qing, X., Lee, X.Y. et al. (2014). Pharmacophore modeling: advances, limitations, and
current utility in drug discovery. Journal of Receptor, Ligand and Channel Research 81: https://doi.
org/10.2147/JRLCR.S46843.
17 Yang, S.Y. (2010). Pharmacophore modeling and applications in drug discovery: challenges and
recent advances. Drug Discovery Today 15: 444–450. https://doi.org/10.1016/j.drudis.2010.03.013.
18 Maia, E.H.B., Assis, L.C., de Oliveira, T.A. et al. (2020). Structure-based virtual screening: from
classical to artificial intelligence. Frontiers in Chemistry 8: https://doi.org/10.3389/fchem.
2020.00343.
19 De Vivo, M., Masetti, M., Bottegoni, G., and Cavalli, A. (2016). Role of molecular dynamics and
related methods in drug discovery. Journal of Medicinal Chemistry 59: 4035–4061. https://doi.
org/10.1021/acs.jmedchem.5b01684.
20 Dara, S., Dhamercherla, S., Jadav, S.S. et al. (2021). Machine learning in drug discovery: a review.
Artificial Intelligence Review 55: 1947–1999. https://doi.org/10.1007/s10462-021-10058-4.
21 Kirsch, P., Hartman, A.M., Hirsch, A.K.H., and Empting, M. (2019). Concepts and core principles
of fragment-based drug design. Molecules 24: 4309. https://doi.org/10.3390/molecules24234309.
22 Hartenfeller, M. and Schneider, G. (2010). De novo drug design. Methods in Molecular Biology
299–323. https://doi.org/10.1007/978-1-60761-839-3_12.
23 Aarsland, D., Batzu, L., Halliday, G.M. et al. (2021). Parkinson disease-associated cognitive
impairment. Nature Reviews Disease Primers 7: https://doi.org/10.1038/s41572-021-00280-3.
24 Khan, A.U., Akram, M., Daniyal, M., and Zainab, R. (2018). Awareness and current knowledge of
Parkinson’s disease: a neurodegenerative disorder. International Journal of Neuroscience 129:
55–93. https://doi.org/10.1080/00207454.2018.1486837.
25 Tysnes, O.B. and Storstein, A. (2017). Epidemiology of Parkinson’s disease. Journal of Neural
Transmission 124: 901–905. https://doi.org/10.1007/s00702-017-1686-y.
26 Ascherio, A. and Schwarzschild, M.A. (2016). The epidemiology of Parkinson’s disease: risk factors
and prevention. The Lancet Neurology 15: 1257–1272. https://doi.org/10.1016/
S1474-4422(16)30230-7.
27 Verma, A.K., Raj, J., Sharma, V. et al. (2017). Epidemiology and associated risk factors of
Parkinson’s disease among the north Indian population. Clinical Epidemiology and Global Health
5: 8–13. https://doi.org/10.1016/j.cegh.2016.07.003.
28 Klein, C. and Westenberger, A. (2012). Genetics of Parkinson’s disease. Cold Spring Harbor
Perspectives in Medicine 2: a008888. https://doi.org/10.1101/cshperspect.a008888.
29 Ball, N., Teo, W.P., Chandra, S., and Chapman, J. (2019). Parkinson’s disease and the environment.
Frontiers in Neurology 10: https://doi.org/10.3389/fneur.2019.00218.
30 Mahul-Mellier, A.L., Burtscher, J., Maharjan, N. et al. (2020). The process of Lewy body formation,
rather than simply α-synuclein fibrillization, is one of the major drivers of neurodegeneration.

384
Proceedings of the National Academy of Sciences 117: 4971–4982. https://doi.org/10.1073/
pnas.1913904117.
31 Moon, H.E. and Paek, S.H. (2015). Mitochondrial dysfunction in Parkinson’s disease [Internet].
Experimental Neurobiology 24: 103–116. https://doi.org/10.5607/en.2015.24.2.103.
