Software and Programming Tools in Pharmaceutical Research E-Book

• Herausgeber: Bentham Science Publishers
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2024

Seitenzahl: 559

Beschreibung

Software and Programming Tools in Pharmaceutical Research is a detailed primer on the use for computer programs in the design and development of new drugs. Chapters offer information about different programs and computational techniques in pharmacology. The book will help readers to harness computer technologies in pharmaceutical investigations. Readers will also appreciate the pivotal role that software applications and programming tools play in revolutionizing the pharmaceutical industry.

The book includes nine structured chapters, each addressing a critical aspect of pharmaceutical research and software utilization. From an introduction to pharmaceutical informatics and computational chemistry to advanced topics like molecular modeling, data mining, and high-throughput screening, this book covers a wide range of topics.

Key Features:
· Practical Insights: Presents practical knowledge on how to effectively utilize software tools in pharmaceutical research.
· Interdisciplinary Approach: Bridges the gap between pharmaceutical science and computer science
· Cutting-Edge Topics: Covers the latest advancements in computational drug development, including data analysis and visualization techniques, drug repurposing, pharmacokinetic modelling and screening.
· Recommendations for Tools: Includes informative tables for software tools
· Referenced content: Includes scientific references for advanced readers

The book is an ideal primer for students and educators in pharmaceutical science and computational biology, providing a comprehensive foundation for this rapidly evolving field. It is also an essential resource for pharmaceutical researchers, scientists, and professionals looking to enhance their understanding of software tools and programming in drug development.

Readership
Pharmaceutical researchers, scientists, and professionals; students and educators in pharmacology and computational biology.

Leseprobe

Table of Contents

BENTHAM SCIENCE PUBLISHERS LTD.

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General:

PREFACE

List of Contributors

Introduction to Computer-Based Simulations and Methodologies in Pharmaceutical Research

Abstract

1. INTRODUCTION

1.1. Types of Computer-Based Simulations in Pharmaceutical Research

1.1.1. Challenges and Limitations of Computer-Based Simulations

1.1.2. Advances in Computer-Based Simulations

2. MOLECULAR MODELLING: PRINCIPLES AND APPLICATIONS IN DRUG DISCOVERY

2.1. Principles of Molecular Modelling

2.2. Applications of Molecular Modelling in Drug Discovery

2.3. Molecular Modeling Techniques

3. COMPUTER-AIDED DRUG DESIGN: CONCEPTS AND TECHNIQUES

3.1. Principles of Computer-Aided Drug Design

3.1.1. Virtual Screening

3.2. Applications of Computer-Aided Drug Design in Drug Discovery

3.2.1. Computer-Aided Drug Design Techniques

3.2.2. Molecular Docking: Predicting Protein-ligand Interactions

3.2.2.1. Principles of Molecular Docking

3.2.2.2. Applications of Molecular Docking in Drug Discovery

3.2.3. Quantitative Structure-Activity Relationship (QSAR) Modeling

3.2.3.1. Principles of QSAR Modelling

3.2.3.2. Applications of QSAR Modelling in Drug Discovery

3.2.4. Virtual Screening: Accelerating Drug Discovery Through Computational Techniques

3.2.4.1. Principles of Virtual Screening

3.2.4.2. Applications of Virtual Screening in Drug Discovery

3.2.5. Standardization of Methods for Data Collection and Analysis

3.2.5.1. Validation of Computational Models Using Experimental Data

3.2.5.2. Interdisciplinary Collaboration

CONCLUSION

ACKNOWLEDGEMENT

REFERENCES

Tools for the Calculation of Dissolution Experiments and their Predictive Properties

Abstract

1. INTRODUCTION

2. THEORIES OF DISSOLUTION

2.1. The Diffusion Layer Model

2.2. The Interfacial Barrier Model

2.3. Danckwert’s Model

3. IVIVC (IN VITRO - IN VIVO CORRELATION) STUDIES

4. EXPERIMENTAL TECHNIQUES FOR DISSOLUTION

5. FUNDAMENTALS OF DISSOLUTION TESTING

5.1. Signifance of Dissolution Testing

5.2. Product Stability

5.3. Comparability Assessment

5.4. Noyes-Whitney Rule

5.5. Nernst and Brunner Film Theory

6. MATHEMATICAL AND STATISTICAL TOOLS FOR IN VITRO DISSOLUTION METHODS

6.1. Kinetics in Dosage Form

6.2. Empirical and Semi-empirical Mathematical Modeling

6.2.1. Zero order Kinetics

6.2.2. First-order Kinetics

6.2.3. Higuchi Model

6.2.4. Hixson- Crowell Model

6.2.5. Korsmeyer-Peppas Model

6.2.6. Baker Lonsdale Model

6.2.7. Weibull Model

6.2.8. Hopfenberg Model

7. PREDICTION OF IN VITRO DISSOLUTION STUDIES USING ADVANCED MEASUREMENTS TECHNIQUES

CONCLUSION

CONSENT FOR PUBLICATON

CONFLICT OF INTEREST

ACKNOWLEDGEMENT

REFERENCES

The Role of Principal Component Analysis in Pharmaceutical Research: Current Advances

Abstract

1. INTRODUCTION

1.1. Definition of PCA

1.1.1. Definition

1.1.2. Goals

1.1.2.1. Dimensionality Reduction

1.1.2.2. Variance Maximization

1.1.2.3. Feature Interpretation

1.1.2.4. Data Visualization

1.1.2.5. Data Compression

1.1.2.6. Data Preprocessing

1.2. History of PCA

2. Terminology in the PCA Algorithm

2.1. Dimensionality

2.2. Correlation

2.3. Orthogonal

2.4. Eigenvectors

2.5. Covariance Matrix

2.6. The PCA Algorithm's Steps

2.6.1. Getting the Dataset

2.6.2. Structure of Representing Data

2.6.3. Standardizing the data

2.6.4. Covariance of Z

2.6.5. Eigenvalues and Eigenvectors

2.6.6. Sorting the Eigenve+-ctors

2.6.7. Calculating the New Features

2.6.8. Unimportant Features from the New Data

2.7. PCA for Feature Engineering

2.7.1. Dimensionality Reduction

2.7.2. Anomaly Detection

2.7.3. Noise Reduction

2.7.4. Decorrelation

2.8. Role of Principal Component Analysis in Pharmaceutical Research

2.8.1. Covariance

2.8.2. Eigen vectors and eigen values

2.9. PCA in Drug Excipients Interaction Studies

2.10. Role of PCA in Various Pharmaceutical Fields

2.10.1. Adaptations

2.10.2. Functional PCA

2.10.2.1. Discretization

2.10.2.2. Discretization

2.10.2.3. Numerical Approximation

2.10.3. Simplified PCA

2.11. Symbolic Data Principal Component Analysis

2.11.1. Advantages of PCA

2.11.1.1. Dimensionality Reduction

2.11.1.2. Data Visualization

2.11.1.3. Noise Reduction

2.11.1.4. Multicolinearity

2.11.2. Disadvantages of PCA

2.11.2.1. Information Loss

2.11.2.2. Scaling

2.11.2.3. Outliers

2.12. Software’s used to Perform Principal Component Analysis

3. APPLICATIONS OF PCA IN PHARMACEUTCAL RESEARCH

3.1. Neuroscience

3.2. Role of PCA in Drug Discovery

3.3. Image Recognition

3.3.1. Advantages

3.4. QSAR Studies

4. MEDICAL DATA IN PCA REPOSITORIES

4.1. As Sensory Assessment Tool for Fermented Food Products

4.2. In Nanomaterials

4.3. Bimolecular Molecule Dynamics

4.4. ECG Signal Determination

CONCLUSION

REFERENCES

Quality by Design in Pharmaceutical Development: Current Advances and Future Prospects

