Search

Browse Subject Areas

For Authors

Submit a Proposal

Data Science in Pharmaceutical Development

Edited by Vivek P. Chavda and Usha Desai
Copyright: 2025   |   Expected Pub Date:2025/07/30
ISBN: 9781394287352  |  Hardcover  |  
412 pages

One Line Description
This book is an indispensable guide for anyone looking to understand how AI, machine learning, and data science are revolutionizing drug discovery, development, and delivery, offering practical insights and addressing crucial real-world applications and considerations.

Audience
Research scholars, pharmacy students, pharmaceutical process engineers, and pharmacy professionals in the pharmaceutical and biopharmaceutical industry who are working in drug discovery, chemical biology, computational chemistry, medicinal chemistry, and bioinformatics.

Description
Data Science in Pharmaceutical Development offers a comprehensive and forward-looking exploration of how artificial intelligence, machine learning, and data science are reshaping the pharmaceutical landscape. From the earliest stages of drug discovery to advanced delivery systems and post-market surveillance, this volume bridges the gap between innovation and rea-lworld application. Practical examples and case studies bring to life the transformative potential of AI-powered tools in accelerating research, enhancing patient outcomes, and improving efficiency throughout the pharmaceutical product lifecycle.
Designed for researchers, industry professionals, and students alike, this book not only showcases cutting-edge technologies but also addresses the ethical, legal, and regulatory considerations critical to their implementation. Whether you’re navigating the complexities of clinical trials, optimizing supply chains, or seeking to understand the implications of smart drug delivery systems, this book is an indispensable guide to the future of medicine and healthcare innovation.
Readers will find the book:
• Explores the role of AI, machine learning, and data science across the entire pharmaceutical pipeline—from drug discovery and clinical trials to smart drug delivery systems;
• Rich with real-world case studies and practical examples, connecting theory to implementation in modern pharmaceutical research and development;
• Introduces advanced topics like predictive modeling, personalized medicine, IoT, pharmacovigilance, and nanotechnology-enabled drug delivery;
• Highlights emerging trends, ethical considerations, and the regulatory framework surrounding AI in healthcare.

Back to Top
Author / Editor Details
Vivek P. Chavda, PhD is an assistant professor in the Department of Pharmaceutics and Pharmaceutical Technology, Lallubhai Motilal College of Pharmacy, Ahmedabad, India. He has over 100 national and international publications, 30 book chapters, ten books, and two patents to his credit. His research interests include the development of biologics processes and formulations, medical device development, nanodiagnostics and non-carrier formulations, long-acting parenteral formulations, and nano-vaccines.

Usha Desai, PhD is a professor and the Dean of Research and Development at the South East Asia College of Engineering and Technology, Bangalore, India. She authored over 50 research articles, five books, and six patents, and has presented technical research papers in numerous international conferences. Her research interests include biomedical signal processing, machine learning, and brain-computer interface.

