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for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products: Draft Guidance for Industry and Other Interested Parties

DraftCenter for Veterinary Medicine Office of Inspections and Investigations Oncology Center of Excellence Center for Biologics Evaluation and Research Center for Devices and Radiological Health Center for Drug Evaluation and Research Office of the Commissioner,Office of the Chief Medical Officer,Office of Combination Products01/07/2025

Description

This guidance provides recommendations to sponsors and other interested parties on the use of artificial intelligence (AI) to produce information or data intended to support regulatory decision-making regarding safety, effectiveness, or quality for drugs. Specifically, this guidance provides a risk-based credibility assessment framework that may be used for establishing and evaluating the credibility of an AI model for a particular context of use (COU).

Scope & Applicability

Product Classes

3
Combination Product

Products combining drug, device, or biological constituents; Generally recommended for Enhanced Documentation; Requires 14971-based framework incorporating ICH Q9; A drug-device combination where the device constituent part detects ingestion.

Biological Product

Regulated under section 351(i) of the PHS Act; Virus, therapeutic serum, toxin, vaccine, or protein applicable to prevention or treatment; Alternative regulation category for products meeting device definition

Drug and Biological Products

Scope of the electronic transmission guidance

Stakeholders

4
Sponsor

Entity responsible for submitting applications under section 524B

Applicant

Entity submitting development data and knowledge; Entity performing the work process for change

Manufacturer

Entity responsible for submitting NDINs

Quality Control Unit

Responsible for ensuring the overall quality of the final drug product.

Regulatory Context

Attributes

6
Credibility

Trust established through the collection of credibility evidence

Data drift

Phenomenon where AI model performance degrades because development data differs from deployed environment data.

Critical Quality Attribute

The degree of uncertainty can impact the critical quality attribute (CQA) risk ranking

Model Risk

Combination of model influence and decision consequence

Model Influence

Contribution of AI evidence relative to other evidence

Decision Consequence

Significance of an adverse outcome from an incorrect decision

Identified Hazards

Hazards

1
Algorithmic Bias

Potential tendency to produce incorrect results due to training data limitations

Related CFR Sections (7)

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See Also (8)

Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products: Draft Guidance for Industry and Other Interested Parties | Guideline Explorer | BioRegHub