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We note that many of these areas of consideration bear a striking resemblance to issues that FDA’s Center for Devices and Radiological Health (CDRH) has been tackling in relation to AI-enabled medical devices for the past few years. We anticipate that thinking and findings from the medical device world may be informative in the pharmaceutical manufacturing space. For example, might predetermined change control plans be deployed for continuously learning AI systems in drug manufacturing, in addition to the regulation of AI-enabled medical devices?
The paper also raises familiar and critical issues of data integrity, explainability, and ability to transfer learning between models, applying them to the manufacturing side of the house. Does this portend the same level of expected validation of AI models that FDA has come to expect of software as a medical device (SaMD) and clinical decision support software (CDS)?
And, while the discussion paper includes a broad swath of manufacturing issues in its scope, including AI-enabled bioinformatics pipelines to “generate and select candidates for precision medicine complex biological products,” also of note is what is not included: AI in drug supply chain management – an issue of particular and recent importance in the pandemic and its global disruption of supply chains, including pharmaceutical ones. Will separate guidance for AI-enabled supply chain management be forthcoming?
CDER notes that, while there are areas of consideration not covered in the paper, the five areas presented are those where CDER thinks public feedback would be valuable. Arising from these five considerations, CDER is seeking stakeholder input on the following eight questions:
The above questions are not meant to be exhaustive. CDER is interested in any pertinent information the public may have to share arising from the discussion paper. The publication of previous discussion papers in the advanced manufacturing space have been followed by FDA workshops, so a public meeting announcement on AI in drug manufacturing may very well be on the horizon.
Advanced manufacturing momentum
The release of the discussion paper on AI in drug manufacturing is just one example of FDA activity in advanced manufacturing policy development. On March 1, 2023, FDA also issued final guidance on continuous manufacturing of drug substances and drug products, implementing the International Council for Harmonisation (ICH) guideline on the subject.
The pace of policy addressing advanced manufacturing technologies is likely to increase, building upon the 2014 CDER Emerging Technology Program and the 2021 National Academies of Sciences, Engineering, and Medicine (NASEM) report on advanced drug manufacturing.
Further activity, anticipated this coming year, as reflected in the recently enacted Food and Drug Omnibus Reform Act, includes:
Interested in providing comments to FDA?
The comment period for the discussion paper ends May 1, 2023. If you are interested in submitting information to FDA or have any questions about AI in drug manufacturing or related matters, please reach out to the authors of this alert.
 FDA has sent draft guidance on Marketing Submission Recommendations for A Change Control Plan for AI/ML-Enabled Device Software Functions to the White House for review and potential clearance, indicating that the document will soon be ready for publication.
 FDA has been engaged in developing an ICH guideline to standardize terminology and regulatory approach to continuous manufacturing (ICH guideline Q13) for a number of years. On November 16, 2022, ICH finalized and adopted ICH guideline Q13. FDA implemented ICH guideline Q13 by publishing it as final guidance.
 Part of the Consolidated Appropriations Act for 2023, also known as the omnibus appropriations or funding bill.
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