Back to Top

Table of Contents
Foreword
Preface
Part 1: Fundamentals of Data Science in Pharmaceuticals
1. Introduction to AI in Medicine and Drug Delivery
Dixa A. Vaghela, Pankti C. Balar and Vivek P. Chavda
1.1 Introduction
1.2 Applications of AI in Medicine
1.2.1 AI in Drug Discovery
1.2.1.1 Target Identification
1.2.1.2 Compound Selection
1.2.1.3 Predictive Modeling in Drug Discovery
1.2.2 Personalized Medicine
1.2.2.1 Tailoring Treatments
1.2.2.2 Genetic and Lifestyle Consideration
1.2.3 Advanced AI Techniques in Medicine
1.2.3.1 Medical Imaging and Diagnostic
1.2.3.2 Patient Monitoring and Remote Care
1.2.3.3 Surgical Assistance and Robotics
1.3 AI in Drug Delivery Systems
1.3.1 Smart Drug Delivery Networks
1.3.2 Nanotechnology-Based Drug Delivery
1.4 Future Trends and Ethical Considerations
1.5 Conclusion
References
2. Data Visualization in Pharmaceutical Development
Gagandeep Kaur, Benu Chaudhary, Vikas Sharma, Parul Sood and Rupesh K. Gautam
2.1 Introduction
2.2 Digitalization of a Continuous Process Manufacturing for Formulated Products
2.2.1 Data Visualization and Cloud Integration
2.3 Clinical Trial Data Visualization
2.4 Decision Making in Product Portfolios of Pharmaceutical Research and Development—Managing Streams of Innovation in Highly Regulated Markets
2.5 Genomic Data Visualization
2.5.1 Opportunities and Challenges
2.6 Real-World Evidence (RWE) Research
2.7 Pharmacokinetic/Pharmacodynamic (PK/PD) Indices
2.8 Supply Chain Visualization
2.9 Designing Medical Data Visualizations
2.10 Pharmacovigilance: Data Visualization
2.11 Health Econometric: Data Visualization
2.12 Explainable Artificial Intelligence: Visualizing
2.13 Conclusion
References
3. Data Science and AI for Transforming R&D
Parul Sood, Gagandeep Kaur, Jatin Kumar, Narinderpal Kaur, Nitin Jangra and Rupesh K. Gautam
3.1 Introduction
3.2 Artificial Intelligence and Machine Learning
3.3 Data Science and AI for Transforming R&D
3.4 Machine Learning and AI Approaches in Drug Discovery
3.4.1 Target Selection and Validation
3.4.2 Drug Design
3.4.3 ADMET Modeling
3.5 Methods for Improving Existing Approaches in R&D
3.5.1 Deep Learning for Protein Structure Prediction and Drug Repurposing
3.5.2 AI in Advancing Pharmaceutical Product Development
3.5.3 Machine Learning/AI for Developing Predictive Biomarkers
3.5.4 AI in Product Cost
3.5.5 AI Emergence in Nanomedicine
3.5.6 AI/ML for Precision Medicine
3.5.7 AI/ML in Quality Control and Quality Assurance
3.5.8 AI/ML-Assisted Tool for Clinical Trial Oversight
3.5.9 AI in Finding the Hit or Lead
3.6 Conclusion
References
Part 2: Applications of Data Science in Pharmaceutical Development
4. Applications of Medical IoT and Smart Sensor Paradigm for Handling Patients
Keshava Jetha, Krupa Vyas, Jalpan Shah, Dhvani Trivedi and Ritul Patel
4.1 Introduction to Medical IoT and Smart Sensors
4.2 IoT and Smart Sensor in Chronic Disease Management
4.3 IoT and Smart Sensors in Post-Operative Monitoring
4.4 Applications of Medical IoT and Smart Sensor
4.5 Remote Patient Monitoring
4.6 Enhancing Patient Safety and Ethical Perspective
4.7 Future Directions and Challenges
4.8 Conclusion
References
5. Predictive Models for Drug Development Using Expert Systems
Nirmal Joshi, Deepak Chandra Joshi, Suraj Koranga, Kajal Gurow and Mayuri Bapu Chavan
5.1 Introduction to Predictive Modeling in Drug Development
5.1.1 Overview of the Drug Development Process
5.1.2 Role of Predictive Modeling in Drug Discovery and Development
5.1.3 Introduction to Expert Systems and Their Applications in Pharmaceutical Research