Abstract

1. INTRODUCTION

1.1. Conventional Approach vs Design Approach

1.2. QbD Paradigm and Regulatory Authorities

1.3. Contribution of Ishikawa Diagram

1.4. Impact of FEMA on Quality Improvement

1.5. RRMA into QbD and Process Failures

1.6. Role of CQA, KPI and CPP within QbD

2. ICH Q8 Pharmaceutical Development

2.1. Overview of ICH Q8 (R2) Pharmaceutical Development

2.2. Quality by Testing Approach vs QbD Approach

2.3. QbD Elements in Pharmaceutical Development

2.4. Formulation Development

3. Quality by design (QbD) tools: application in product development

3.1. Quality Product Quality Profile (QTPP)

3.2. Critical Material Attributes (CMA’s), Critical Material Parameters (CPPs), and Critical Quality Attributes (CQA’s)

3.3. Risk Assessment

3.4. Design of Experiment (DOE) and Design Space

3.5. Control Strategy

3.5.1. Process Analytical Technology (PAT)

4. Quality-by-Design: Current Trends in Pharmaceutical Development

5. Strategies for Evaluating Risk in Pharmaceutical Production Procedures

5.1. Risk Identification

5.2. Risk Analysis

5.3. Risk Evaluation

6. QbD in Pharmaceutical Development

6.1. QbD Approach in Process Control

6.2. QbD in the Development of Analytical Methods and Pharmaceutical Manufacturing

6.2.1. QbD Approach in Chromatographic Techniques

6.2.2. Strategy for HPLC Method Development

6.2.3. Strategy for HPTLC Method Optimization and Development

6.2.4. Method Development/Optimization Strategy for U.V.

6.3. Quality-based Design for Novel Drug Delivery Systems

6.3.1. Polymeric nanoparticles

6.3.2. Polymeric micelles

6.3.3. Liposomes

6.3.4. Microemulsions and nanoemulsions

6.3.5. Solid lipid nanoparticles

6.4. QbD Approach in the Extraction of Phytochemicals & Polyherbal Formulation

6.5. QbD Approach in Green Synthesis

6.6. QbD on the Processing of Biotherapeutics

6.7. QbD Approach in Immunoassays

7. Regulatory and Industry View on QbD

CONCLUSION

REFERENCES

Virtual Tools and Screening Designs for Drug Discovery and New Drug Development

Abstract

1. INTRODUCTION

2. Concept of Drug Design

2.1. Quantitative Structure-activity Relationship (QSAR)

2.1.1. Topological Approach

2.1.2. Physicochemical Approach

2.1.3. Quantum Chemical Approach

2.1.4. Molecular Mechanics Approach

2.1.5. Hybrid Approach

2.2. 2D-QSAR

2.3. 3D-QSAR

3. Molecular Modelling

3.1. Protein Modelling

3.2. Lead Modelling

3.2.1. Lead Optimization

3.2.2. Scaffold Hopping

3.2.3. Protein Engineering

3.2.4. Virtual Screening

3.2.5. Toxicity Prediction

3.3. Software for Molecular Modelling

3.3.1. Schrödinger

3.3.2. MOE

4. Molecular Docking

4.1. Rigid Docking

4.2. Flexible Docking

5. Molecular Dynamic Simulation (MDS)

6. Virtual Screening

6.1. Types of Virtual Screening

6.1.1. Ligand-Based Screening

6.1.1.1. Similarity-based Screening

6.1.1.1.1. Selection of the Reference Compounds

6.1.1.1.2. Calculation of Molecular Descriptors

6.1.1.1.3. Calculation of Similarity Scores

6.1.1.1.4. Ranking and Validation of the Hits

6.1.1.2. Pharmacophore-based Screening

6.1.1.2.1. Development of the Pharmacophore Model

6.1.1.2.2. Preparation of the Compound Library

6.1.1.2.3. Screening of the Compound Library

6.1.1.2.4. Validation of the Hits

6.1.1.3. Machine learning-based Screening

6.1.1.3.1. Data Preparation

6.1.1.3.2. Feature Selection

6.1.1.3.3. Model Training

6.1.1.3.4. Virtual Screening

6.1.2. Structure-Based Screening

CONCLUSION

REFERENCES

Predicting Drug Properties: Computational Strategies for Solubility and Permeability Rates

Abstract

1. INTRODUCTION

2. COMPUTATIONAL MODEL FOR PREDICTING PERMEABILITY AND SOLUBILITY OF DRUG

2.1. Computational Model for Predicting Permeability of Drug

2.2. Parallel Artificial Membrane Permeability Assay (PAMPA)

2.3. Immobilized Artificial Membrane (IAM) Method

2.4. Immobilized Liposome Chromatography (ILC) Technique

2.5. Caco-2 Model for Predicting Permeability

3. SOLUBILITY PREDICTION MODEL

3.1. Data Sources

3.2. Descriptors

3.3. Model Development

3.4. Feature Selection

3.5. Validation

3.6. Applicability Domain

3.7. Data Quality

3.8. External Validation

3.9. Computer-aided Drug Discovery using Ligands (LB-CADD)

3.10. Quantum Mechanics

3.11. Quantum Mechanical Methods

3.11.1. ADF COSMO-RS Program

3.11.2. MFPCP Method

3.11.3. QSAR Method for Solubility Prediction of Drug

3.11.4. QSPR Technique for Solubility Prediction of Drug

3.12. Critical Factors Affecting Solubility and Permeability

3.12.1. Factors Affecting the Solubility of the Drug

3.12.2. Factors Impacting Drug Permeability

3.12.3. Relationship Between the Drug's Solubility and Permeability

3.12.4. Solubility and Permeability Impacts on Drug Bioavailability

CONCLUSION

ACKNOWLEDGEMENT

REFERENCES

Pharmacokinetic and Pharmacodynamic Modeling (PK/PD) in Pharmaceutical Research: Current Research and Advances

