ML in drug delivery-current scenario and future trends
- Title
- ML in drug delivery-current scenario and future trends
- Creator
- Muthudoss P.; Ali H.; Ramasamy G.; Allada R.; Fink E.; Kalaiselvan V.; Jayakrishnan B.; Shahane S.; Ramalingam M.; Kanakaraj L.; Kaliappan I.; Paudel A.
- Description
- Machine learning (ML) has enabled transformative applications and emerged as a domain-agnostic decision-making tool as a virtue of its rapid democratization. The authors believe that a systematic assortment of important publications on this issue is indispensable in this context. In terms of data ingestion, data curation, data preprocessing, data handling, and model cross-validation, this review gathers together several studies that have demonstrated a minimum ML framework approach. In general, ML models are described as black-box models, with limited information supplied about their transparency. The authors propose techniques based on the US Food and Drug Administration (FDA)'s current good ML practice (GMLP) in order to improve the ML framework and minimize the aforementioned gap, especially for data. Considering this, the conversation around a model's logic and interpretability are additionally provided. Explicitly, the authors explore the challenges and constraints that ML execution confronts throughout the development of pharmaceuticals. In this context, a structural approach in statistics is presented to allow the scientist to assess the quality of data and incorporate important ideas and techniques that would be implemented in modern ML. The data analytics tetrahedron proposed here can be applied to data of any size. To further contextualize, selected case studies capturing good practices are highlighted to provide pharmaceutical scientists, pharmaceutical ML enthusiasts, readers, reviewers, and regulatory authorities an exposure to fundamental and cuttingedge techniques of ML and data science with respect to chemistry, manufacture, and control (CMC) of drug products. In addition, the authors believe that leveraging ML within CMC procedures can assist in improving decision-making, increasing quality, and enhancing the speed of pharmaceutical product development. IOP Publishing Ltd 2023. All rights reserved.
- Source
- Advances in Drug Delivery Systems for Healthcare: From concept to clinic, pp. 9-.
- Date
- 2024-01-01
- Publisher
- Institute of Physics Publishing
- Coverage
- Muthudoss P., A2Z4.0 Research and Analytics Private Limited, Chennai, 600062, India; Ali H., Christ University, Bangalore, 560029, India; Ramasamy G., Christ University, Bangalore, 560029, India; Allada R., Novugen Pharma (Malaysia) Sdn Bhd, Hicomglenmarie Industrial Park, 3, Jalan Jururancang U1/21, Selangor, 40150, Malaysia; Fink E., Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, Graz, 8010, Austria; Kalaiselvan V., Indian Pharmacopoeia, Commission, Ministry of Health and Family Welfare, Government of India, Sector 23, Rajnagar, 201002, India; Jayakrishnan B., Business School, Vellore Institute of Technology (VIT), Chennai Campus, Kekottaiyur, 600127, India; Shahane S., The Machine Learning Company, Maharashtra, India; Ramalingam M., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Chennai, 603103, India; Kanakaraj L., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Chennai, 603103, India; Kaliappan I., SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, India; Paudel A., Graz University of Technology, Institute of Process and Particle Engineering, Inffeldgasse 13/3, Graz, 8010, Austria
- Rights
- Restricted Access
- Relation
- ISBN: 978-075035615-2; 978-075035611-4
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Muthudoss P.; Ali H.; Ramasamy G.; Allada R.; Fink E.; Kalaiselvan V.; Jayakrishnan B.; Shahane S.; Ramalingam M.; Kanakaraj L.; Kaliappan I.; Paudel A., “ML in drug delivery-current scenario and future trends,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 22, 2025, https://archives.christuniversity.in/items/show/17817.