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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

6.1 ADMET 133
process of discovering, developing, and designing potential drug candidates. To better understand a
compound’s pharmacokinetic properties, optimize PK parameters, and choose the most drug-like
compounds for development, in vitro biotransformation experiments are an important tool [85, 86].
6.1.4 Excretion
The process by which toxic compounds and/or their metabolites are transported from the body to
the environment outside the body in an irreversible manner is referred to as excretion. Therefore,
excretion is one of the key systems that protect the body from the poisonous effects of toxicants. The
process of excretion allows for the removal of these substances from the body. This process decreases
the concentration of the drug at its specific location, where it exerts its effects. A gradual elimination
mechanism facilitates accumulating the required drug concentration to sustain the desired
therapeutic effects. The two main forms of drug metabolites that are excreted from the body are their
unaltered forms and their structurally distinct forms. The energy-dependent transport pathways that
regulate drug clearance by the kidneys and liver are discussed in this section. The kidney plays a
crucial role in eliminating drugs and their metabolites from the body by excreting them through
urine. Renal clearance is the kidneys’ process of getting rid of drugs through urine. It is composed of
multiple components, including active tubular secretion, passive and active glomerular filtration,
and reabsorption [87]. In the process of glomerular filtration, an ultrafiltrate of the body’s plasma is
produced. This ultrafiltrate contains foreign substances and their metabolites in amounts that are
roughly equivalent to those that are found in the blood. After filtration at the glomeruli, the molecule
can either be ejected or passively reabsorbed into the bloodstream by crossing the tubular cells of the
nephron. The chemicals that are reabsorbable are not very common in urine. However, lipid-
insoluble ionized molecules, like conjugates of foreign chemicals, are less able to be reabsorbable
and are therefore more easily flushed out of the body by the kidneys than their precursors [65].
Transport mechanisms across membranes are involved in both passive and active tubular secretion
and reabsorption. Drug filtering is a passive process that occurs through diffusion. Drug excretion by
tubular secretion is facilitated by many active transporters that uptake substances from the
interstitium of the kidney and release them into the tubular lumen [88]. The majority of renal
transporters comprise OAT1, OAT3, OCT2, MATE, P-gp, and BCRP. These transporters play an
active role in facilitating the elimination of xenobiotics from the bloodstream into the urine [59].
The rate of filtration and secretion can be added to determine the renal excretion of medicines, and
the rate of reabsorption can be subtracted. The amount of medication expelled over a given period
can be divided by the time interval and the average plasma concentration to calculate renal
clearance [69].
CL
R
mL
excreted amount time interval
plasma concentrat
/min
iiion
The liver has developed a number of different systems that allow it to take in nutrients and
endogenous substances that are essential to its function and to the maintenance of homeostasis in
general. An excretory pathway exists in the liver for the removal of nonionized compounds that
contain lipophilic and polar groups. Furthermore, apart from the aforementioned transport systems
for organic chemicals, the liver likely possesses an additional transport mechanism specifically
designed for the elimination of metals. Efflux mechanisms are present on the canalicular and
sinusoidal surfaces of hepatocytes, and they have developed to detoxify and eliminate byproducts or
metabolites. Multiple transporters are accountable for the absorption of medicines into the

6 ADMET and Physicochemical Assessments in Drug Design134
hepatocytes through the sinusoidal (basolateral) membrane [89, 90]. The OATP (SLC21 family)
transporters facilitate the transportation of endogenous compounds, including thyroid hormones
and bile acids. These transporters have demonstrated the ability to transport drugs such as
fexofenadine, rifampicin, pravastatin, and rifamycin. Additional uptake transporters comprise the
organic anion transporters (OATs) and organic cation transporters (OCTs) belonging to the SLC22
family, as well as the sodium taurocholate co-transporting polypeptide (NTCP) encoded by the
SLC10A1 gene, and the peptide transporter (PGT) encoded by the SLC21A2 gene [91].
CL
B
mL
bile concentration bile flow
plasma concentrati
/min
ooon
Liver cells have the ability to discharge chemicals into bile, which then enters the small intes-
tine. A process known as the enterohepatic cycle can occur when substances or their metabolites
have characteristics that facilitate intestinal reabsorption. This cycle continues with biliary secre-
tion and intestinal reabsorption until the compounds are excreted from the body by the kidneys.
