FDA Report on AI Use in Regulatory Decision-Making for Drugs and Biological Products

Dr. Bani Tcheckanoa
Feb 10
2 min read

Updated: Mar 6

Overview

The FDA draft guidance (January 2025) provides recommendations for the use of Artificial Intelligence (AI) in regulatory decision-making regarding the safety, effectiveness, and quality of drugs and biological products. The guidance is intended for industry sponsors, regulatory applicants, and other stakeholders.

Dr. Bani Tchekanova, ClinReg Partners' resident expert in drug development and regulatory affairs, elaborates on the key takeaways from the report.

Key Considerations

Scope of AI Use
Challenges with AI in Drug Regulation

Risk-Based Credibility Assessment Framework

The FDA proposes a 7-step framework to assess AI model credibility based on risk:

Define the Question of Interest
Define the Context of Use (COU)
Assess AI Model Risk
Develop a Credibility Assessment Plan
Execute the Plan
Document Results & Deviations
Determine Model Adequacy

Life Cycle Maintenance of AI Models

AI models must be continuously monitored and updated to ensure performance and reliability.
AI in pharmaceutical manufacturing requires ongoing validation to manage evolving data inputs.
Changes to AI models may require regulatory notification based on their impact on product quality.

Early FDA Engagement & Regulatory Pathways

The FDA encourages early discussions with sponsors to set expectations for AI credibility assessments. Engagement options include:

Pre-IND meetings for AI-driven clinical development.
Emerging Technology Program (ETP) for AI in pharmaceutical manufacturing.
Real-World Evidence (RWE) Program for AI use in post-market safety and effectiveness studies.

The FDA's guidance aims to standardize AI use in regulatory decision-making by ensuring transparency, reliability, and risk-based oversight. AI models used in drug development must be validated through a structured assessment framework and undergo continuous life cycle management.

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