32 Ye, H., Robak, L.A., Yu, M. et al. (2023). Genetics and pathogenesis of Parkinson’s syndrome
[Internet]. Annual Review of Pathology: Mechanisms of Disease 18: 95–121. https://doi.org/10.1146/
annurev-pathmechdis-031521-034145.
33 Troncoso-Escudero, P., Parra, A., Nassif, M., and Vidal, R.L. (2018). Outside in: unraveling the role
of neuroinflammation in the progression of Parkinson’s disease [Internet]. Frontiers in Neurology
9: https://doi.org/10.3389/fneur.2018.00860.
34 Cook, C., Stetler, C., and Petrucelli, L. (2012). Disruption of protein quality control in Parkinson’s
disease. Cold Spring Harbor Perspectives in Medicine 2: a009423. https://doi.org/10.1101/cshperspect.
a009423.
35 Dias, V., Junn, E., and Mouradian, M.M. (2013). The role of oxidative stress in Parkinson’s disease.
Journal of Parkinson’s Disease 3: 461–491. https://doi.org/10.3233/JPD-130230.
36 Pan-Montojo, F., Loewenbrück, K.F., and Reichmann, H. (2020). The role of environmental toxins
and inflammation in Parkinson’s disease pathophysiology: a historical perspective and research-
based evidence. Diagnosis and Management in Parkinson’s Disease 39–55. https://doi.org/10.1016/
B978-0-12-815946-0.00003-X.
37 Dong-Chen, X., Yong, C., Yang, X. et al. (2023). Signaling pathways in Parkinson’s disease:
molecular mechanisms and therapeutic interventions. Signal Transduction and Targeted Therapy
8: https://doi.org/10.1038/s41392-023-01353-3.
38 Bohush, A., Niewiadomska, G., and Filipek, A. (2018). Role of mitogen activated protein kinase
signaling in Parkinson’s disease. International Journal of Molecular Sciences 19: 2973. https://doi.
org/10.3390/ijms19102973.
39 Xu, F., Na, L., Li, Y., and Chen, L. (2020). Retracted article: roles of the PI3K/AKT/mTOR
signalling pathways in neurodegenerative diseases and tumours. Cell & Bioscience 10: https://doi.
org/10.1186/s13578-020-00416-0.
40 Goyal, A., Agrawal, A., Verma, A., and Dubey, N. (2023). The PI3K-AKT pathway: a plausible
therapeutic target in Parkinson’s disease. Experimental and Molecular Pathology 129: 104846.
https://doi.org/10.1016/j.yexmp.2022.104846.
41 Serafino, A. and Cozzolino, M. (2023). The Wnt/β-catenin signaling: a multifunctional target for
neuroprotective and regenerative strategies in Parkinson’s disease. Neural Regeneration Research
18: 306. https://doi.org/10.4103/1673-5374.343908.
42 Singh, S.S., Rai, S.N., Birla, H. et al. (2019). NF-κB-mediated neuroinflammation in Parkinson’s
disease and potential therapeutic effect of polyphenols. Neurotoxicity Research 37: 491–507.
https://doi.org/10.1007/s12640-019-00147-2.
43 Bonam, S.R., Tranchant, C., and Muller, S. (2021). Autophagy-lysosomal pathway as potential
therapeutic target in Parkinson’s disease. Cells 10: 3547. https://doi.org/10.3390/cells10123547.
44 Peng, J. and Andersen, J. (2003). The role of c-Jun N-terminal kinase (JNK) in Parkinson’s disease.
IUBMB Life 55: 267–271. https://doi.org/10.1080/1521654031000121666.
45 Curry, D.W., Stutz, B., Andrews, Z.B., and Elsworth, J.D. (2018). Targeting AMPK signaling as a
neuroprotective strategy in Parkinson’s disease. Journal of Parkinson’s Disease 8: 161–181. https://
doi.org/10.3233/JPD-171296.
