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Drug Manufacturing Meets Machine Learning: FDA Seeks Feedback on the Use of Artificial Intelligence in Drug Manufacturing

Taking another step towards embracing the use of Artificial Intelligence in prescription drug production, last week, the FDA released the discussion paper “Artificial Intelligence in Drug Manufacturing.” In the paper, FDA indicated its intention to rely on a risk-based regulatory approach for the use of AI in drug manufacturing for the time being while it builds out an appropriate regulatory framework. To build the new framework, the Agency is seeking industry feedback in several areas related to the future approval of drugs manufactured with AI technologies.

The discussion paper provides examples of future AI uses in the space. Among these applications is AI aided:

  • Process design and scale-up to identify optimal production methods;
  • Process monitoring and fault detection to monitor equipment and detect deviations that may impact efficiency and product quality; and
  • Trend monitoring to examine consumer complaints and deviation reports.

Notably, FDA is seeking feedback to understand where the industry sees beneficial uses of AI. Some areas in which FDA seeks feedback include the types of AI applications that the industry envisions being used in pharmaceutical manufacturing, the use of AI for cloud-based submissions, and the feasibility of applying the existing Current Good Manufacturing Practice (“CGMP”) regulations to AI.

While the Agency is crafting its regulatory pathway for AI in prescription drug production, it is important for stakeholders to contribute. The Michael Best Team can assist with the drafting and submission of comments in response to the Agency’s call for industry feedback. We can also assist companies with developing AI implementation and compliance strategies for incorporating new technologies into the drug manufacturing process. 

AI offers many possibilities in the pharmaceutical industry, including but not limited to optimizing process design and process control, smart monitoring and maintenance, and trend monitoring to drive continuous improvement.


fda, healthcare, life sciences, regulatory