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Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry and Other Interested Parties; Draft Guidance

Eigen Bio, a biotechnology company, supports the draft guidance and suggests several improvements to enhance its scope and practical application. They recommend including preclinical AI models, providing specific guidance for federated learning, creating accelerated pathways for orphan drugs, and establishing a centralized AI advisory point for early-stage sponsors.

The LUNGevity Foundation, a lung cancer nonprofit, supports the draft guidance on using AI in regulatory decision-making for drug and biological products. They recommend providing more specific guidance on early consultation with the FDA, methodological transparency (including an optional template), defining the context of use across different programs, and clarifying risk assessment and performance evaluation metrics.

The Connected Health Initiative (CHI) supports the FDA's draft guidance on AI for drug and biological products, particularly its risk-based approach and total product lifecycle perspective.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 commenter suggests that the FDA provide more specific guidance on life cycle maintenance and change control for AI models in drug development. They recommend incorporating elements like a model maintenance plan and adapting the Predetermined Change Control Plan (PCCP) framework from device regulation.

Global Key Solutions Corp. provides detailed feedback on the draft guidance, suggesting specific terminology changes and clarifications to ensure AI's role is accurately represented as "assisting" rather than "supporting" decisions. They also advocate for clearer definitions regarding human oversight, life cycle maintenance, and the role of the Quality Unit in overseeing AI systems.

The commenter suggests that the FDA should explicitly include guidance on the use of publicly available AI foundation models, such as ChatGPT and Gemini, in the draft guidance. They argue that because these models lack transparency and can change frequently, the FDA should provide specific requirements for monitoring, risk assessment, and documentation to ensure regulatory consistency.

The PharmAgility Consulting Group supports the draft guidance but recommends that the FDA enhance it by explicitly addressing data provenance, lifecycle-based performance verification, and the foundational role of cybersecurity in AI governance. They argue that these additions are necessary to ensure that AI tools in the pharmaceutical and biotechnology sectors are reliable, secure, and fit for purpose.

Dr. Lidiko Ziegler, an independent pharmaceutical quality expert, supports the draft guidance and suggests specific additions to improve its utility. The commenter recommends including requirements for data quality, a structured AI validation framework (pre-, during, and post-validation), clear triggers for model retraining, and the establishment of a database for post-market surveillance.

CognifAI Solutions Pvt Ltd. supports the draft guidance and provides specific feedback regarding their AI-enabled pharmacovigilance tool, CoVigilAI. They request further clarification on regulatory thresholds for AI-detected safety signals, specific validation protocols, and frameworks for accountability and bias mitigation.