Data Science in Pharmaceutical Development

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 real-world 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.

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.