46 Yang, X.X., Yang, R., and Zhang, F. (2022). Role of Nrf2 in Parkinson’s disease: toward new
perspectives. Frontiers in Pharmacology 13: https://doi.org/10.3389/fphar.2022.919233.

eferences 385
47 Dezsi, L. and Vecsei, L. (2017). Monoamine oxidase B inhibitors in Parkinson’s disease. CNS &
Neurological Disorders – Drug Targets 16: https://doi.org/10.2174/1871527316666170124165222.
48 Rivest, J., Barclay, C.L., and Suchowersky, O. (1999). COMT inhibitors in Parkinson’s disease.
Canadian Journal of Neurological Sciences/Journal Canadien des Sciences Neurologiques 26: S34–
S38. https://doi.org/10.1017/s031716710000007x.
49 Wojewska, D.N. and Kortholt, A. (2021). LRRK2 targeting strategies as potential treatment of
Parkinson’s disease. Biomolecules 11: 1101. https://doi.org/10.3390/biom11081101.
50 Martínez-Bailén, M., Clemente, F., Matassini, C., and Cardona, F. (2022). GCase enhancers: a
potential therapeutic option for Gaucher disease and other neurological disorders. Pharmaceuticals
15: 823. https://doi.org/10.3390/ph15070823.
51 Salemi, M., Mazzetti, S., De Leonardis, M. et al. (2021). Poly (ADP-ribose) polymerase 1 and
Parkinson’s disease: a study in post-mortem human brain. Neurochemistry International 144:
104978. https://doi.org/10.1016/j.neuint.2021.104978.
52 Vizziello, M., Borellini, L., Franco, G., and Ardolino, G. (2021). Disruption of mitochondrial
homeostasis: the role of PINK1 in Parkinson’s disease. Cells 10: 3022. https://doi.org/10.3390/
cells10113022.
53 Heremans, I.P., Caligiore, F., Gerin, I. et al. (2022). Parkinson’s disease protein PARK7 prevents
metabolite and protein damage caused by a glycolytic metabolite. Proceedings of the National
Academy of Sciences 119: https://doi.org/10.1073/pnas.2111338119.
54 Haddad, F., Sawalha, M., Khawaja, Y. et al. (2017). Dopamine and levodopa prodrugs for the
treatment of Parkinson’s disease. Molecules 23: 40. https://doi.org/10.3390/molecules23010040.
55 Luo, D., Reith, M., and Dutta, A.K. (2020). Dopamine agonists in treatment of Parkinson’s disease:
an overview. Diagnosis and Management in Parkinson’s Disease 445–460. https://doi.org/10.1016/
B978-0-12-815946-0.00026-0.
56 Tan, Y.Y., Jenner, P., and Chen, S.D. (2022). Monoamine oxidase-B inhibitors for the treatment of
Parkinson’s disease: past, present, and future. Journal of Parkinson’s Disease 12: 477–493. https://
doi.org/10.3233/JPD-212976.
57 Fabbri, M., Ferreira, J.J., and Rascol, O. (2022). COMT inhibitors in the management of Parkinson’s
disease. CNS Drugs 36: 261–282. https://doi.org/10.1007/s40263-021-00888-9.
58 Katzenschlager, R., Sampaio, C., Costa, J., and Lees, A. (2002). Anticholinergics for symptomatic
management of Parkinson’s disease. Cochrane Database of Systematic Reviews 2010: https://doi.
org/10.1002/14651858.CD003735.
59 Pagano, G., Rengo, G., Pasqualetti, G. et al. (2014). Cholinesterase inhibitors for Parkinson’s
disease: a systematic review and meta-analysis. Journal of Neurology, Neurosurgery & Psychiatry 86:
767–773. https://doi.org/10.1136/jnnp-2014-308764.