Biliary clearance can be determined by monitoring the rate of bile flow and the concentration of
drugs in both plasma and bile. When a drug’s concentration in bile is significantly higher than its
concentration in plasma, resulting in a clearance value greater than bile flow (0.5–0.8 mL/min),
biliary clearance becomes significant [69].
6.1.5 Toxicity
During the period of development that occurs in the womb or from postnatal to adolescent, drugs
and other xenobiotics have the ability to disrupt homeostasis as well as proper growth, differentia-
tion, and developmental processes among the developing organism. The normal development of
the organism that is developing is disrupted as a result of this disorder, which could result in birth
defects, teratogenesis, structural abnormalities, and ultimately the death of the creature.
Developmental toxicity and acute oral toxicity are assessed by numerous international regulatory
agencies. An estimated 100 million rodents are subjected to toxicity studies every year, with the
majority of these animals belonging to the cosmetics and pharmaceutical industries [92]. Because
of this, drug toxicity is a significant challenge in the process of drug development. The following
criteria must be satisfied in order to conduct an in vitro assessment of the toxicity of a drug:
i) Human-derived cells should be utilized in the process of determining the toxicity of drugs
to humans.
ii) It is recommended that cells generated from the organ in issue, such as hepatocytes, be utilized
in order to assess the level of toxicity that is exhibited by particular organs, such as the liver.
iii) Various cell types can be found in an organ. When dealing with particular kinds of organ toxic-
ity, it is important to use the ideal target cell.
iv) The testing panel may also include enzyme-based and receptor-binding assays as additional
criteria.
v) Toxicogenomics is a highly effective method for identifying drug toxicity in people due to its
comprehensive assessment of several causes of drug toxicity. As a result of the fact that toxi-
cogenomics, which is the evaluation of the effects of drugs on the expression of genes that are
toxicologically relevant, enables the simultaneous evaluation of multiple toxicity mechanisms,
it is most likely to be useful for toxicity evaluation, particularly for the elucidation of mecha-
nism and, consequently, significance [93–95].

6.2 Physicochemical Assessments 135
6.2 Physicochemical Assessments
6.2.1 Partition Coefficient
Lipophilicity is regarded as a primary physicochemical descriptor in several contexts, including
drug absorption, plasma protein binding, hydrophobic drug–receptor interactions, and, to some
extent, the pharmacokinetic behavior and toxicological characteristics of drug molecules, as well
as formulation attributes like solubility [96].
The partition coefficient, which measures the fat-soluble tendency of the drug, is one of the most
important factors affecting its activity. The partition coefficient (P), defined as the ratio of the con-
centrations of a drug in equilibrium in two immiscible phases (polar aqueous phase and nonpolar
organic phase), is formulated as follows:
P
A
A
octanol
water
The above equation is the general partition coefficient equation based on octanol and water. The
relative lipophilicity of a medicine can be determined by measuring the octanol–water partition coef-
ficient. Octanol was partly chosen for the octanol–water system due to its flexibility and polar head
and nonpolar tail resemblance to biological membrane components. Therefore, it is believed that a
medication’s propensity to exit the aqueous phase and partition into octanol is a measure of how well
the drug will diffuse through and partition into biological barriers like the intestinal membrane [97].
Lipophilicity is the result of all intermolecular interactions involved in the partitioning of a solute
between two phases, a property that combines contributions from polarity as well as hydrophobicity,
which is the tendency of nonpolar groups or molecules to associate in an aqueous environment [98].
Currently,
0/
log
P
is arguably the most successful and instructive physicochemical property uti-
lized in medicinal chemistry; it is a critical component of many quantitative structure–activity
relationships (QSAR) created for pharmacological, environmental, biochemical, or drug design
applications. It has been widely used to explain drug–receptor and drug–biological membrane
interactions [99].
A compound’s physicochemical characteristics, such as its lipophilicity, are closely correlated
with its chemical structure and can be quantitatively linked to its pharmacological activity. The
change in the log P value as a result of the replacement change is displayed in Figure 6.2. Therefore,
both the drug’s physicochemical and biological qualities are determined by its structure [100].