Abstract

1. INTRODUCTION

2. Pharmacokinetic models

2.1. Methods of Pharmacokinetic Study on Experimental Data

2.1.1. Non-compartmental Analysis (NCA)

2.1.1.1. Calculation of Area Under the Curve (AUC)

2.1.1.2. Calculation of Clearance (CL)

2.1.1.3. Calculation of Volume of Distribution (Vd)

2.1.1.4. Calculation of Half-Life (t½)

2.1.2. Compartmental Modeling

2.1.2.1. One-compartment Model

2.1.2.2. Multi-compartment Model

2.1.3. Population Pharmacokinetic Modeling

2.1.3.1. Data Collection

2.1.3.2. Model Development

2.1.3.3. Parameter Estimation

2.1.3.4. Model Validation

3. Methodology of PK/PD modeling simulation

3.1. Non-compartmental and Compartmental Pharmacokinetic Analysis

3.1.1. Population Approach

3.2. Approahes for popPK Data

3.2.1. Two-Stage Approach

3.2.2. Nonlinear Mixed-Effects Method (NLME)

3.3. Bayesian Method

3.3.1. Prior Distribution

3.3.2. Likelihood Function

3.3.3. Bayesian Inference

3.3.4. Posterior Distribution

3.3.5. Parameter Estimation

3.3.6. Model Validation

3.3.7. Predictions and Decision Making

4. Applications of modeling and simulation in drug development

4.1. Preclinical Development

4.2. Clinical Development

4.3. Lifecycle Assessment

5. Regulatory Aspects

Future Prospectives and Conclusion

REFERENCES

Experimental Tools as an “Alternative to Animal Research” in Pharmacology

Abstract

1. INTRODUCTION

2. BRIEF HISTORY OF ANIMAL RESEARCH IN PHARMACOLOGY

2.1. Overview of Alternative Experimental Tools and Techniques

2.1.1. Cell Cultures

2.1.2. Computer Modeling and Simulation

2.1.3. Microfluidic Sevices

2.1.4. Human Tissue Samples

2.1.5. In vitro Assays

2.1.6. Epidemiological Research

2.1.7. Non-invasive Imaging Techniques

2.1.8. High-throughput Screening

2.2. Need for Alternative Methods to Animal Research in Pharmacology

2.2.1. Benefits of Using Alternative Methods

2.2.1.1. Reduced Reliance on Animal Testing

2.2.1.2. Greater Accuracy and Dependability of Results

2.2.1.3. Research that is Quicker and more Cost-effective

2.2.1.4. More Ethical Research

2.2.1.5. The Safety and Effectiveness of Medications and other Chemicals

2.2.1.6. More Sustainable Research Practices

2.3. Ethical Concerns and Criticisms of Animal Research

2.4. In vitro Methods for Alternative to Animal Research

2.4.1. Types of In vitro Models

2.4.1.1. 3D Models of Cells and Tissues

2.4.1.2. Organoids

2.4.1.3. Artificial Membranes

2.4.1.4. 3D Tissue Models

2.4.1.5. Organ-On-a-Chip

2.4.1.6. Cell Cultures/Tissue Cultures

2.4.1.7. Tissue Engineering

2.4.1.7.1. Applications of Tissue Engineering

2.4.1.7.2. Advantages and Disadvantages of Tissue Engineering

2.4.2. Advantages and Disadvantages of In vitro Methods

2.4.3. In vitro Models Applied in Pharmacological Research

2.4.4. Comparison with Animal Studies

3. In Silico Methods (Computer Modeling And Simulation)

3.1. Animal Eelfare

3.2. Cost and Time

3.3. Molecular Modeling and Simulations

3.3.1. Quantitative Structure-Activity Relationship (QSAR)

3.3.2. Virtual Screening

3.4. Applications of Computer Modeling and Simulation in Pharmacological Research

3.4.1. Drug Development

3.4.2. Drug Repurposing

3.4.3. Molecular Docking

3.4.4. In Silico Imaging in Clinical Trials

3.5. Advantages and Limitations of In Silico Methods

3.6. Comparison with Animal Studies

4. In vivo Non-Animal Methods (Human-Based Methods)

4.1. Significance of Micro-Dosing

4.2. Advantages of Micro-dosing

4.3. Human-Based Methods Employed in Pharmacological Research [8]

4.3.1. Microbiological Systems

4.3.2. Tissue/Organ Culture Preparation

4.3.3. Human Dopaminergic Neurons

4.3.4. Plant Analysis

4.3.5. Stem Cells in Toxicological Research

5. Emerging Experimental Tools

5.1. Microfluidic Devices

5.2. Organ-on-a-chip Technology

5.2.1. Dynamic Mechanical Stress

5.2.2. Fluid Shear

5.2.3. Concentration Dradients

6. Regulatory Perspectives

6.1. Current Regulatory Guidelines

6.2. Challenges to Regulatory Acceptance and Implementation

6.3. Recommendations for Overcoming these Challenges

6.4. Future Directions for Regulatory Framework and Validation

CONCLUSION

ACKNOWLEDGEMENT

REFERENCES

Newer Screening Software for Computer Aided Herbal Drug Interactions and its Development

Abstract

1. INTRODUCTION

2. HDI estimations

2.1. Current Approaches

2.2. Limitations of Current Strategies

2.3. Databases and Web Services

2.3.1. Drugbank

2.3.2. Supertarget

2.3.3. Database of Therapeutic Targets

2.4. TDR Methods

2.5. MATADOR

2.6. PDTD

2.7. Integrity

2.8. FAERS

2.9. ZINC

2.10. SIDER

2.11. ChemBank

2.12. CanSAR

2.13. The IUPHAR/BPS Pharmacology Manual

2.14. DCDB

2.15. DINIES

3. Digital Modeling and Simulation

3.1. HDI Data Accessed Without Cost

3.1.1. Chi Mei's Indexing Service (CMSS)

3.1.2. SUPP.AI

3.1.3. Information Stored in the PHYDGI Database

3.2. Commercially-available HDI Databases

3.2.1. Database of Drug Interactions at UW (DIDB)

3.2.2. A One-Stop Source for Natural Health Products (NMCD)

3.2.3. Herbal Drug Interactions from Stockley's (SHMI)

3.3. HDI Screening: Cutting-edge Intelligent In silico Methods

3.3.1. In silico Forecasting HDIs

3.3.2. Lexicomp Drug Interactions (LDI)

3.3.3. Literature Excerpt

3.3.4. Frequency, Liability and Sustainability of Content Updates.

3.3.5. Interfaces for Users

3.3.6. AI's Role in Creating HDI Databases

3.3.7. HDI Database Development Going Forward

CONCLUDING REMARKS

ACKNOWLEDGEMENTS

REFERENCES

Deliberations and Considerations of Mesodyn Simulations in Pharmaceuticals

Abstract

1. INTRODUCTION

2. Mesoscale Simulations in Pharmaceutics

2.1. Theoretical Background of Mesodyn

2.1.1. Dynamical Considerations

2.1.2. Numerical Considerations

2.1.3. Order parameters

3. Properties of Mesodyn

3.1. Aggregation and Coagulation

3.2. Phase Morphology

3.3. Effect of Confinement on Miscibility

3.4. Effect of Shear on Morphology

3.5. Compositional Order Parameters

3.6. Free Energy and Entropy Evolution

3.7. Density Histograms

4. Applications of Mesodyn Simulations in Formulation Development

4.1. Precipitation Membrane Formation

4.2. Nanoscale Drug Delivery Systems

4.3. Development of thin Films from Block Copolymers

4.4. Solubility of Menthol by Platycodin D

4.5. Prediction of Chitosan and Poly(e-caprolactone) Binary Systems, Miscibility and Phase Separation Behaviour

4.6. Nanotube Self-forming Process of Amphiphilic Copolymer

4.7. Utilizing a Macrocyclic Terbium Compound with Hetero-ligands to Create an Effective Luminous Soft Media in a Lyomesophase

4.8. Design and Development of Polymersome Chimaera

4.9. Mesoscopic Simulation for the Phase Separation Behavior of the Pluronic Aqueous Mixture

4.10. Quercetin’s Solubility and Release Characteristics

CONCLUSION

ACKNOWLEDGEMENT

REFERENCES

Computational Tools to Predict Drug Release Kinetics in Solid Oral Dosage Forms

Abstract

1. INTRODUCTION

2. CALCULATIONS AND VALIDATION OF DISSOLUTION MODELS USING MS EXCEL

2.1. Zero-order Drug Release Model

2.2. First Order Drug Release Model

2.3. Higuchi Model

2.4. Hixson Crowell Model

2.5. Korsmeyer-Peppas Model

2.6. Similarity Factor Calculations Using Excel

2.6.1. Using Excel for f2 Calculation

2.6.1.1. Data Collection

2.6.1.2. Data Entry in Excel

2.6.1.3. Calculation

2.6.1.4. Average Calculation

2.6.1.5. Final Calculation

2.6.1.6. Interpretation

2.7. Difference Factor (f1)

2.7.1. Interpretation

2.7.2. Using Excel for f1 Calculation

2.7.2.1. Data Collection

2.7.2.2. Data Entry in Excel

2.7.2.3. Calculation

2.7.2.4. Average Calculation

2.7.2.5. Interpretation

3. Integrated Tools for Dissolution Modeling of Various Dosage Forms

3.1. DD Solver: Modeling and Comparison of Drug Dissolution Profiles

3.2. Key Features and Capabilities

3.2.1. Mechanistic Modeling

3.2.2. Curve Fitting and Prediction

3.2.3. Dissimilarity and Similarity Assessment

3.2.4. Visualization Tools

3.2.5. Predictive Insights

3.2.6. User-Friendly Interface

3.3. Procedure for Using DD Solver for Drug Dissolution Profile Analysis and Comparison

3.3.1. Data Collection and Input Preparation

3.3.2. Model Selection and Curve Fitting

3.3.3. Simulation and Prediction

3.3.4. Dissolution Profile Comparison

3.3.5. Visualization and Reporting

3.3.6. Interpretation and Decision-Making

3.4. Applications of DD Solver

3.4.1. Generic Product Development

3.4.2. Formulation Optimization

3.4.3. Comparative Studies

3.4.4. Risk Assessment

4. PCPDisso (v3i)

4.1. Hypothetical Release Models for PCP Disso

4.1.1. Zero-Order Release Model

4.1.2. First-order Release Model

4.1.3. Higuchi Release Model

4.1.4. Weibull Release Model

4.1.5. Fractional Release Model

5. KinetDS 3.0 Software

6. Other Dissolution Workstations/tools

CONCLUSION

ACKNOWLEDGEMENTS

REFERENCES

Warp and Woof of Drug Designing and Development: An In-Silico Approach

Abstract

1. INTRODUCTION

1.1. Drug Design

2. Structure-based drug design

3. Ligand-based drug design

3.1. Quantitative Structure-activity Relationship

3.2. Pharmacophore Modelling

4. Virtual screening

4.1. Molecular Docking

4.2. ADMET Predictions

CONCLUSION

REFERENCES

Data Interpretation and Management Tools for Application in Pharmaceutical Research