60 Zhuo, C., Xue, R., Luo, L. et al. (2017). Efficacy of antidepressive medication for depression in
Parkinson disease. Medicine 96: e6698. https://doi.org/10.1097/MD.0000000000006698.
61 Shotbolt, P., Samuel, M., and David, A. (2010). Quetiapine in the treatment of psychosis in
Parkinson’s disease. Therapeutic Advances in Neurological Disorders 3: 339–350. https://doi. org/
10.1177/1756285610389656.
62 Tarsy, D., Parkes, J.D., and Marsden, C.D. (1975). Metoclopramide and pimozide in Parkinson’s
disease and levodopa-induced dyskinesias. Journal of Neurology, Neurosurgery & Psychiatry 38: 331–
335. https://doi.org/10.1136/jnnp.38.4.331.
63 Barrett, M.J., Sargent, L., Nawaz, H. et al. (2021). Antimuscarinic anticholinergic medications in
Parkinson disease: to prescribe or deprescribe? Movement Disorders Clinical Practice 8: 1181–1188.
https://doi.org/10.1002/mdc3.13347.

386
64 Groiss, S.J., Wojtecki, L., Südmeyer, M., and Schnitzler, A. (2009). Review: deep brain stimulation
in Parkinson’s disease. Therapeutic Advances in Neurological Disorders 2: 379–391. https://doi. org/
10.1177/1756285609339382.
65 Radder, D.L.M., Sturkenboom, I.H., van Nimwegen, M. et al. (2017). Physical therapy and
occupational therapy in Parkinson’s disease. International Journal of Neuroscience 127: 930–943.
https://doi.org/10.1080/00207454.2016.1275617.
66 Tjaden, K. (2008). Speech and swallowing in Parkinson’s disease. Topics in Geriatric Rehabilitation
24: 115–126. https://doi.org/10.1097/01.TGR.0000318899.87690.44.
67 Oliveira de Carvalho, A., Filho, A.S.S., Murillo-Rodriguez, E. et al. (2018). Physical exercise for
Parkinson’s disease: clinical and experimental evidence. Clinical Practice & Epidemiology in Mental
Health 14: 89–98. https://doi.org/10.2174/1745017901814010089.
68 Knight, E., Geetha, T., Burnett, D., and Babu, J.R. (2022). The role of diet and dietary patterns in
Parkinson’s disease. Nutrients 14: 4472. https://doi.org/10.3390/nu14214472.
69 Pires, A.O., Teixeira, F.G., Mendes-Pinheiro, B. et al. (2017). Old and new challenges in Parkinson’s
disease therapeutics. Progress in Neurobiology 156: 69–89. https://doi.org/10.1016/j.pneurobio.
2017.04.006.
70 LeWitt, P.A. and Chaudhuri, K.R. (2020). Unmet needs in Parkinson disease: motor and non
motor. Parkinsonism & Related Disorders 80: S7–S12. https://doi.org/10.1016/j.parkreldis.
2020.09.024.
71 Shalaby, K.E. and El-Agnaf, O.M.A. (2022). Gene-based therapeutics for Parkinson’s disease.
Biomedicines 10: 1790. https://doi.org/10.3390/biomedicines10081790.
72 Glaab, E. (2017). Computational systems biology approaches for Parkinson’s disease. Cell and
Tissue Research 373: 91–109. https://doi.org/10.1007/s00441-017-2734-5.
73 Yousef, M., Najami, N., Abedallah, L., and Khalifa, W. (2014). Computational approaches for
biomarker discovery. Journal of Intelligent Learning Systems and Applications 6: 153–161. https://doi.
org/10.4236/jilsa.2014.64012.
74 Kumar, R., Malik, M.Z., Thanaraj, T.A. et al. (2023). A computational biology approach to identify
potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis. Cellular
Signalling 112: 110915. https://doi.org/10.1016/j.cellsig.2023.110915.
