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 will be 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.
Table of Contents
1. Artificial intelligence applied to drug product lifecycle: present status and future prospects Section I: Fundamentals 2. Artificial Intelligence: the foundation principles 3. Machine learning framework 4. Deep learning 5. Artificial Intelligence: A regulatory perspective Section II: Drug Discovery and Product Development 6. Artificial Intelligence in Healthcare 7. Survey of Machine Learning Techniques in Drug Discovery 8. Automating drug discovery 9. Automated self-optimisation of multi-step reaction and separation processes using machine learning 10. Machine learning for target discovery in drug development 11. Advanced Methods for Product Quality Control 12. Pharmaceutical formulation and manufacturing using particle/powder technology for personalized medicines 13. Computational Approaches in Biopharmaceutical Development and Manufacturing Section III: Preclinical and clinical development 14. Contribution of AI in Preclinical Studies on Drug Discovery and Development 15. Chemistry, Manufacturing, and Controls Content With a Structured Data Management Solution: Streamlining Regulatory Submissions 16. DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy 17. Rethinking clinical trials design in the artificial intelligence era 18. Opportunities and challenges using artificial intelligence in ADME/Tox 19. Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine 20. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective 21. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis 22. Artificial intelligence against COVID-19 Section IV: Post-approval drug product 23. Implementation of AI in drug product post-approval 24. Pharmacovigilance strategy 25. Artificial intelligence and pharmacoeconomics 26. Recent evolutions of machine learning applications in clinical laboratory medicine
Authors
Alberto Pais Professor and Director, Department of Chemistry, Coimbra Chemistry Center, University of Coimbra, Coimbra, Portugal. Alberto Pais is Full Professor at the University of Coimbra and Director of the Department of Chemistry. He is also Head of the macromolecules, Colloids and Photochemistry research group of the Coimbra Chemistry Centre, one of the largest groups of this research centre. Possesses expertise in Physical Chemistry, Polymer Science, Polyelectrolytes and DNA technology, Modeling, Simulation, and Data Science. The main contributions are included in ca. 200 scientific articles, 2 edited book, and 18 book chapters. He also supervised 11completed PhD Thesis and many MSc theses. Participated as Principal Investigator/co-Principal Investigator/PhD Researcher in 18 projects Carla Vitorino Assistant Professor, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal. Professor Carla Vitorino graduated in Pharmaceutical Sciences from the University of Coimbra and obtained the PhD degree in Pharmaceutical Sciences, in the specialty of Pharmaceutics, by the same institution. Currently, she is Assistant Professor at the Faculty of Pharmacy of the University of Coimbra. She has been working on the application of nanotechnology in drug permeation enhancement strategies for transdermal, oral and drug delivery systems to brain targeting, which has resulted in the publication of several scientific papers in peer-reviewed high impact journals. It brings together vast experience in pharmaceutical technology, standing out in the areas of nanotechnology and regulatory science. Her main research interests are nanotechnology, controlled release, and the development of new drug delivery systems in a Quality by Design perspective. Sandra Nunes Coimbra Chemistry Center, Department of Chemistry, University of Coimbra , Portugal.Sandra Nunes is a Junior Researcher at Coimbra Chemistry Center, Department of Chemistry, University of Coimbra, since 2019, collaborating in the teaching component of UC. Her expertise includes the development of coarse-grained models of polyelectrolyte systems, ab initio methods and molecular dynamics simulation applied to nucleic acids behaviour, host-guest interactions, modelling of reaction-pathways and the study of the interactions between drugs/biomolecules and membranes.
She published 41 papers, has 2 book chapters, 1 peer-reviewed conference proceeding, 40 communications in scientific meetings, 2 covers and 1 provisional patent application. She is member of the editorial board of 2 journals, reviewer in several international scientific journals and has been member of the jury panels of dissertations at University of Coimbra and abroad.
T�nia Cova Coimbra Chemistry Center, Department of Chemistry, University of Coimbra , Portugal. T�nia Cova is a Junior Researcher from the Coimbra Chemistry Center, Department of Chemistry, University of Coimbra. She has completed her PhD in Chemistry - Macromolecular Chemistry, from University of Coimbra in 2018. She owes over 10 years of research experience in the field of theoretical and computational chemistry, especially on in silico studies of supramolecular systems for drug delivery, DNA-aptamer/peptide-carbohydrate conjugates to improve drug recognition in biological matrices, and tumor targeting, and on topics machine learning and big data. TC is the author of 4 book chapters, 33 articles, 1 Patent application, 2 special issue editions, 21 oral communications, and 17 conference posters. TC was nominated MC Substitute of COST Action CA19145 PT. She participated as PhD Researcher in 2 projects.