Abstract

1. INTRODUCTION

2. HISTORY

2.1. Conventional Approach

2.2. Computer and Software Resolutions

3. TYPES OF RESEARCH DATA

3.1. Qualitative Data

3.2. Quantitative Data

3.3. Observational Data

3.4. Experimental Data

3.5. Survey Data

3.6. Secondary Data

3.7. Meta-Data

4. DATA ANALYSIS

4.1. Intelligent Data Analysis

4.2. Data Mining

4.3. Data Abstracting

5. DATA REPRESENTATION

5.1. Software Applications

5.1.1. SPSS (Statistical Package for the Social Sciences)

5.1.1.1. Statistical Descriptions

5.1.1.2. Statistical Inference

5.1.1.3. Information Perception

5.1.1.4. Factor Examination

5.1.1.5. Analysis of Clusters

5.1.1.6. Analyses of Survival

5.1.2. GraphPad Prism

5.1.2.1. Analyses of Data

5.1.2.2. Information Perception

5.1.2.3. A Fitting Curve

5.1.2.4. Portion Reaction Examination

5.1.2.5. Modelling Statistical Data

5.2. Implementation Application

5.2.1. EHRs, or Electronic Health Records

5.2.2. Systems for Dispensing Medications

5.2.3. Information Systems for Pharmacies

5.2.4. Apps for Mobile Devices

5.2.5. Tele Pharmacy

5.3. Examples of Implementation

5.3.1. Electronic Recommending

5.3.2. Automating Pharmacies

5.3.3. The Clinical Choice was Emotionally Supportive Networks

5.3.4. Management of Medical Treatment

5.3.5. Purpose of Care Testing

CONCLUSION

REFERENCES

Software and Programming

Tools in Pharmaceutical Research

Edited by

Dilpreet Singh

Department of Pharmaceutics

ISF College of Pharmacy

Moga, India

&

Prashant Tiwari

Department of Pharmaceutical Sciences

Dayanand Sagar University

Bengaluru, India

BENTHAM SCIENCE PUBLISHERS LTD.

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PREFACE

The research and development of new pharmaceuticals are carried out with various pharmaceutical hurdles, ethical ramifications, and social duties, along with the approach seeming obscure. The current pharmaceutical research needs creativity, which is the prime lifeblood of every industry. Nowadays, only computer software in the field of pharmaceutical sciences makes it possible to comprehend complicated processes and manage resources, money, and labor effectively and efficiently. Computer software may relieve medical professionals of tedious multitasking processes and complicated prescreening and evaluation of resource materials. The use of computers in all stages of drug discovery, development, and marketing is addressed holistically and comprehensively in this unique contributed work. It explains the process of simulations such as added functions, data mining, predicting human response, quality by design and artificial intelligence to develop cost-effective drugs, multi-formulation approaches, and high-throughput screening which are applied at various phases in clinical development. The book gives readers a comprehensive overview and a systems viewpoint from which they can design strategies to fully utilize the use of computers in their pharmaceutical industry throughout all stages of the discovery and development process. Researchers working in informatics and ADMET, drug discovery, and technology development, as well as IT professionals and scientists in the pharmaceutical sector, should read this. The book's multifaceted, cross-functional approach offers a singular chance for a comprehensive investigation and evaluation of computer applications in pharmaceutics.

There are the following sections in the book: the role of computers in drug discovery, preclinical development, clinical development, drug delivery, systemic optimization, understanding diseases and repurposing, advanced computer-aided functions to optimize biopharmaceutical variables and future applications, and future development. To maintain a consistent structure and approach throughout the book, each chapter is thoroughly revised after being authored by one or more of the foremost authorities in the field. Figures are frequently used to explain intricate ideas and diverse processes. Each chapter includes references so that readers can keep digging into a particular subject. Finally, many of the chapters provide tables of software resources.

Dilpreet Singh Department of Pharmaceutics ISF College of Pharmacy Moga, India &Prashant Tiwari Department of Pharmaceutical Sciences Dayanand Sagar University Bengaluru, India

List of Contributors

Abhijeet PuriAET’s St. John Institute of Pharmacy and Research, Palghar-401404, Maharashtra, IndiaAmardeep K.Himalayan Institute of Pharmacy, Kala Amb, Dist. Sirmour, Himachal Pradesh-173030, IndiaAmol GholapAET’s St. John Institute of Pharmacy and Research, Palghar-401404, Maharashtra, IndiaAnjali SharmaAmar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, (An Autonomous College) Ropar, IndiaAnshita Gupta SoniShri Rawatpura Sarkar Institute of Pharmacy, Kumhari, Durg, Chhattisgarh, IndiaAnita A.College of Pharmaceutical Sciences, Dayananda Sagar University, Bengaluru, Karnataka-560078, IndiaAnglina KiskuNeuro Pharmacology Research Laboratory (NPRL), Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, IndiaAnchal AroraAll India Institute of Medical Sciences, Bathinda, IndiaArvinder KaurDepartment of Pharmaceutics, KLE College of Pharmacy, Constituent Unit of KLE Academy of Higher Education and Research (Deemed to be University), Rajajinagar, Bengaluru-560010, Karnataka, IndiaAvichal KumarDepartment of Pharmaceutics, KLE College of Pharmacy, Constituent Unit of KLE Academy of Higher Education and Research (Deemed to be University), Rajajinagar, Bengaluru-560010, Karnataka, IndiaAnjali SinhaDepartment of Pharmaceutics, KLE College of Pharmacy, Constituent Unit of KLE Academy of Higher Education and Research (Deemed to be University), Rajajinagar, Bengaluru-560010, Karnataka, IndiaBharti SapraDepartment of Pharmaceutical Sciences & Drug Research, Punjabi University, Patiala, Punjab, IndiaChanchal Deep KaurRungta Institute of Pharmaceutical Sciences and Research, Raipur, Chhattisgarh, IndiaDeependra SoniFaculty of Pharmacy, MATS University Campus, Aarang, Raipur, Chhattisgarh, IndiaDeepa Bagur ParameshDepartment of Pharmaceutics, KLE College of Pharmacy, Constituent Unit of KLE Academy of Higher Education and Research (Deemed to be University), Rajajinagar, Bengaluru-560010, Karnataka, IndiaDevendra S. ShirodeDepartment of Pharmacology, Dr. D. Y. Patil College of Pharmacy, Akurdi, Pune, Maharashtra, IndiaDhriti MahajanDepartment of Pharmaceutical Sciences & Drug Research, Punjabi University, Patiala, Punjab, IndiaDiksha SharmaAmar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, (An Autonomous College) Ropar, IndiaGunpreet KaurUniversity Center of Excellence in Research, BFUHS, Faridkot, IndiaKavya ManjunathDepartment of Pharmacology, KLE College of Pharmacy, Constituent Unit of KLE Academy of Higher Education and Research (Deemed to be University), Rajajinagar, Bengaluru-560010, Karnataka, IndiaKunjbihari SulakhiyaNeuro Pharmacology Research Laboratory (NPRL), Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, IndiaMadhavi SahuNeuro Pharmacology Research Laboratory (NPRL), Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, IndiaManisha YadavDepartment of Pharmaceutical Sciences & Drug Research, Punjabi University, Patiala, Punjab, IndiaMonika ChauhanSchool of Health Sciences and Technology, UPES, Dehradun, IndiaNeelam SharmaAmar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, (An Autonomous College) Ropar, IndiaOm SilakariDepartment of Pharmaceutical Sciences & Drug Research, Punjabi University, Patiala, Punjab, IndiaParveen BansalUniversity Center of Excellence in Research, BFUHS, Faridkot, IndiaPankaj Kumar SinghDepartment of Pharmaceutics, National Institute of Pharmaceutical Education and Research NIPER), Hyderabad, Telangana-500037, IndiaPopat MohiteAET’s St. John Institute of Pharmacy and Research, Palghar-401404, Maharashtra, IndiaPranay SoniDepartment of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, IndiaPrashant TiwariDepartment of Pharmacology, Dayananda Sagar University, Bengaluru, IndiaPunam GabaAmar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, (An Autonomous College) Ropar, IndiaRam Babu S.Himalayan Institute of Pharmacy, Kala Amb, Dist. Sirmour, Himachal Pradesh-173030, IndiaRahul Kumar SharmaAmar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, (An Autonomous College) Ropar, IndiaRavinder SharmaUniversity Institute of Pharmaceutical Sciences and Research, BFUHS, Faridkot, IndiaRenjil JoshiShri Rawatpura Sarkar Institute of Pharmacy, Kumhari, Durg, Chhattisgarh, IndiaRicha SoodCollege of Pharmaceutical Sciences, Dayananda Sagar University, Bengaluru, Karnataka-560078, IndiaRishi PaliwalNanomedicine and Bioengineering Research Laboratory (NBRL), Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, IndiaSagar PardeshiAET’s St. John Institute of Pharmacy and Research, Palghar-401404, Maharashtra, IndiaSamaresh Pal RoyDepartment of Pharmacology, Maliba Pharmacy College, Uka Tarsadia University, Bardoli-394350, Surat, Gujarat, IndiaSakshi T.Himalayan Institute of Pharmacy, Kala Amb, Dist. Sirmour, Himachal Pradesh-173030, IndiaSaurabh MaruDepartment of Pharmacology, School of Pharmacy and Technology Management, SVKM’s Narsee Monjee Institute of Management Studies, Maharashtra, IndiaShailesh SharmaAmar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, (An Autonomous College) Ropar, IndiaShivam AdityaNeuro Pharmacology Research Laboratory (NPRL), Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, IndiaShilpa P. ChaudhariDepartment of Pharmacology, Dr. D. Y. Patil College of Pharmacy, Akurdi, Pune, Maharashtra, IndiaShilpa MurthyDepartment of Pharmaceutical Chemistry, KLE College of Pharmacy, Constituent Unit of KLE Academy of Higher Education and Research (Deemed to be University), Rajajinagar, Bengaluru-560010, Karnataka, IndiaSonal DubeyCollege of Pharmaceutical Sciences, Dayananda Sagar University, Kumaraswamy Layout, Bengaluru-560111, IndiaSunil Kumar KadiriDepartment of Pharmacology, College of Pharmaceutical Sciences, Dayananda Sagar University, K.S Layout, Bengaluru-560111, Karnataka, IndiaSwarnlata SarafUniversity Institute of Pharmacy, Pt.Ravishankar Shukla University, Raipur, Chhattisgarh, IndiaTanavirsing RajputAET’s St. John Institute of Pharmacy and Research, Palghar-401404, Maharashtra, IndiaVaibhav R. VaidyaDepartment of Pharmacology, Dr. D. Y. Patil College of Pharmacy, Akurdi, Pune, Maharashtra, IndiaVikas GuptaUniversity Center of Excellence in Research, BFUHS, Faridkot, India