If we look at the research done by Meyer and Overton, they established a linear relationship,
within certain bounds, between the lipophilic nature of a group of related medications and their
biological activity.
Benzene
(Log P = 2.13)
Cl
Cl
CONH
2
CONH
2
Chlorobenzene
(Log P = 2.84)
Benzamide
(Log P = 0.64)
Chlorobenzamide
(Log P = 1.51)
Figure 6.2 Impact of substitution on a molecule’s logP value.

6 ADMET and Physicochemical Assessments in Drug Design136
log / . log .1 0 94 087C P
This formula expresses the link between the octanol–water partition coefficient and the molar
concentration (C) that causes isonarcosis as a linear free energy relationship [101].
The groups a substance contains determine its lipophilic; compounds with high partition
coefficients are more lipophilic. The partition coefficient value rises as groups that offer the
molecule lipophilic characteristics are added. Hansch established the hydrophobic bond constant,
which is the numerical representation of the substituent’s contribution to the partition coefficient,
to establish quantitative correlations between activity and the partition coefficient. In the
equilibrium below, Hammett σ values are used to monitor electronic effects, Taft E values are
employed to monitor steric effects, and 1-octanol–water log P uses σ to indicate the difference in
log P between the substituted and parent molecules.
log /1
2
C a b
cE
s
where π is determined by the variation in log P between the substituted compound and the parent,
and σ is the Hammett constant for the substituent’s electronic impact [102].
log logP P
x H
6.2.2 Log D: Ionizable Compound Lipophilicity
When characterizing the lipophilicity of possible medications, it is important to include the chemi-
cal’s ionic state because a molecule’s ionization reduces lipophilicity relative to the neutral
state [103]. A discernible difference exists between the distribution ratio D, determined for the
total analytical concentration, and the partition coefficient P, which represents the partition of
neutral species between octanol and water.
P
HA
HA
D
HA A
HA A
o
o
o
the concentrations of neutral and anionic species are represented by [HA] and [A
−
], where the sub-
scripts o and stand for the concentrations in the aqueous and octanol phases, respectively [104].
P and D are the same since there are only neutral species in the case of nonionizable compounds.
In the pH range where ionic species exist, D should be used instead of P for ionizable chemicals.
For example, it was shown that the “Rule of 5” can be significantly affected when employing log D
rather than log P. But in many systems, ionic species go into the nonaqueous phase, necessitating
a more sophisticated strategy [105].
6.2.2.1 Methods for Calculating Lipophilicity
For medicinal chemists, determining lipophilicity has become a standard procedure [106]. Two
types of experimental procedures are used to measure lipophilicity: direct methods and indirect
approaches. When utilizing direct experimental techniques, the concentration ratio in the
equilibrium of a substance divided into the nonaqueous and aqueous phases is utilized to

6.2 Physicochemical Assessments 137
calculate the partition coefficient directly. Through correlations, such as those between a
compound’s partition coefficient and retention factor in a reverse-phase chromatographic
system, the partition coefficient is calculated via indirect experimental approaches. Liquid
chromatography (LC) is a significant tool for directly and indirectly determining
lipophilicity [107].
6.2.2.2 Direct Experimental Determination of Lipophilicity
In silico determination of lipophilicity: Numerous silico techniques have been developed to
predict lipophilicity from a molecule’s molecular structure since the seminal work of Hansch et al.,
who discovered that the partition coefficients had an additive constitutive nature [108]. In silico
calculations are made based on a set of experimental data and evaluated with predicted results.
Sometimes, the calculated log P value may need to be corrected. Additionally, in silico methods do
not draw definitive conclusions as there can be differences of up to two different log P values
depending on the method used [109].
Shake-flask method: Quantification of the chemical concentration in the aqueous and non-
aqueous phases is necessary for the direct assessment of lipophilicity. The basic process applied in
determining the partition coefficient is based on shaking a certain amount of chemical compound
with measured amounts of water-saturated octanol and octanol-saturated water or buffer adjusted
to pH 7.4. After sufficient time for the equilibrium between the two phases to be established,
the amount of substance in one or both phases is measured by an appropriate analytical method.
