Public feedback to FDA on regulatory considerations for AI in drug manufacturing

Abstract FDA’s Center for Drug Evaluation and Research (CDER) established the Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) initiative to establish a regulatory framework to support the adoption of advanced manufacturing technologies that could benefit patients. FRAME prioritized artificial intelligence (AI) as a technology that has the potential to advance pharmaceutical manufacturing capabilities. FDA published a discussion paper titled Artificial Intelligence in Drug Manufacturing on March 1, 2023, and held a public workshop on The Regulatory Framework for the Utilization of Artificial Intelligence in Pharmaceutical Manufacturing: An Opportunity for Stakeholder Engagement from September 26–27, 2023. To ensure that FDA’s evaluation of the regulatory framework for AI is thorough, interested parties were invited to comment on the discussion paper and provide feedback through moderated discussions at the public workshop. This paper summarizes public feedback related to data management, governance of data used to build AI/ML models, third-party data, risk-based model development and validation requirements, AI in the pharmaceutical quality system (PQS), lifecycle considerations of AI models, and other aspects of AI in drug manufacturing. In general, interested parties expressed a desire to implement AI, seek assurance that guidance or policies are compatible with current AI strategies in the manufacture of drugs and biological products, and feel that international harmonization will facilitate AI adoption. Key findings from public feedback showed that interested parties value good data management practices, seek best practices for AI models, face uncertainty in managing AI models provided by third parties, and are challenged by implementing AI in the PQS framework.

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Publication Details

Journal
AAPS Open
Published
2025-05-04
DOI
https://doi.org/10.1186/s41120-025-00110-w
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65
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article

Public feedback to FDA on regulatory considerations for AI in drug manufacturing

Riley Myers, Johnny Lam, Manuel I. Osorio, Jayanti Das et al.
1 citations
AAPS Open
Artificial Intelligence in Healthcare and Education
0.65
article

Public feedback to FDA on regulatory considerations for AI in drug manufacturing

Riley Myers, Johnny Lam, Manuel I. Osorio, Jayanti Das, Thomas O’Connor, Adam C. Fisher
article en
1 citations

Abstract

Abstract FDA’s Center for Drug Evaluation and Research (CDER) established the Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) initiative to establish a regulatory framework to support the adoption of advanced manufacturing technologies that could benefit patients. FRAME prioritized artificial intelligence (AI) as a technology that has the potential to advance pharmaceutical manufacturing capabilities. FDA published a discussion paper titled Artificial Intelligence in Drug Manufacturing on March 1, 2023, and held a public workshop on The Regulatory Framework for the Utilization of Artificial Intelligence in Pharmaceutical Manufacturing: An Opportunity for Stakeholder Engagement from September 26–27, 2023. To ensure that FDA’s evaluation of the regulatory framework for AI is thorough, interested parties were invited to comment on the discussion paper and provide feedback through moderated discussions at the public workshop. This paper summarizes public feedback related to data management, governance of data used to build AI/ML models, third-party data, risk-based model development and validation requirements, AI in the pharmaceutical quality system (PQS), lifecycle considerations of AI models, and other aspects of AI in drug manufacturing. In general, interested parties expressed a desire to implement AI, seek assurance that guidance or policies are compatible with current AI strategies in the manufacture of drugs and biological products, and feel that international harmonization will facilitate AI adoption. Key findings from public feedback showed that interested parties value good data management practices, seek best practices for AI models, face uncertainty in managing AI models provided by third parties, and are challenged by implementing AI in the PQS framework.

AAPS OpenVol. 11(1)
Center for Biologics Evaluation and Research (US), Center for Drug Evaluation and Research (US)
Openalex Percentile: Top 25%
Artificial Intelligence in Healthcare and Education
0.65
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