Introduction to Computer-Based Simulations and Methodologies in Pharmaceutical Research

Samaresh Pal Roy1,*

1 Department of Pharmacology, Maliba Pharmacy College, Uka Tarsadia University, Bardoli-394350, Surat, Gujarat, India

Abstract

Pharmaceutical research is increasingly using computer-based simulations and approaches to hasten the identification and development of new drugs. These methods make use of computational tools and models to forecast molecular behavior, evaluate therapeutic efficacy, and improve drug design. Molecular modeling is a key application of computer-based simulations in pharmaceutical research. It allows researchers to build virtual models of molecules and simulate their behavior, which provides insights into their interactions and properties. Molecular docking is a computational method used in Computer-Aided Drug Design (CADD) to predict the binding mode and affinity of a small molecule ligand to a target protein receptor. Quantitative structure-activity relationship (QSAR) modeling is another pharmaceutical research tool. QSAR models predict molecular activity based on the chemical structure and other attributes using statistical methods. This method prioritizes and optimizes drug candidates for specific medicinal uses, speeding up drug discovery. Another effective use of computer-based simulations in pharmaceutical research is virtual screening. It entails lowering the time and expense associated with conventional experimental screening methods by employing computational tools to screen huge libraries of chemicals for prospective therapeutic candidates. While computer-based techniques and simulations have many advantages for pharmaceutical research, they also demand a lot of processing power and knowledge. Also, they are an addition to conventional experimental procedures rather than their replacement. As a result, they frequently work in tandem with experimental techniques to offer a more thorough understanding of drug behavior and efficacy. Overall, computer-based simulations and methodologies enable pharmaceutical researchers to gather and analyze data more efficiently, bringing new medications and therapies to market.

Keywords: Computer-based simulations, Computer-aided drug design (CADD), Drug behavior, Drug efficacy, Drug discovery, Molecular modeling, Molecular dynamics, Pharmaceutical research, Quantitative structure-activity relationship (QSAR) modeling, Virtual screening.

*Corresponding author Samaresh Pal Roy: Department of Pharmacology, Maliba Pharmacy College, Uka Tarsadia University, Bardoli-394350, Surat, Gujarat, India; Tel: [phone]; E-mail: [email protected]

1. INTRODUCTION

Pharmaceutical research is a crucial aspect of healthcare that aims to discover, develop, and test new medications in order to improve human health. During this process, potential drug candidates are identified, their characteristics are optimized, and their effectiveness and safety are evaluated. The drug development process is expensive and time-consuming due to the low success rate of new medication candidates and the high average cost of bringing a new treatment to market [1, 2]. Computer-based simulations have become important tools in pharmaceutical research to speed up the identification of novel drugs and increase the likelihood that new medication candidates will be successful. To simulate the behaviour of molecules and their interactions with biological targets, these simulations make use of computer techniques and models. The molecular underpinnings of disorders like Parkinson's disease and Alzheimer's disease have been studied using computational simulations [3, 4]. Additionally, it aids in predicting the activity of prospective therapeutic targets including enzymes and receptors as well as their interactions with other molecules [5, 6]. Physicochemical parameters, including solubility, permeability, and stability, can be predicted using computer-based simulations in order to design and refine drug candidates [7, 8]. Additionally, it predicts aspects of drug candidates' pharmacokinetics and pharmacodynamics, such as absorption, distribution, metabolism, and excretion [9, 10]. Researchers can get a more thorough understanding of the molecular principles underlying drug action by combining computer-based simulations with experimental data, which eventually results in safer and more effective drugs [11].

1.1. Types of Computer-Based Simulations in Pharmaceutical Research

Pharmaceutical research extensively utilizes a range of computer-based simulation techniques, with molecular modeling being a prominent example. This technique entails constructing and analyzing three-dimensional (3D) molecular models to comprehend their dynamics and interactions with other molecules. The molecular modelling technique known as molecular docking, for instance, forecasts the binding mechanism and affinity of small molecule ligands to their target proteins [12]. Simulating the movements of molecules over time to examine their interactions and behaviour at the atomic level is known as molecular dynamics simulation. For instance, protein conformational changes and their interactions with other molecules can be studied using molecular dynamics simulations [13]. Using statistical techniques, quantitative structure-activity relationship (QSAR) modelling links the chemical structure of molecules with their biological activity. For instance, QSAR modelling can be used to infer new drug candidates' biological activity from their chemical structure [14]. Virtual screening involves the identification of potential therapeutic candidates from vast chemical libraries by evaluating the affinity and selectivity of each compound toward a specific protein. For instance, this method can be applied to discover inhibitors for viral proteins, which could then be utilized as a means to treat viral infections [15].