75 Zhang, F., Wu, X., and Chen, J.Y. (2013). Computational biomarker discovery. Approaches in
Integrative Bioinformatics 355–386. https://doi.org/10.1007/978-3-642-41281-3_13.
76 Kaushik, A.C., Bharadwaj, S., Kumar, S., and Wei, D.Q. (2018). Nano-particle mediated inhibition
of Parkinson’s disease using computational biology approach. Scientific Reports 8: https://doi.org/
10.1038/s41598-018-27580-1.
77 Muddapu, V.R., Mandali, A., Chakravarthy, V.S., and Ramaswamy, S. (2019). A computational
model of loss of dopaminergic cells in Parkinson’s disease due to glutamate-induced excitotoxicity.
Frontiers in Neural Circuits 13: https://doi.org/10.3389/fncir.2019.00011.
78 Tan, S., Lu, R., Yao, D. et al. (2023). Identification of LRRK2 inhibitors through computational
drug repurposing. ACS Chemical Neuroscience 14: 481–493. https://doi.org/10.1021/
acschemneuro.2c00672.
79 Vaz, R.L., Sousa, S., Chapela, D. etal. (2020). Identification of antiparkinsonian drugs in the 6-
hydroxydopamine zebrafish model. Pharmacology Biochemistry and Behavior 189: 172828. https://
doi.org/10.1016/j.pbb.2019.172828.
80 Kurnik, M., Sahin, C., Andersen, C.B. et al. (2018). Potent α-synuclein aggregation inhibitors,
identified by high-throughput screening, mainly target the monomeric state. Cell Chemical Biology
25: 1389–1402.e9. https://doi.org/10.1016/j.chembiol.2018.08.005.

eferences 387
81 Zhang, H., Deng, K., Li, H. et al. (2020). Deep learning identifies digital biomarkers for self
reported Parkinson’s disease. Patterns 1: 100042. https://doi.org/10.1016/j.patter.2020.100042.
82 Liu, Y.Y., Yu, L.H., and Zhang, J. (2021). Network pharmacology-based and molecular docking
based analysis of Suanzaoren decoction for the treatment of Parkinson’s disease with sleep disorder.
(Alatas, B., ed.). BioMed Research International 2021: 1–12. https://doi.org/10.1155/2021/1752570.
83 Prasad, E.M. and Hung, S.Y. (2021). Current therapies in clinical trials of Parkinson’s disease: a
2021 update. Pharmaceuticals 14: 717. https://doi.org/10.3390/ph14080717.
84 Mei, J., Desrosiers, C., and Frasnelli, J. (2021). Machine learning for the diagnosis of Parkinson’s
disease: a review of literature. Frontiers in Aging Neuroscience 13: https://doi.org/10.3389/
fnagi.2021.633752.
85 Yadav, R., Bagrodia, V., Holla, V. et al. (2022). Parkinson’s disease and wearable technology: an
Indian perspective. Annals of Indian Academy of Neurology 25: 817. https://doi.org/10.4103/aian.
aian_653_22.
86 Morgan, C., Tonkin, E.L., Masullo, A. et al. (2023). A multimodal dataset of real world mobility
activities in Parkinson’s disease. Scientific Data 10: https://doi.org/10.1038/s41597-023-02663-5.
87 Ahmed, S., Komeili, M., and Park, J. (2022). Predictive modelling of Parkinson’s disease
progression based on RNA-Sequence with densely connected deep recurrent neural networks.
Scientific Reports 12: https://doi.org/10.1038/s41598-022-25454-1.
88 Bergamino, M., Keeling, E.G., Ray, N.J. etal. (2023). Structural connectivity and brain network
analyses in Parkinson’s disease: a cross-sectional and longitudinal study. Frontiers in Neurology 14:
https://doi.org/10.3389/fneur.2023.1137780.
89 Mishima, T., Fujioka, S., Morishita, T. et al. (2021). Personalized medicine in Parkinson’s disease:
new options for advanced treatments. Journal of Personalized Medicine 11: 650. https://doi.org/
10.3390/jpm11070650.
