Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry and Other Interested Parties; Draft Guidance

The Animal Health Institute (AHI), a national trade association representing animal health product manufacturers, supports the proposed guidance on using AI in regulatory decision-making. They advocate for a risk-based approach and provide specific recommendations to enhance the guidance, including clarifying data requirements, expanding the credibility framework to include various model types (like LLMs and regression models), and incorporating a structured model development plan.

The Connected Health Initiative (CHI) supports the FDA's draft guidance on AI for drug and biological products, particularly its risk-based approach and total product lifecycle perspective. They suggest prioritizing performance testing over training data transparency and urge the FDA to align its policies with existing CDRH guidance and the organization's own health AI principles.

The Personalized Medicine Coalition (PMC) supports the FDA's draft guidance on using AI for regulatory decision-making, praising its risk-based approach and recognition of AI's potential in personalized medicine. They recommend that the final guidance include specific technical standards for data quality, cybersecurity protections, and international harmonization to ensure safe and equitable AI adoption.

The commenter suggests that the FDA should strengthen the draft guidance by requiring sponsors to perform specific bias detection and mitigation as part of their AI model credibility assessments. They argue that these requirements, including subgroup performance analysis and documentation of fairness checks, are necessary to ensure patient safety and regulatory integrity.

The commenter expresses concerns regarding data integrity, bias, and transparency in the use of AI for regulatory decision-making. They support the proposed action but urge the FDA to develop specific guidance on pharmacovigilance, model architecture documentation, and fairness metrics to ensure safety and transparency.

The commenter provides specific technical suggestions to improve the draft guidance on AI in drug and biological product regulation. They recommend clarifying the scope of AI applications, providing concrete examples for context of use, and establishing structured frameworks for model revalidation and documentation.

The Society of Quality Assurance (SQA) provides specific technical feedback and proposed wording revisions to the FDA's draft guidance on AI in drug and biological product regulation. They advocate for clearer definitions, more explicit requirements for monitoring bias and data drift, and the inclusion of risk-based categories aligned with international standards.

The Critical Path Institute, a nonprofit organization focused on medical product development, submitted comments to improve the utility and clarity of the draft guidance on AI in regulatory decision-making. They recommend specific technical additions, such as clarifying the application to generative AI, providing guidance on handling missing data, and establishing standardized documentation templates to align industry expectations.

Ipca Laboratories Ltd., a global pharmaceutical company, supports the FDA's initiative to develop guidance on using AI/ML in regulatory decision-making for drug and biological products. The company suggests that the FDA should specify minimum batch numbers for validation and clarify methodologies for mitigating model bias, while also advocating for streamlined post-approval monitoring and retraining of AI models.

The IMI Big Picture consortium welcomes the draft guidance as a necessary first step for addressing AI in the regulatory space for drug and biological products. They provide specific technical feedback and requests for clarification regarding risk classification, model parameters, explainability, and the scope of the guidance.