Artificial Intelligence For Drug Product Lifecycle Applications
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Artificial Intelligence for Drug Product Lifecycle Applications
Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery
A Handbook of Artificial Intelligence in Drug Delivery
A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more. This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies. - Focuses on the use of Artificial Intelligence in drug delivery strategies and future impacts - Provides insights into how artificial intelligence can be effectively used for the development of advanced drug delivery systems - Written by experts in the field of advanced drug delivery systems and digital health
Applications of Artificial Intelligence in Pharmaceuticals
Author: Muniasamy, Anandhavalli
language: en
Publisher: IGI Global
Release Date: 2025-07-11
Artificial intelligence (AI) is transforming the pharmaceutical industry by accelerating drug discovery, improving clinical trial design, and optimizing manufacturing processes. Its integration into areas such as polymers science, omics, and hospital administration enhances precision, efficiency, and patient outcomes. The use of advanced technologies is not only streamlining operations but also opening new frontiers for innovation. As AI reshapes the healthcare landscape, it raises important questions about ethics, data integrity, and equitable access, prompting the need for thoughtful implementation and governance. Applications of Artificial Intelligence in Pharmaceuticals offers a comprehensive exploration of how AI is revolutionizing every facet of the pharmaceutical industry, from drug discovery to hospital administration. With a focus on practical implementation, ethical considerations, and future innovations, this book serves as a roadmap to navigating the challenges and opportunities of AI in pharmaceuticals. Covering topics such as data-driven modelling, patient feedback, and smart manufacturing, this book is an excellent resource for pharmaceutical industry professionals, AI and data science practitioners, academicians, researchers, healthcare professionals, regulators, policymakers, entrepreneurs, innovators, and more.