90 Cubo, E. and Delgado-López, P.D. (2022). Telemedicine in the management of Parkinson’s disease:
achievements, challenges, and future perspectives. Brain Sciences 12: 1735. https://doi.org/
10.3390/brainsci12121735.


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17.1 Introduction
Inflammatory disease (ID) is recognized as a leading cause of morbidity within the population and
its term comprises a comprehensive spectrum of diseases involved in chronic inflammation for the
progression of disease progression [1]. These conditions are linked to an engaged immune system,
encompassing activated immune cells and various biomolecules [2]. Inflammation serves as a
defensive response of an organism to counteract the intrusion of foreign entities such as viruses,
bacteria, and parasites [3]. IDs exert a profound impact on the physiology of organs, whether
deeply situated or superficially located. There are many conditions are present that affect various
organs, such as the intestine (inflammatory bowel disease; IBD), lungs (acute respiratory distress
syndrome; ARDS), liver (hepatitis), arteries (atherosclerosis), pancreas (pancreatitis), kidney
(nephritis), and heart (myocardial infarction). Similarly, psoriasis (influencing the skin), arthritis
(affecting joints), periodontitis (impacting dental health), and uveitis (affecting the eyes) are exam-
ples of IDs involving superficially located organs. It includes both “autoimmune diseases” and
“auto-IDs” [4]. The attack of the immune system on self-tissues like psoriasis, ulcerative, and rheu-
matoid arthritis is called an autoimmune disease in many diseases, whereas diseases like hepatitis,
uveitis, atherosclerosis, pulmonary diseases, myocardial infarction, pancreatitis, nephritis involves
uncontrolled inflammation called as auto-IDs [5]. In aggregate, IDs significantly contribute to the
global disease burden, commonly assessed through disability-adjusted life year (DALY) statistics.
For example, in 2017, the average DALY rate for both genders with chronic obstructive pulmonary
disease (COPD) was 1028.8 per 100,000 age-standardized years [6]. Over the decades, some small
molecules, like nonsteroidal anti-inflammatory drugs (NSAID), glucocorticoids, and antioxidants
have been widely used for the treatment of various ID, and other than their effect they have many
side effects like aseptic joint necrosis, gastrointestinal bleeding cardiovascular risk, liver/kidney
injury [7]. Certain monoclonal antibodies that target different pro-inflammatory cytokines,
chemokines, or other bio-macromolecules have been used in clinical settings or studied in preclini-
cal studies for the treatment of numerous conditions like rheumatoid arthritis (RA) [8], IBD [9],
17
Rational Design of Anti-inflammatory Therapeutics
Kratika Singh
1
, Anmol Gupta
2
, Irum Siddiqui
3
, Ashapurna Sinha
2
,
Mukesh Kumar Patwa
1
, and Urmila Singh
1
1
Department of Microbiology, King George Medical University, Lucknow, Uttar Pradesh, India
2
Department of Biosciences, Integral University, Lucknow, Uttar Pradesh, India
3
IIRC-1, Department of Bioengineering, Integral University, Lucknow, Uttar Pradesh, India

390
atherosclerotic disease [10], and asthma [11]. These antibodies regulate molecular and cellular
procedures closely linked to inflammatory responses (IRs). Still, the clinical application of these
agents has been accompanied by various limitations and side effects like cost effectiveness, a pri-
mary or secondary lack of reaction, as well as an increased risk of serious infections [12].
Undesirable effects in large part result from the systemic delivery in nontarget cells and tissue,
irrespective of whether it’s small molecule drugs or biological therapies. In addition, for certain
newly developed therapies like specialized pro-resolving mediators, which exhibit potent
inflammation-resolving activity and are highly effective in treating the inflammatory disorder, it
also becomes challenging to provide a controlled release and site-specific distribution in inflamma-
tory cells/tissues [7]. Over the past decades, many cutting-edge drug delivery strategies have been
explored to overcome the drawbacks accompanying traditional formulations of anti-inflammatory
therapies.
