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Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry and Other Interested Parties; Draft Guidance
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- Title
- 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Support Regulatory Decision-Making for Drug and Biological Products Guidance for Industry and Other Interested Parties; Draft Guidance
- Posted
- Jan 7, 2025
- Comment period
- Jan 7, 2025 – Apr 8, 2025
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| Organization | Ai and ml validation guidelines | Bias and fairness | Model life cycle maintenance | Combination products | Definition of ai |
|---|---|---|---|---|---|
CognifAI Solutions Pvt Ltd. BusinessSupport CognifAI Solutions Pvt Ltd. | · | · |
11 organization-typed comments could not be identified.
Explorer
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- May 20, 2026Comment from Bhasker SambarSupportIndividual📎 Attachment
Bhasker Sambar, a pharmaceutical development and CMC operations manager, provides specific recommendations to improve the draft guidance on AI in regulatory decision-making. He suggests expanding the scope to include combination products, extending practical examples through all steps, clarifying requirements for third-party AI models, and addressing cybersecurity risks.
Read comment → - Apr 7, 2025Comment from Sharif HoqueSupportIndividual
The commenter provides constructive feedback and suggestions to improve the draft guidance on AI in regulatory decision-making. They recommend expanding the scope to include combination products, adding a glossary of terms, and providing more specific examples of model risk categories and GXP impacts.
Read comment → - Apr 5, 2025Comment from International Consortium for Innovation & Quality in Pharmaceutical Development (IQ Consortium, www.iqconsortium.org)SupportTrade association📎 Attachment
The International Consortium for Innovation & Quality in Pharmaceutical Development (IQ) supports the draft guidance, commending its flexible, risk-based framework for using AI in drug and biologic development. They provide numerous specific recommendations to improve the guidance's clarity on software requirements, reproducibility, data management, model evaluation metrics, and regulatory engagement.
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