5.1.4 Importance of Predictive Models in Accelerating Drug Development Timelines
5.2 Fundamentals of Expert Systems
5.2.1 Definition and Characteristics of Expert Systems
5.2.2 Components of Expert Systems: Knowledge Base, Inference Engine, User Interface
5.2.3 Types of Expert Systems: Rule-Based, Fuzzy Logic, Bayesian Networks, etc.
5.2.4 Advantages and Limitations of Expert Systems in Drug Development
5.2.5 Advantages of Expert Systems in Drug Development
5.2.6 Limitations of Expert Systems in Drug Development
5.3 Data Collection and Pre-Processing for Predictive Modeling
5.3.1 Sources of Data in Drug Development: Clinical Trials, Pre-Clinical Studies, Literature, Databases, etc.
5.3.2 Data Pre-Processing Techniques: Data Cleaning Feature Selection, Normalization, etc.
5.3.3 Challenges in Data Collection and Pre-Processing for Predictive Modeling in Drug Development
5.4 Building Rule-Based Expert Systems for Drug Development
5.4.1 Principles of Rule-Based Systems
5.4.2 Knowledge Acquisition: Expert Interviews, Literature Review, and Data Analysis
5.4.3 Rule Generation and Representation
5.4.4 Case Studies Illustrating the Development of Rule-Based Expert Systems for Drug Discovery and Development
5.5 Applications of Fuzzy Logic in Predictive Modeling
5.5.1 Introduction to Fuzzy Logic and Fuzzy Sets
5.5.2 Fuzzy Inference Systems for Drug Development
5.5.3 Case Studies Demonstrating the Application of Fuzzy Logic in Predicting Pharmacokinetic Parameters, Toxicity, etc.
5.6 Bayesian Networks in Drug Development
5.6.1 Basics of Bayesian Networks
5.6.1.1 Applications of Bayesian Networks in Drug Development
5.6.1.2 Advantages of Utilizing BNs in Pharmaceutical Research
5.6.2 Bayesian Networks for Predicting Drug-Target Interactions, Drug Efficacy, Adverse Effects, etc.
5.6.3 Challenges and Opportunities in Using Bayesian Networks for Predictive Modeling in Drug Development
5.7 Integration of Predictive Models in Drug Development Workflow
5.7.1 Incorporating Predictive Models into Decision-Making Processes
5.7.2 Challenges in Integrating Predictive Models with Experimental Data
5.7.3 Real-World Examples of Successful Integration of Predictive Models in Drug Development Pipelines
5.8 Validation and Evaluation of Predictive Models
5.8.1 Importance of Model Validation and Evaluation
5.8.2 Validation Techniques: Cross-Validation, Bootstrapping, External Validation, etc.
5.8.2.1 Validation Techniques
5.8.3 Performance Metrics for Evaluating Predictive Models in Drug Development
5.8.4 Considerations for Selecting Appropriate Validation Methods Based on the Type of Predictive Model
5.9 Future Perspectives and Emerging Trends
5.9.1 Advances in Predictive Modeling Techniques for Drug Development
5.9.2 Role of Artificial Intelligence and Machine Learning in Enhancing Predictive Modeling Capabilities
5.9.3 Challenges and Opportunities in the Future of Predictive Modeling in Pharmaceutical Research
5.10 Conclusion
5.10.1 Drug Development
5.10.1.1 Improved Drug Discovery
5.10.1.2 Personalized Medicine
5.10.1.3 Integration of Multi-Omics Data
5.10.1.4 Enhanced AI Algorithms
5.10.1.5 Big Data Analytics
5.10.1.6 Collaborative Research Efforts
References
6. Adverse Impact of Human Data Science in Pharmacovigilance (HDS-PV) and Their Potential Applications
B. Prabadevi, M. Pradeepa, S. Sudhagara Rajan and S. Kumaraperumal
6.1 Introduction
6.2 Pharmacovigilance
6.2.1 Introduction to Pharmacovigilance
6.2.2 Phases in Pharmacovigilance
6.3 Human Data Science in Pharmacovigilance
6.3.1 Human Data Science