17.2 Navigating Inflammation and its Microenvironment
Inflammation is a completely natural biological response to internal and/or external stimuli of a
biophysical or chemical nature. This reaction is distinguished by the coordinated involvement of
various inflammatory cells and molecular mediators. Based on the duration of inflammation, it is
possible to classify it into two types: chronic and acute inflammatory responses. Severe inflamma-
tion, which is characterized by the prominent clinical symptoms of redness, swelling, pain, and
heat, typically remains used for some hours to a few days. In response to invading tissue injuries,
acute inflammation is generally accompanied by a quick and remarkable accumulation of inflam-
matory cells (especially neutrophils), cytokines, fluid, and chemokines in the affected tissues/
organs [13, 14]. Acute IR typically resolves on its own, restoring tissue balance (Figure 17.1).
Nevertheless, should a pathological insult persist or resolution be unattainable, tissue injury and
an ongoing IR may occur, resulting in chronic inflammation that can endure for months or even
years. Monocytes/macrophages are likely to play a significant role in this process by releasing
numerous inflammatory cytokines, which are closely linked to the development of chronic dis-
eases. While the specific triggers and body responses determine the initiation and inflammation
progression, their shared physiological and pathological characteristics are important targets for
diagnosing and treating various inflammatory conditions. In the subsequent part, we will briefly
discuss the typical pathophysiological traits of the inflammatory microenvironment, which pro-
vide valuable insights for designing drug delivery systems and bio-responsive materials to manage
inflammation-related diseases.
17.2.1 Inflammatory Cell Infiltration and Vascular Permeability
The altered structure and function of the microvasculature are the most important characteris-
tics of initial inflammation. This results in the widening of blood vessels, reduced permeability
of the blood vessels, and an increase in white blood cells in the tissue [15]. Substances in the
plasma and cells that cause blood vessels to react, such as complement components, histamine,
serotonin, fibrin, kinins, prostaglandins, and platelet-activating factors, are unlikely to have the
main influence on the immediate responses. These substances can bind to specific receptors on
the cells that line the blood vessels, causing them to contract and form gaps. As a result, the per-
meability of the blood vessels increases, and fluids from the blood leak out within 30 minutes of
an injury.

391
Temporarily, the activation of cells that line the interior of blood vessels is initiated by these sub-
stances that induce inflammation to increase the synthesis of diverse molecules that promote cellular
adhesion. These molecules can be categorized into four primary groups, characterized by their func-
tion and structure: immunoglobulins, integrins, selectins, and cadherins. The cells lining the inner
walls of blood vessels display the expression of these molecules that promote adhesion. Subsequently,
these molecules aid the movement of white blood cells from the bloodstream into the damaged tis-
sues via a meticulously controlled process that encompasses various stages of attachment, rolling,
strong adhesion, and migration. The body’s IR involves diverse subgroups of white blood cells, such
as monocytes/macrophages, basophils, eosinophils, lymphocytes, and primarily neutrophils, as well
as mast cells. Neutrophils, which constitute a significant proportion of white blood cells in the blood-
stream (50–70%), play a significant role in the immediate response to inflammation. They perform a
number of tasks, including creating extracellular neutrophil traps, generating reactive oxygen species
(ROSs), and triggering and controlling the immune systems of the body [16, 17]. Monocytes can
migrate toward inflamed areas and transform into macrophages, which depends on the severity and
duration of the injury and different substance activations. These macrophages, along with invading
macrophages and their cytokines, are present in the tissues and play a crucial role in maintaining tis-
sue and host equilibrium during the acute phase response of inflammation.