The ratio of the substance concentration in the octanol phase to the substance concentration in the
aqueous phase gives the partition coefficient. Although various analysis methods can be used to
determine the amount of substance in the phases, the most preferred is the spectrophotometric
method. Since P values are generally quite large, logarithmic P values are used and the partition
coefficient equation is written as follows:
log log / log logP C C C C
octanol water octanol water
C
octanol
and C
water
= drug concentration in the octanol and water phase at equilibrium [110, 111].
One disadvantage of this approach is that it takes a long time to attain equilibrium (up to two or
three days) while stirring [112].
6.2.2.3 Indirect Experimental Determination of Lipophilicity
The need to create techniques that can estimate lipophilicity without requiring measurement is
growing as a result of the drawbacks of conducting direct trials. In addition to being more accurate
and versatile than direct procedures, these techniques provide higher throughput and environ-
mental benefits [113].
Reversed-phase thin layer chromatography: In reversed-phase systems, the partitioning
between the stationary and mobile phases primarily controls retention. Therefore, the RM value
may be used to calculate the lipophilicity index as determined by RP-TLC.
RM
f
log
1
1
R
where R
f
, the retention factor, is calculated by dividing the sample’s distance traveled by the mobile
phase’s distance traveled [114].

6 ADMET and Physicochemical Assessments in Drug Design138
Reversed-phase high-performance liquid chromatography: In order to measure
lipophilicity experimentally, reversed-phase high-performance liquid chromatography (RP-HPLC)
is the most used indirect approach. The OECD has officially suggested a standard protocol for
determining log P [115].
Programs for Calculation of Log P The initial methods to calculate lipophilicity were fragmental
approaches, which involved splicing molecules into suitable segments and using correction criteria
in conjunction with molecular connectivity. Atom-based techniques, on the other hand, do not
require correction factors and specify an enormous number of atom-types; lipophilicity is simply
measured by adding up the values of each atom-type. A satisfactory treatment of the effect of
stereochemistry on lipophilicity is not provided by fragmentary and atom-based methods.
Therefore, the most recent generation of calculation techniques aims to supply the user hydrophobic
fields for use in 3D QSAR and to reflect conformational features. The calculating techniques and
programs are briefly outlined in the following based on the subclassification above [116] (Tables6.1
and 6.2).
6.2.3 Acid–Base Properties and Ionization
The acid–base ionization constant is an essential physicochemical parameter of a compound.
Because it has an impact on biological activity, absorption, distribution, metabolism, and excretion
(ADME), it is particularly important in medicinal chemistry [117–119].
According to Brønsted and Lowry, substances that donate protons are acids, and substances that
accept protons are bases [120]. pK
a
and pK
b
, as ionization coefficients, indicate that an acid donates
a proton and a base accepts a proton [121] (Figure 6.3).
Table 6.1 Programs based on fragmental methods.
Program Method Platform Commercial
∑f-SYBYL Rekker, revised version Unix Tripos
PCMODELS Hansch/Leo Unix Daylight
CLGOP Hansch/Leo PC, Mac, Vax BioByte
∑f Rekker — —
PROLOGP_cdr Rekker PC Compudrug
Table 6.2 Programs based on atomic contributions.
Program Method Platform Commercial
GLGOP Atomic fragments — —
ATOMIC5 Atomic values PC Compudrug
Tsar 2.2 Atomic values Unix Oxford molecular
SMILOGP Atomic contributions PC —
MOLCAD Atomic values PC —

6.2 Physicochemical Assessments 139
Two parameters determine a drug’s degree of ionization in solution: pH and pK
a
. It is understood
whether a compound is a strong or weak acid or base by its pK
a
value [122, 123].
pK
a
< 2: Strong acid
pK
a
4–6: Weak acid
pK
a
8–10: Very weak acid
pK
a
> 12: strong base
Generally, the functional groups determine the pK
a
of a drug molecule. A molecule may have
more than one pK
a
if it has more than one functional group. A chemical with several functional
groups, like ciprofloxacin, can have both basic and acidic characteristics [122] (Figure 6.4).