6.3.2 Data for Human Data Science in Pharmacovigilance (HDS-PV)
6.3.3 Medical Data for Pharmacovigilance
6.3.4 Techniques in Human Data Science
6.3.4.1 Data Mining
6.3.4.2 Disproportionality
6.3.4.3 Change-Point Analysis (CPA)
6.3.4.4 Geographical Information Systems (GIS)
6.3.4.5 Natural Language Processing and Its Application
6.3.4.6 Artificial Intelligence Methodologies
6.3.4.7 Data Visualization
6.4 Challenges in the Amalgamation of Human Data Science and Pharmacovigilance
6.4.1 Potential Risks in Pharmacovigilance
6.4.2 Data Challenges in HDS-PV
6.4.3 Various Errors in the Process
6.4.4 Legal Issues and Concerns
6.4.5 Other Challenges
6.5 Future Research Prospects
6.5.1 Federated Learning for Pharmacovigilance
6.5.2 Explainable AI to Avoid Transparency Issues
6.5.3 Blockchain for Enhanced Security
6.5.4 6G and Beyond for Pharmacovigilance
6.6 Conclusion
References
7. Data Science for Product Lifecycle Management
Bhagyashree N. Singh, Shivani Gandhi and Nisha Parikh
Abbreviation
7.1 Introduction
7.1.1 The Beginner’s Guide to Product Lifecycle Management
7.1.2 Alliteration Techniques in Data Science of Product Lifecycle Management
7.2 Role of Data Science in Preclinical Trial Studies for Product Lifecycle Management
7.2.1 Clinical Trial Organizations Significantly Improving Pharmaceutical Manufacturing
7.2.1.1 Enhancing Efficiency with the Internet of Things (IoT) in Pharma
7.2.1.2 Integrating the Internet of Things in Pharmaceutical Manufacturing
7.2.1.3 Techniques for Integrating the Internet of Things in Waste Management Systems
7.3 Exploring Data Science Applications in Active Ingredient Management
7.3.1 Data Science: A Catalyst for Advancement in Protein Design
7.3.1.1 Assessing Risks of AI-Designed Protein
7.3.2 Role of Artificial Intelligence/Machine Learning in Modern Pharmacology
7.4 Machine Learning Algorithms for Toxicity Prediction
7.4.1 Machine Learning Tools Used in Drug Development
7.5 Redefining R&D Efficiency in Pharma through Data Science
7.6 Intersection of Data Science and Pharmacovigilance
7.6.1 The Challenges of Data Science in Pharmacovigilance
7.7 Ways to Enhance Product Lifecycle Management Stability in Data Science
7.7.1 Data Science Improving Quality Management System
7.7.2 Impactful Data Science Trends in the Pharmaceutical Industry
7.7.3 Utilization of Data Science in Pharma Regulations
7.8 Optimizing Product Lifecycle with Data Science
7.9 Conclusion
References
8. Data Science for Quality Management
Dixa A. Vaghela, Amit Z. Chaudhari, Pankti C. Balar, Anup Kumar, Hetvi Solanki and Vivek P. Chavda
8.1 Introduction
8.2 Literature Review
8.2.1 Historical Context of Quality Management
8.2.2 Evolution of Data Science
8.2.3 Integration of Data Science in Quality Management
8.2.3.1 Data Quality Management
8.2.3.2 Process Monitoring and Control
8.2.3.3 Root Cause Analysis
8.2.3.4 Optimization and Design of Experiments
8.2.4 Key Theories and Frameworks
8.2.4.1 Total Data Quality Management (TDQM)
8.2.4.2 Six Sigma
8.3 Data Quality Dimension
8.3.1 Definition of Data Quality Dimensions
8.3.2 Key Dimensions of Data Quality
8.3.2.1 Timeliness
8.3.3 Measuring Data Quality
8.4 Data Quality Management
8.4.1 Overview of Data Quality Frameworks
8.4.2 Components of a Data Quality Framework
8.4.2.1 Data Profiling and Assessment
8.4.2.2 Data Governance and Stewardship
8.4.2.3 Data Cleansing and Enrichment
8.4.2.4 Continuous Monitoring and Improvement
8.4.3 Common Data Quality Frameworks
8.4.3.1 DAMA DMBOK
8.4.3.2 COBIT
8.4.3.3 ITIL
8.5 Challenges and Barriers
8.5.1 Common Challenges in Data Quality Management