1.1.1. Challenges and Limitations of Computer-Based Simulations

While computer-based simulations offer numerous advantages for pharmaceutical research, they also come with significant drawbacks. The accuracy and reliability of computational models, which hinge on the quality of input data and the assumptions made during model creation, pose a considerable challenge [16]. Another concern is the need for high-performance computing resources, which can be costly and demand specialized expertise to carry out complex simulations. Integrating computational and experimental data can prove challenging due to variations in the data generated by each method [17]. Furthermore, comparing results across different studies can be difficult due to the lack of standardized data processing techniques and tools [18].

1.1.2. Advances in Computer-Based Simulations

Some of the difficulties and restrictions of these methodologies have been overcome by recent developments in computer-based simulations. For instance, the accuracy and dependability of computational models have increased as a result of the development of machine learning algorithms [19]. High-performance computer resources are now easier to obtain and more affordable thanks to cloud computing services [20]. Additionally, the introduction of novel techniques like cryo-electron microscopy has made the integration of computational and experimental data more practical [21].

To validate and refine computer models of protein-ligand interactions, cryo-electron microscopy can be employed to determine the three-dimensional structure of proteins at nearly atomic resolution. The future of pharmaceutical research is expected to continue relying significantly on computer-based simulations. The accuracy and reliability of these computer models are projected to greatly improve with the emergence of novel computational techniques such as deep learning [22]. The integration of computer-based simulations with other technologies like high-throughput screening and gene editing is also anticipated to facilitate the advancement of personalized medicine [23].

By providing insights into molecular behavior and interactions that are challenging to obtain solely through experimental methods, computer-based simulations have the potential to revolutionize the approach to pharmaceutical research. Future drug discovery and development efforts are anticipated to benefit much more from the continued development of computational approaches. To ensure these techniques' accuracy and dependability in drug development, it is crucial to overcome the difficulties and restrictions related to them.

2. MOLECULAR MODELLING: PRINCIPLES AND APPLICATIONS IN DRUG DISCOVERY

A computational method called molecular modelling is used to investigate the atomic-level dynamics, interactions, and structure of molecules [24]. This method has become a crucial component of drug discovery research because it enables scientists to see and examine the interactions between drug candidates and their target proteins as well as to create new substances with enhanced binding affinity and selectivity [25]. The development of safe and effective medicines depends on the ability to predict features of drug candidates such as pharmacokinetics and pharmacodynamics.

2.1. Principles of Molecular Modelling

The process of building and analysing three-dimensional (3D) representations of molecules is known as molecular modelling [26]. The features of potential therapeutic candidates can be predicted using these models, and novel compounds can be created with enhanced binding capabilities [27]. The fundamentals of molecular modeling encompass various aspects, including force field calculations. These calculations assess the stability and energy of molecular structures by considering atom-to-atom interactions like bond stretching, bond angle bending, and nonbonded interactions [28]. Simulating the movements of molecules over time to examine their interactions and behaviour at the atomic level is known as molecular dynamics simulation. This method sheds light on the kinetics, flexibility, and structural changes that occur in biomolecules [29]. Calculations based on quantum mechanics: Examining molecules' electronic structures to forecast their chemical properties, such as reactivity, stability, and electronic spectra. In comparison to conventional force fields, these computations can give a more precise representation of molecular interactions and characteristics [30]. Molecular docking: Investigating the conformational space of the protein-ligand complex to predict the binding mechanism and affinity of small molecule ligands to their target proteins. This process is frequently employed to find new medication candidates and to enhance their binding qualities [31].

2.2. Applications of Molecular Modelling in Drug Discovery

Drug discovery has benefited from the use of molecular modelling, which enables researchers to understand the molecular underpinnings of drug action and resistance and requires visualisation and analysis of interactions between drug candidates and their target proteins [32]. By examining the interactions between ligands and target proteins and making appropriate modifications to their chemical structures, it is possible to design and optimise therapeutic candidates with higher binding affinity and selectivity [33]. Computationalmodels are used to forecast the pharmacokinetics (drug absorption, distribution, metabolism, and excretion) and pharmacodynamics (drug-receptor interactions and effects) of drug candidates. This can help prioritise candidates for experimental testing and can also help with drug molecule design and optimisation [34]. Using structure-based and ligand-based virtual screening techniques, one may quickly and cheaply identify new therapeutic targets and check the activity of vast databases of compounds [35].

2.3. Molecular Modeling Techniques

There are different methodologies and procedures which are applied in molecular modelling techniques. The first model comprises building a 3D model of a protein based on its amino acid sequence and the structures of related proteins are known as homology modelling. When a target protein's experimental structure is unavailable, this strategy is especially helpful [36]. Simulations of molecular dynamics: As was already indicated, this method mimics the motion of molecules overtime to examine their interactions and behaviour at the atomic level, revealing information on the dynamics and function of proteins [37]. Designing new therapeutic candidates using the structure and characteristics of existing ligands is known as “ligand-based drug design.” This strategy can be especially helpful when the target protein's structure is unknown or challenging to identify experimentally [38]. Designing new therapeutic candidates using the target protein's structure and characteristics is known as “structure-based drug design.” This strategy enables the logical development of medications that can specifically interact with a target protein, potentially improving efficacy and reducing negative effects [39]. The accuracy and dependability of computational models: The quality of molecular models depends on the accuracy of force fields, quantum mechanics methods, and docking algorithms used, as well as the accessibility of high-quality experimental data to validate and refine the models [40]. While molecular modelling offers many benefits in drug discovery, there are also some limitations and challenges to take into account. High-performance computing resources are required for molecular modelling, especially for quantum mechanics calculations and molecular dynamics simulations, which could be a barrier for some research teams and institutions [41]. Combining data from computational modelling and experimental research can be difficult since molecular systems are not all represented with the same amount of information, resolution, and represen- tation. For the field to evolve, methodologies and tools to analyze diverse data sources must be developed [42].

The area of molecular modelling is quickly developing new tools and methods securely for drug discovery and development. The repeatability and generalizability of findings may be constrained by the lack of integrated tools and procedures for data analysis, which can make it challenging to compare outcomes across studies and research groups [43].

Despite these difficulties, molecular modelling is still evolving and becoming a more crucial part of the drug discovery process. In the upcoming years, it is anticipated that improvements in computational hardware, software, and algorithms as well as the incorporation of multidisciplinary techniques and data sources will further increase the influence of molecular modelling on drug discovery and development.

3. COMPUTER-AIDED DRUG DESIGN: CONCEPTS AND TECHNIQUES

In the computational method of computer-aided drug design (CADD), drug candidates are designed and optimized using computer simulations and models [44]. To find prospective drug candidates and improve their properties, CADD integrates molecular modelling, virtual screening, and other computational tools [45]. To expedite drug discovery and identify novel drug candidates with improved potency, selectivity, and pharmacokinetic properties, Computer-Aided Drug Design (CADD) has emerged as a vital component of pharmaceutical research [46]. This collaborative effort involves experts in chemistry, biology, and computer science working together to develop superior medications that specifically target proteins or receptors while minimizing adverse effects [47]. In the computational approach known as computer-aided drug design (CADD), drug candidates are conceptualized and refined through the use of computer simulations and models [44]. CADD employs a combination of molecular modeling, virtual screening, and various other computational tools to identify potential drug candidates and enhance their properties [45]. In the pursuit of expediting the drug discovery process and uncovering new candidates with enhanced potency, selectivity, and pharmacokinetic attributes, Computer-Aided Drug Design (CADD) has risen as a pivotal aspect of drug discovery research [46]. CADD plays a crucial role in crafting enhanced medications that precisely target specific proteins or receptors while minimizing undesirable side effects. This involves collaborative efforts between experts in chemistry, biology, and computer science [47].

3.1. Principles of Computer-Aided Drug Design

Constructing and examining three-dimensional (3D) models of molecules to investigate their dynamics, structure, and interactions with other molecules are referred to as molecular modeling [48]. This approach often involves simulations rooted in quantum mechanics, molecular mechanics, and molecular dynamics to accurately capture the behavior of molecules and their interactions with target proteins [49].