At physiological pH, 95% of pharmaceutical substances ionize to some degree. The majority of
medications have weak bases or weak acids. Permeability and solubility, two characteristics fre-
quently utilized in pharmaceutical research to forecast the pharmacokinetic profile of a molecule,
are controlled by the degree of ionization [122, 123].
The certain equation calculates the concentration of ionized and nonionized forms of a drug
with known pK
a
at a Henderson–Hasselbach pH [110, 124].
For weak acid: pH–pK
a
= log [i]/[ni]
For weak base: pH–pK
a
= log [ni]/[i]
Weak acids will be more unionized at pH values below their pK
a
and more ionized at pH values
above their pK
a
. For weak bases, the opposite is true [100] (Figure 6.5).
The solubility, medication absorption, and distribution are significantly impacted by the pK
a
value due to the assumption that only neutral species can pass through the lipophilic
Figure 6.3 Acid and base equations.
Basic group
Ciprofloxacin
F
N
O O
N
HN
Acid group
OH
Figure 6.4 Acidic and basic groups of ciprofloxacin.

6 ADMET and Physicochemical Assessments in Drug Design140
membrane [125]. With some exceptions, drugs pass through nonpolar membranes such as cell
membranes and the BBB in a nonionized manner. They generally show their effects in an ionized
form through electrostatic interactions such as ionic and ion–dipole bonds, as seen in drug–receptor
interactions. Therefore, the environment’s pH determines the substance’s ionized and nonionized
state, its passage through cell membranes, and the compound’s activity. Accordingly, acidic drugs
are generally more active at low pH. Because they will be nonionized at higher rates at these pHs,
their passage through membranes, that is, their absorption, will be easier, and the amounts reach-
ing the site of action will be higher [110, 126]. This also explains why organic acids and bases make
up so many medications. A neutral chemical, an acid, or a base can be well absorbed at a certain
point in the gastrointestinal tract due to the significantly different pH values in the stomach and
intestines. Absorption may become troublesome if the pK
a
values are excessively high compared
to the physiological pH values, such as amidines or guanidines. This also holds for molecules
with several acidic or basic groups inside their structure, such as amino acids and zwitterionic
chemicals [127].
Figure 6.6 illustrates how the zwitterionic molecule ciprofloxacin changes in the gastrointestinal
tract. The strongest base in stomach fluid is the secondary amine, which can take up a proton. The
carboxylic acid is the most acidic functional group in the duodenum; it produces a carboxylate
anion by giving off a proton. A zwitterion is a molecule that has both positive and negative charges.
In the duodenum, ciprofloxacin is a zwitterion [122].
6.2.4 Solubility
A solid material’s solubility is defined as the concentration at which, at a certain temperature and
pressure, the solution phase and a particular solid phase are in equilibrium [124]. A growing
number of recently developed pharmaceuticals have low aqueous solubility, inadequate and
variable bioavailability, and gastrointestinal mucosal irritation. These characteristics are anticipated
Weak asidic drug
Weak basic drug
Higher pH
Slower
dissolution
Slower
dissolution
Higher pH
Figure 6.5 The impact
of pH on weak bases
and acids.
Figure 6.6 Chemical change of ciprofloxacin depending on pH.

6.2 Physicochemical Assessments 141
to present difficulties when these medications are developed into oral medicinal formulations.
For medications taken orally, solubility is the most important rate-limiting component because
it enables the drug to reach the appropriate concentration in the systemic circulation for a
pharmacological reaction. Low solubility of the pharmaceutically active constituent suggests
a greater likelihood of medication development and innovation failure. In addition,
pharmacodynamics, pharmacokinetics, drug distribution, absorption, and protein binding are all
adversely affected by poor solubility, which has a substantial impact on drug delivery. Furthermore,
poorly soluble medicines reduce the rate of in vitro dissolution and have a major negative effect on
drug delivery [128, 129].