8.5.1.1 Data Accuracy and Integrity
8.5.1.2 Completeness of Data
8.5.1.3 Data Consistency Across Platforms
8.5.1.4 Timeliness of Data
8.5.1.5 Relevance to Quality Management Goals
8.5.2 Barriers to Implementing Data Science in Quality Management
8.5.2.1 Technological Barriers
8.5.2.2 Organizational Resistance to Change
8.5.2.3 Skills Gap in the Workforce
8.5.2.4 Data Privacy and Security Concerns
8.5.2.5 Financial Constraints
8.5.3 Strategies to Overcome Challenges
8.5.3.1 Investing in Scalable Technological Infrastructure
8.5.3.2 Promoting Organizational Change and Cultivating a Data-Driven Culture
8.5.3.3 Addressing the Skills Gap and Enhancing Workforce Readiness
8.5.3.4 Ensuring Data Privacy and Security Compliance
8.5.3.5 Implementing Cost-Effective Solutions for SMEs
8.6 Future Directions
8.6.1 Emerging Trends in Data Science and Quality Management
8.6.1.1 Big Data Analytics
8.6.1.2 Predictive and Prescriptive Analytics
8.6.1.3 Cloud-Based Quality Management Systems (QMS)
8.6.1.4 Advanced Data Visualization
8.6.2 The Role of Artificial Intelligence
8.6.2.1 AI-Powered Quality Control
8.6.2.2 Predictive Maintenance with AI
8.6.2.3 AI in Customer Feedback Analysis
8.6.2.4 AI for Continuous Improvement
8.7 Conclusion
References
9. Data Science for Validation
Shiwali Sharma, Narinderpal Kaur, Gagandeep Kaur and Parul Sood
9.1 Introduction
9.1.1 Overview of Validation in Data Science
9.1.2 Definition of Validation
9.1.3 Types of Validation
9.1.4 Accepting and Relating the Types of Validation
9.2 Importance of Validation
9.2.1 Why Validation is Crucial for Data Science Projects
9.2.2 Risks and Consequences of Neglecting Validation
9.2.2.1 Inaccurate Predictions
9.2.3 Addressing the Risks — Strategies for Effective Validation
9.2.4 Validation as an Iterative Process
9.3 Data Validation
9.3.1 Methods for Validating Data Quality and Integrity
9.3.1.1 Addressing Common Issues in Data Validation
9.3.2 Model Validation
9.3.2.1 Techniques for Validating Predictive Models
9.3.3 Process Validation
9.3.3.1 Importance of Validating Data Processing Pipelines
9.4 Validation Techniques and Tools
9.4.1 Statistical Methods
9.4.2 Machine Learning Techniques
9.4.3 Validation Tools
9.5 Challenges in Data Science Validation
9.5.1 Data Challenges
9.5.2 Model Challenges
9.5.3 Addressing Data and Model Challenges
9.6 Case Studies
9.6.1 Case Study: Manufacturing Predictive Maintenance
9.6.2 Case Study: Fraud Detection in Financial Transactions
9.7 Future Trends in Data Science Validation
9.7.1 Emerging Trends and Technologies
9.7.2 Role of AI and Automation in Improving Validation Processes
9.8 Conclusion
References
Part 3: Advanced Topics and Future Prospects
10. Data Science and Classification of Medical Data for Pharmacovigilance
Rutvi Vaidya, Bhavin Vyas, Shrikant Joshi, Sonia Singh, Dhwani Desai and Preeti Bhatt
10.1 Introduction to Data Science
10.2 Data Processing
10.2.1 Data Gathering
10.2.2 Data Cleaning
10.2.3 Data Integration
10.2.4 Data Transformation
10.2.5 Data Storage
10.2.6 Data Analysis
10.2.7 Data Visualization
10.3 Types of Healthcare Data
10.3.1 Clinical Data
10.3.2 Administrative Data
10.3.3 Financial Data
10.3.4 Patient-Generated Data (PGD)
10.3.5 Public Health Data
10.3.6 Claims Data
10.3.7 Data from Wearable Devices
10.4 Classification of Medical Data Using Data Science
10.4.1 The Importance of Medical Data Classification
10.4.2 Challenges in Medical Data Classification
10.4.3 Data Mining Techniques for Medical Data Classification
10.4.4 Machine Learning Approaches
