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Monday, May 15, 2023

Exploring Artificial Intelligence & Machine Learning in Drug Development

What is Artificial Intelligence and Machine Learning?

Artificial Intelligence (AI) and Machine Learning (ML) are branches of computer science, statistics, and engineering that use 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 data analysis without models being explicitly programmed.

What role is AI/ML playing in drug development?

FDA recognizes the increased use of AI/ML throughout the drug development life cycle and across various therapeutic areas. FDA has seen a significant increase in drug and biologic application submissions using AI/ML features with more additional than 100 submissions reported in 2021. These submissions traverse the landscape of drug development — from drug discovery and clinical research to postmarket safety surveillance and advanced pharmaceutical manufacturing.  

What is the FDA's perspective on using AI/ML in drug development?

FDA is devoted to guaranteeing that pharmaceuticals are safe and efficacious while stimulating technical inventions. However, as with any innovation, AI/ML creates opportunities and new and unique challenges. To meet these challenges, FDA has accelerated its efforts to create an agile regulatory ecosystem that can facilitate innovation while safeguarding public health.

As part of this effort, FDA's Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), issued an initial discussion paper to communicate with a range of stakeholders and to explore relevant considerations for the use of AI/ML in the development of drugs and biological products. The agency will continue to solicit feedback as it advances regulatory science in this area.

AI/ML will undoubtedly play a critical role in drug development. As a result, the FDA plans to develop and adopt a flexible risk-based regulatory framework that promotes innovation and protects patient safety.


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