Solubility is affected by the properties of the solvent and many molecular parameters [121]
(Figure 6.7). These:
pK
a
: This is important for the solubility of drugs. It may affect a drug’s solubility in water. The
charge state of the drug molecules can be changed by adjusting the pH of the solution. If the pH of
the solution is such that a particular molecule bears no net electric charge, the solute frequently
has limited solubility and precipitates out of the solution [130]. Variations in gastrointestinal pH
brought on by food intake can dramatically increase or reduce drug solubility in the case of weakly
acidic or basic medicines, which reside in the aqueous GI environment in ionized and unionized
form. The pH of the stomach in healthy individuals usually ranges from 1 to 3 when they are fast-
ing, but it can momentarily increase to 4–6 when they eat. Food consumption may accelerate the
dissolving of drugs in the stomach because it increases the amount of ionization and, thus, the
solubility of a weakly acidic medicine at higher pH levels. Conversely, at higher stomach pH, a
weakly basic medication’s degree of ionization will decrease, which will lead to less drug dissolu-
tion and/or possible precipitation of already-dissolved drug molecules [131, 132].
Lipophilicity: The term hydrophilic or lipophobic refers to solubility in polar environments,
and the term lipophilic or hydrophobic refers to solubility in nonpolar environments. Theoretically,
a substance can dissolve in both polar and nonpolar environments, but their degree of dissolution
is different. As a general rule, similar substances dissolve in similar solvents. In other words, polar
molecules dissolve more easily in polar solvents, and nonpolar molecules dissolve more easily in
Molecular
size
Polymorphism
pK
a
Lipophilicity
Hydrogen
bond
formation
Solubility
Figure 6.7 Factors affecting solubility.

6 ADMET and Physicochemical Assessments in Drug Design142
nonpolar solvents. Alkyl chains give the molecule oil solubility, and as the alkyl chain lengthens,
oil solubility increases. The introduction of heteroatoms such as oxygen and nitrogen or functional
groups containing them into the molecule increases the polarity of the molecule, that is, its solubil-
ity in water [110].
Hydrogen bond formation: While polar compounds that can make HBs with water molecules
dissolve in water, Van der Waals and hydrophobic bonds cause nonpolar molecules to dissolve in
organic solvents [110].
Molecular size: The drug’s particle size determines how soluble it is. Because they have a lower
surface area, large particles interact with the solvent less. One of the approaches to increase the
drug’s surface area is to minimize its particle size, which improves its dissolving property [130, 133].
Polymorphism: A polymorph is a substance that has a different crystal structure but the same
chemical content. As a result, polymorphs exhibit various physicochemical properties because of
their distinct network architectures and molecular conformations. Many medications can crystal-
lize into different polymorphic forms to boost solubility through a frequent phenomenon called
polymorphism [130].
One way to find solubility is to use the shake-flask method. This procedure involves adding a
component to a solvent in a known volume and shaking the mixture until equilibrium is estab-
lished. Next, using a variety of analytical methods, including high-performance LC, the concentra-
tion of the dissolved component is ascertained [134].
6.2.5 Polymorphism
When studying calcium carbonate in 1788, Klaporoth and associates first investigated polymor-
phism. “The ability of a given elementor compound to crystallize more than one distinct crystal
species” is how McCroneas described polymorphism in 1965 [135]. The ability of a drug to exist in
multiple crystal forms, or polymorphism, is widely used in the pharmaceutical sector. When a
substance is in solution, all of its polymorphs behave chemically in the same way, but when it is
solid, the behaviors can differ greatly. There may be variations in characteristics like surface free
energy, shape, solubility, and melting point [136–139].
Since they include solvent molecules that are integrated into the crystal lattice in addition to the
specific medicinal molecule, hydrates, and solvates are frequently referred to as “pesudopoly-
morphs.” Drug compounds can crystallize into a variety of polymorphs and pseudopolymorphs
quite frequently [124].
One of the known examples of differences in solubility of polymorphs is Ritonavir, the antiretro-
viral drug used in the treatment of HIV. It was discovered by Abbott Laboratories in 1992, and the
FDA approved it in 1996 for use in both liquid and semisolid capsule formulations. However, in
1998, the product failed the dissolution test. The novel polymorph Form II, which is less soluble
(Table 6.3) and thermodynamically more stable than Form I, was identified as the precipitated
Table 6.3 Solubility of Ritonavir in ethanol/water (mg/mL).
Ethanol/water 100/1 75/25
Form I 90 mg/mL 170 mg/mL
Form II 19 mg/mL 30 mg/mL
Source: Brog et al. [140]/with permission of Royal Society of Chemistry.
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