3.1.1. Virtual Screening

Determining possible therapeutic candidates from huge chemical libraries by assessing the selectivity and affinity of the compounds for a target protein [50]. To forecast and rank molecules based on their potential biological activity, this procedure may incorporate the use of high-throughput docking algorithms, machine learning, and artificial intelligence approaches [51]. Designing new drug candidates using the target protein's structure and characteristics is known as “structure-based drug design” (SBD) [52]. This strategy creates compounds that can attach to a protein's active site using the 3D structure as a guide, regulating the protein's activity and producing therapeutic benefits [53]. Designing new therapeutic candidates using the structure and characteristics of existing ligands is known as “ligand-based drug design” [54]. This method seeks to increase the activity and selectivity of molecules by identifying or designing novel compounds with features that are comparable to those of known active molecules [55]. The key characteristics of a ligand are investigated that contribute to its biological activity through pharmacophore modelling [56]. A pharmacophore is a physical configuration of molecular elements required for ideal interactions with a particular target protein. Pharmacophore models can be applied to evaluate compound libraries for possible therapeutic candidates or to direct the design of novel compounds [57].

3.2. Applications of Computer-Aided Drug Design in Drug Discovery

The possible therapeutic possibilities are looked for by searching through extensive databases of chemicals [58]. Virtual screening, which evaluates millions of compounds in silico before choosing a smaller subset for experimental testing, can significantly cut the time and cost of identifying promising drug candidates as a result of the increasing availability of chemical libraries and the advancements in computational methods [59]. Enhanced binding affinity and selectivity can be attained through the design and optimization of therapeutic candidates [60]. Researchers can create compounds with heightened potency and selectivity by utilizing CADD methods to identify crucial interactions between a drug candidate and its target protein. This increases the probability of successful outcomes in clinical trials [61].

The drug candidates' pharmacokinetics and pharmacodynamics are predicted [62]. Researchers can forecast possible problems with therapeutic efficacy and safety by simulating the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug candidates with CADD, allowing for early adjustments in the drug design process [63]. Identifying potential drug targets is a key application [64]. CADD isutilized to examine the structural and functional characteristics of proteins, which enables researchers to discover potential therapeutic targets and gain insights into the role that proteins play in disease development [65].

Molecular causes of medication resistance should be researched [66]. The effectiveness of treatment approaches can be increased by helping researchers build medications that can overcome or circumvent resistance mechanisms by analysing the structural alterations in target proteins and comprehending the molecular mechanisms underlying drug resistance [67].

3.2.1. Computer-Aided Drug Design Techniques

These techniques are used to predict the binding mechanism and affinity of small molecular ligands to their target proteins by molecular docking [68]. This method ranks the most advantageous for binding poses using a variety of scoring functions and algorithms, revealing details about the molecular interactions underlying ligand binding and activity [69]. Simulating the mobility of molecules over time to examine their interactions and behaviour at the atomic level is known as molecular dynamics simulation [70]. This method can help in the design of novel compounds with improved features by offering useful information regarding the conformational changes, flexibility, and stability of proteins and ligands [71]. De novo drug design is the process of applying computer approaches to create brand-new medication candidates [72]. In this method, new chemical scaffolds with desirable features are possibly discovered by creating novel molecular structures based on the binding site or pharmacophore models of the target protein [73]. Drug design using smaller pieces to create bigger molecules is known as fragment-based drug design [74]. In order to create therapeutic candidates with improved affinity and selectivity, this strategy entails finding and optimising tiny molecular fragments that bind to the target protein [75].

The fundamental Computer-Aided Drug Design (CADD) workflow is essential to the process of finding new drugs. Wet lab experiments, Structure-Based Drug Design (SBDD), and Ligand-Based Drug Design (LBDD) are only a few of the methods used in this methodology. The combination of these methods improves the effectiveness and success rate of the drug development process by enabling iterative rounds of ligand creation. Wet-lab experiments are the initial step in the CADD workflow, and they are shown as solid lines. In wet lab experiments, physical laboratory tasks including compound synthesis, testing, and biological activity analysis are all part of the process. These studies offer important information on the potency, pharmacokinetics, and interactions of the drugs with the target molecules. Future CADD approaches are built upon the data collected from wet-lab research.

Structure-Based Drug Design (SBDD) is shown as a key CADD technique by dashed lines. In SBDD, drug candidates are designed and improved using the three-dimensional structures of target molecules, often proteins. This method uses computational simulations and chemical modelling to examine the binding sites of the target molecules and forecast interactions with potential ligands. Utilizing this knowledge, the probability of successful drug development is elevated through the design and optimization of ligands to enhance their affinity and selectivity for the target. Another vital technique, Ligand-Based Drug Design (LBDD), is represented in the CADD workflow by dashed lines. LBDD is built upon the analysis of chemical and biological attributes of known ligands that have exhibited activity against the target molecule. Computational methods like virtual screening and quantitative structure-activity relationship (QSAR) models enable researchers to identify molecular features and patterns influencing the ligands' activity. The design and prioritization of novel ligands with improved potency and other desirable qualities is then made using this knowledge.

The double-headed arrows in the CADD workflow highlight how dynamic SBDD and LBDD procedures are. Iteratively, the use of these methods enables a back-and-forth exchange of information between them. For instance, the knowledge collected by SBDD can direct the choice of chemicals for additional LBDD studies. On the other hand, the results of LBDD can help designers create new ligands that can then be tested in SBDD simulations. Through this iterative process, ligand design can be continuously improved, resulting in the creation of more potent and selective medications. While CADD has numerous benefits for drug development, there are several drawbacks and difficulties to take into account, such as the precision and dependability of computational models [76]. The accuracy of the input data, force fields, and algorithms utilized determine the quality of the models and predictions, which can occasionally result in false positives or incorrect conclusions [77]. There are a few requirements for resources for high-performance computing [78]. Numerous CADD methods, like molecular dynamics simulations and virtual screening, need a lot of computing capacity to process large amounts of data quickly [79]. Also, there is a need to combine experimental and computational data [80]. In order to test and improve computational predictions, effective drug discovery necessitates the combination of CADD with experimental approaches such as biochemical assays, biophysical methods, and X-ray crystallography [81]. There is a lack of tools and processes for data analysis that are standardised [82]. Establishing standardised processes for data analysis and comparison is difficult because the area of CADD is continually growing and there is a large range of software and tools available for various jobs [83]. Fig. (1) depicts a systematic workflow of a computer-aided drug design (CADD).

Fig. (1)) Example of a computer-aided drug design workflow.

Despite these obstacles, CADD has the potential to create new therapies in the future as it continues to improve and assume a more significant position in the drug discovery process.

3.2.2. Molecular Docking: Predicting Protein-ligand Interactions

A computer technique called molecular docking is used to predict the affinities and binding modes of small molecule ligands to their target proteins [84]. In docking simulations, the 3D structure of the protein-ligand complex is predicted, and the binding affinity between the ligand and the protein is estimated. In addition to studying the chemical interactions between the ligand and the protein, this information can be utilized to design and enhance small molecule inhibitors or activators of a target protein [85].

3.2.2.1. Principles of Molecular Docking

The fundamentals of molecular recognition and binding thermodynamics serve as the foundation for molecular docking. A huge number of possible ligand poses are generated throughout the docking process, and these poses are then scored to determine how well they bind to the protein. To correctly estimate the binding mechanism and affinities, the docking algorithm must take into account the flexibility of both the ligand and the protein [86].

Geometric or energy-based docking are the two most widely used docking techniques, respectively [87]. Geometric docking is based on matching the ligand's geometric properties to the protein binding site. Energy-based docking uses molecular mechanics force fields to estimate the binding free energy. Solvation effects and induced fit docking, which considers the conformational changes of the protein following ligand binding, are recent developments in docking methods.

