Author: [AUTHOR] Published on 6/23/2023 6:00:00 AM

FDA Announces Discussion Paper on Artificial Intelligence and Machine Learning in Drug and Biological Product Development

Article text On May 10, 2023, the US Food and Drug Administration issued a discussion paper entitled “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products.” It is intended to facilitate discussions with stakeholders on the use of artificial intelligence (AI) and machine learning (ML) in drug development to help inform the regulatory landscape in this area.

AI can be described as a branch of science and engineering that uses algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is considered a subset of AI that allows models to be developed by training algorithms through analysis of data, without models being explicitly programmed. In the areas of clinical pharmacology and model-informed drug development (MIDD), AI/ML could be employed to help improve clinical trial design/simulations, optimize dose selection, identify the right patient population or enhance safety evaluations/monitoring.

FDA recognizes the increased use of AI/ML throughout the drug development life cycle and across a range of therapeutic areas. FDA is soliciting feedback on the opportunities and challenges with utilizing AI/ML in drug development and medical device development for drugs. This discussion paper will complement and inform future frameworks on AI/ML in drug development. Specifically, the questions in Section B aim to initiate discussions with stakeholders and solicit feedback on three key areas in the context of AI/ML in drug development:

1. Human-led governance, accountability, and transparency
2. Quality, reliability, and representativeness of data
3. Model development, performance, monitoring, and validation

For more information on the discussion paper and how to comment, please see the Federal Register notice and visit the Artificial Intelligence and Machine Learning for Drug Development webpage.

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This communication was prepared by Office of Clinical Pharmacology, Office of Translational Sciences, CDER, FDA.

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