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.
1. Artificial Intelligence for Drug Product Lifecycle Applications - p. ix 2. Cover - p. ix 3. Title Page - p. ix 4. Copyright - p. ix 5. Table of Contents - p. ix 6. Contributors - p. ix 7. About the Editors - p. xi 8. Preface - p. xvi 9. Chapter 1 Artificial intelligence: The foundation principles - p. 1 10. Chapter 2 Artificial intelligence: A regulatory perspective - p. 43 11. Chapter 3 Automating drug discovery - p. 83 12. Chapter 4 Pharmacometrics and machine learning in drug development - p. 99 13. Chapter 5 Multi-omics/genomics in predictive and personalized medicine - p. 109 14. Chapter 6 AI and machine learning in pharmaceutical formulation and manufacturing of personalized medicines - p. 121 15. Chapter 7 Process analytics for the manufacturing of nanomedicines: Challenges and opportunities - p. 169 16. Chapter 8 The role of artificial intelligence and machine learning in clinical trials - p. 205 17. Chapter 9 Artificial intelligence in healthcare - p. 235 18. Index - p. 273
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.
1. Artificial Intelligence for Drug Product Lifecycle Applications - p. ix 2. Cover - p. ix 3. Title Page - p. ix 4. Copyright - p. ix 5. Table of Contents - p. ix 6. Contributors - p. ix 7. About the Editors - p. xi 8. Preface - p. xvi 9. Chapter 1 Artificial intelligence: The foundation principles - p. 1 10. Chapter 2 Artificial intelligence: A regulatory perspective - p. 43 11. Chapter 3 Automating drug discovery - p. 83 12. Chapter 4 Pharmacometrics and machine learning in drug development - p. 99 13. Chapter 5 Multi-omics/genomics in predictive and personalized medicine - p. 109 14. Chapter 6 AI and machine learning in pharmaceutical formulation and manufacturing of personalized medicines - p. 121 15. Chapter 7 Process analytics for the manufacturing of nanomedicines: Challenges and opportunities - p. 169 16. Chapter 8 The role of artificial intelligence and machine learning in clinical trials - p. 205 17. Chapter 9 Artificial intelligence in healthcare - p. 235 18. Index - p. 273