3.2.2.2. Applications of Molecular Docking in Drug Discovery

With applications in lead identification, lead optimization, and virtual screening, molecular docking has emerged as a key method in drug discovery [88]. Small molecule inhibitors or activators can be designed and optimized using docking simulations, and the binding affinity and selectivity of compounds for their target proteins can be predicted. Docking simulations can also be used to find probable binding sites on a target protein. Although molecular docking has proven to be a useful tool in the drug development process, there are several restrictions and difficulties to take into account. The quality of the protein and ligand structures used, the choice of the docking technique, and the scoring function all influence the accuracy and reliability of docking predictions [89]. Incorporating water molecules and cofactors into docking simulations can be challenging, as they can significantly impact the ligand's binding mechanism and affinity. Since many proteins change their shape following ligand binding, correct modelling of protein flexibility is another challenge in molecular docking. Induced fit docking was created to overcome this issue. However, it requires significant processing power and may not be suitable for large-scale virtual screening. The advancement of new algorithms and scoring functions that enhance the accuracy and reliability of docking predictions holds promising potential for the future of molecular docking. The integration of artificial intelligence and machine learning techniques is also expected to play a pivotal role in advancing molecular docking, allowing for more precise and efficient predictions of protein-ligand interactions [90].

Several widely recognized programs are utilized in molecular docking, including software such as AutoDock, AutoDock Vina, DOCK, GOLD, Glide, MOE, and the Schrödinger Suite. These programs offer a range of tools and techniques for simulating protein-ligand interactions, which prove valuable in discovering new drug candidates, assessing binding affinities, and exploring the space of potential ligand conformations. Researchers in the field of molecular docking find these programs useful due to the various ways they streamline the process of drug development. Table 1 provides a list of software tools for molecular docking, along with their applications.

Table 1Software tools for molecular docking and their applications.SoftwareApplicationAutoDockUsed for ligand-protein docking, virtual screening, and drug discovery. It incorporates various scoring functions and search algorithms for efficient docking simulations.AutoDock VinaWidely used for protein-ligand docking and virtual screening. It offers high-speed docking calculations with an emphasis on accuracy and efficiency.DOCKUtilized for protein-ligand docking and virtual screening. It employs a geometric matching algorithm for efficient docking calculations.GOLDPrimarily used for ligand docking and virtual screening. It incorporates genetic algorithms for exploring ligand conformational space and protein-ligand interactions.GlideWidely used for high-throughput virtual screening, ligand docking, and scoring. It employs a combination of molecular docking and ligand-based methods for accurate predictions.MOE (Molecular Operating Environment)Offers a comprehensive suite of tools for protein-ligand docking, virtual screening, and drug design. It incorporates advanced algorithms for efficient and accurate docking simulations.Schrödinger SuiteA software suite that includes various tools for molecular docking, such as Glide, Prime, and Induced Fit Docking. It is widely used for drug discovery and virtual screening.

Insights into the manner of binding and affinities of small molecule ligands to their target proteins are provided by molecular docking, a useful technique in the drug discovery process. This chapter has covered the fundamentals and uses of molecular docking as well as its drawbacks and difficulties. Despite these obstacles, molecular docking is still developing and becoming a more crucial part of the drug discovery process.

3.2.3. Quantitative Structure-Activity Relationship (QSAR) Modeling

The biological activity of molecules can be predicted using a computational technique known as quantitative structure-activity relationship (QSAR) modeling. The basis of QSAR models is the concept that a molecule's biological activity is influenced by its physicochemical characteristics, including lipophilicity, electronic structure, and molecular size [91]. QSAR models can be employed to design and optimize compounds with desired activity profiles and to predict the biological activity of novel molecules [92].

3.2.3.1. Principles of QSAR Modelling

QSAR modelling entails the creation of a mathematical model that links the physical and chemical characteristics of a group of molecules with their biological activity. With similar chemical structures, the model can be used to forecast the action of novel compounds [93]. The concept of molecular descriptors, which are numerical values characterizing a molecule's chemical and physical properties, forms the basis for QSAR models [94]. Molecular descriptors encompass factors such as electronegativity, logP, and molecular weight. The creation of a QSAR model involves processes such as data collection, calculation of molecular descriptors, model construction, and model validation [95]. The input data for the model should accurately represent the chemical space of interest and exhibit diversity. The model's molecular descriptors must be pertinent to the biological activity being predicted, and it must be built and validated using the proper statistical techniques.

3.2.3.2. Applications of QSAR Modelling in Drug Discovery

With applications in lead identification, lead optimisation, and toxicity prediction, QSAR modelling has grown in significance as a tool in the drug discovery process. In place of time-consuming and expensive experimental testing, QSAR models can be employed to predict the biological activity of novel drugs based on their chemical structure [96]. Additionally, QSAR models can be utilized to optimize the chemical structure of a lead molecule to enhance its biological activity or reduce its toxicity [97]. Although QSAR modelling has shown to be an effective method for drug development, there are several restrictions and difficulties to take into account. The choice of molecular descriptors and statistical techniques employed in the model development and validation process, as well as the quality and diversity of the data used to build the model, all affect the accuracy and dependability of QSAR models. It can be difficult to extrapolate QSAR models to new chemical spaces since the model might not be reliable for molecules with vastly different chemical structures [98]. Another issue with QSAR modeling is the lack of model interpretability. While QSAR models may effectively predict biological activity, it is often challenging to understand the relationship between the molecular descriptors and the predicted biological activity [99]. This can make it more difficult to use QSAR models for informing rational drug development.

With the development of new techniques and algorithms that enhance the precision and reliability of QSAR predictions, the future of QSAR modeling appears promising. To achieve more accurate and efficient predictions of biological activity, machine learning and artificial intelligence approaches, such as deep learning and reinforcement learning, are expected to play a significant role in QSAR modeling in the future [100]. Furthermore, the integration of QSAR with other computational techniques, such as molecular docking and pharmacophore modeling, could enhance the drug development process and provide a more comprehensive understanding of molecular interactions [101]. QSAR modeling contributes to drug discovery by illuminating the relationship between chemical structure and biological activity. This chapter has covered the fundamentals and applications of QSAR modeling as well as its limitations and challenges. Despite these obstacles, QSAR modeling continues to advance and remains increasingly important in the quest for new drugs.

3.2.4. Virtual Screening: Accelerating Drug Discovery Through Computational Techniques

A computational method called virtual screening is used to pick out prospective therapeutic candidates from vast libraries of chemicals [102]. The foundation of virtual screening techniques is the prediction of a compound's binding affinity and selectivity for a target protein. To prioritize compounds for experimental testing and to identify lead compounds for further optimisation, virtual screening can be utilized. The development in computational power, algorithms, and data availability has led to a major increase in the use of virtual screening in recent years [103].

3.2.4.1. Principles of Virtual Screening

Using computational methods, virtual screening assesses the binding affinity and selectivity of drugs toward a specific target protein. The virtual screening process typically involves the following steps: (1) preparing the target protein, (2) selecting a library of compounds, (3) docking or scoring the compounds with the target protein, and (4) filtering or ranking the compounds based on their predicted binding affinity and selectivity [104]. When employing docking methods, the energetics of a compound-protein complex are calculated, taking into account their molecular interactions [105]. Scoring techniques utilize statistical models to predict the binding affinity of the compound based on the compound's chemical structure and the structure of the target protein [106].

3.2.4.2. Applications of Virtual Screening in Drug Discovery

With applications in lead identification, lead optimisation, and hit-to-lead growth, virtual screening has emerged as a key tool in the drug discovery process [107]. In order to prioritise compounds for experimental testing based on their expected binding affinity and selectivity, virtual screening can be performed to find novel compounds with the necessary biological activity [108]. Virtual screening has also found applications in repurposing already approved medications for new uses. Researchers can identify potential therapeutic candidates for new indications by screening libraries of licensed medications or medications currently in clinical trials, thereby expediting and reducing the cost of drug development [109]. While virtual screening has proven to be a valuable technique for drug development, it is essential to consider several limitations and challenges [110]. The quality of the protein structure, the selection of the chemical library, and the choice of docking or scoring algorithms used in the virtual screening process all influence the accuracy and reliability of virtual screening approaches [111]. Predicting the pharmacokinetic and pharmacodynamic characteristics of possible drug candidates is another issue in virtual screening. Virtual screening techniques frequently ignore a compound's features related to absorption, distribution, metabolism, and excretion (ADME) in favour of predicting binding affinity and selectivity [112].

With the development of new techniques and algorithms that enhance the precision and reliability of virtual screening predictions, the future of virtual screening appears promising [113