Furthermore, peripheral acute inflammatory and immune reactions rarely lead to acute inflam-
mation in affected tissues, as short and controlled macrophage responses are linked to various
Initiation and filteration
Acute phase
Blood capillary
Endothelial cell
Migration
Tethring rolling
Phagocytosis
Digestion
Phagocytosis with
macrophage
scavanger receptor
and Ox-LDL
MMPs
TNF-alpha
ROS
ROS cathepsin
Firm adhesion
Diapedesis
Chronic phase
Figure 17.1 Inflammation and environment of inflammation with acute and chronic phases in connective
tissues.

392
noninfectious and infectious diseases [18]. Therefore, macrophages have been extensively
researched as potential targets for visualizing and treating various chronic inflammatory condi-
tions [19]. However, eosinophils, basophils, and mast cells are unlikely to play a significant role in
allergies, parasite-related inflammation, hypersensitivity, and asthma responses[20]. Consequently,
associated inflammatory conditions attack these cells. In addition to serving as therapeutic targets,
these unique inflammatory effector cells can absorb various substances and migrate to inflamma-
tory sites, making them interesting “Trojan Horses” for the targeted delivery of various molecules
and particulate therapies to inflammatory sites [20, 21].
17.2.2 Acidosis
Acidification occurs locally, which is associated with chronic and acute inflammations. In com-
parison to healthy tissues, the regions affected by inflammation generally display low-pH levels.
As an example, the pH in the area of cardiac ischemia ranges between 6.6 and 6.1 [22], whereas
levels ≥4.2 have been documented in the discontinuity microenvironment [23]. In the situation of
patients who have acute asthma, their inhaled vapor that condensed exhibited a pH of 5.2, in con-
trast to a pH of 7.8 in control individuals who were in good health [24]. Regular synovial fluid typi-
cally has a pH ranging from 7.4 to 7.8, whereas synovial fluid in arthritic joints decreases to a
range of 6.7–7.3 [25, 26]. The degree of the sickness and its inflammatory activity are related to
this pH drop. Consequently, this results in glycolysis that is independent of oxygen and an increase
in lactic acid production [27]. When the condensed breathed vapor from patients with acute
asthma cleared, it had a pH of 5.2 as opposed to 7.8 in healthy control individuals [28].
Consequently, this leads to glycolysis that is not dependent on oxygen and an increase in lactic
acid production [27].
During an infection, acidification in the spaces between cells can be contributed by bacterial
metabolites like lactate [29, 30]. Inflammatory lung diseases, including asthma and COPD, can
result in respiratory acidosis due to accidental inhalation of acids present in the air and pollution
from fog. Extended acid exposure can exacerbate acidosis due to varying airway pH controls,
which is a major risk factor for the development of bronchitis and asthma. By acting as warning
signs and regulating the production and release of extracellular acidosis, inflammatory mediators
alter the intrinsic properties of different immune and inflammatory cells [31, 32]. Furthermore,
delivery systems that display acid-triggered release behaviors for various medicines may be
designed using acidosis as an internal signal. It is important to note that, apart from these patho-
logical situations, various cells, organs, and tissues in the body have different levels of acidic envi-
ronments under regular physiological circumstances. For example, the gastrointestinal tract’s pH
gradient, which ranges from 5.6 to 7.6 in the colon to 1.1–3.6 in the stomach, is well-regulated [33].
To treat inflammatory disorders, a variety of pH-responsive biomaterials and delivery devices have
been created.
17.2.3 Increased Oxidative Stress in Tissues
ROS are chemical compounds that contain oxygen and display reactivity. Some examples of
typical ROS consist of hydrogen peroxide (H
2
O
2
), hypochlorous acid (HOCl), singlet oxygen
(
1
O
2
), hydroxyl radical (
•
OH), and superoxide anion radical (•O
2
−
). Physiological levels of ROS
play a significant role in the maintenance and regulation of cellular functions, like prolifera-
tion, survival, migration, and cell differentiation. Furthermore, ROS are important for the
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