10.4.5 Case Studies in Medical Data Classification
10.4.6 Future Directions in Medical Data Classification
10.5 Role and Significance of Data Science in Pharmacovigilance
10.5.1 What is Data Science?
10.5.2 What is Pharmacovigilance?
10.5.3 Search Strategy
10.5.4 Various Applications of Data Science
10.6 Data Processing Algorithms — AI, ML, and DL
10.6.1 AI and ML Algorithms for Pharmacovigilance
10.6.2 Challenges and Considerations in Adopting AI/ML for Pharmacovigilance
10.6.3 The Future of Pharmacovigilance with AI/ML
10.6.4 Advancements in Deep Learning for Pharmacovigilance
10.6.5 Challenges and Limitations for Deep Learning in Pharmacovigilance
10.7 Predictive Models for Adverse Drug Reaction Detection
10.7.1 Introduction to Pharmacovigilance and Its Importance in Healthcare Analytics
10.7.2 Predictive Models in Pharmacovigilance: From Logistic Regression to Neural Networks
10.7.3 Challenges and Future Directions in Predictive Modeling for Pharmacovigilance
10.7.4 Key Insights and Perspectives of Predictive Model in Pharmacovigilance
10.8 Application in Regulatory Attainment
10.9 Availability of Open-Source Tools
10.9.1 Introduction
10.9.2 Data Collection and Management
10.9.3 Data Integration and Interoperability
10.9.4 Data Repositories and Ontologies
10.10 Future Prospects and Ethical Considerations
10.11 Conclusion
References
11. Data Science for Analytical Development and Quality Control
Kunjan Bodiwala, Rahul Lalwani, Zalak Jain and Anuradha Gajjar
11.1 Introduction
11.2 Importance of Analytical Development and Quality Control in Pharmaceutical Industry
11.3 Digitalization and Data Science in Pharma 4.0
11.4 Data Science Tools for Process Development
11.4.1 Process Understanding
11.4.2 Product Understanding
11.4.3 Key Tools Relevant to Process Development
11.4.4 Specific Tools Relevant to the Analytical Development Stage
11.5 Role of Data Science in Analytical Development and Quality Control
11.5.1 Applications of Data Science in Analytical Laboratories
11.5.1.1 Automation and Efficiency
11.5.1.2 Sample Preparation and Analysis
11.5.1.3 Data Management and Laboratory Information Management Systems (LIMS)
11.5.1.4 Quality Assurance and Monitoring
11.5.1.5 Statistical Process Control (SPC)
11.5.1.6 Predictive Analytics and Risk Mitigation
11.5.1.7 Machine Learning for Method Optimization
11.5.1.8 Algorithmic Approaches to Method Development
11.5.1.9 Predictive Modeling for Method Validation
11.5.1.10 Real-Time Data Visualization
11.5.1.11 Interactive Dashboards and Key Performance Indicators (KPIs)
11.5.1.12 Collaborative Data Sharing
11.5.2 Case Studies in Data Science Integration
11.6 Applications of Data Science in Quality Control
11.6.1 Predictive Models for Drug Development
11.6.2 Application of Machine Learning
11.6.3 Forecasting Patient Flow and Demand
11.6.4 Time Series Analysis and Demand Forecasting
11.6.5 Integrating External Factors
11.6.6 Real-Time Analysis and Process Verification
11.6.7 Implementing Advanced Sensors and IoT
11.6.8 Benefits of Real-Time Analysis
11.6.9 Statistical Quality Control and Process Monitoring
11.6.10 Control Charts and Process Capability Analysis
11.6.11 Data Science Enhancements
11.6.12 Continued Process Verification (CPV) Using Data Science
11.6.13 Implementing a CPV Framework
11.6.14 Risk Assessment and Mitigation
11.6.15 Improving Process Robustness
11.6.16 Designing Robust Processes
11.6.17 Continuous Learning and Adaptation
11.6.18 Case Studies
11.7 Challenges and Solutions
11.8 Future Directions and Trends
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page