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Coming Soon·Opens soon·Synced from source July 6, 2026

Utilizing Real-World Data and Algorithmic Analyses to Assess Post-Market Clinical Outcomes in Patients Switching Amongst Therapeutically Equivalent Complex Generic Drug Products and Reference Listed Drugs (U01) Clinical Trial Not Allowed

Food and Drug Administration

Posted
Oct 23, 2025
Amount
$300,000
Closes
No deadline published by the funder
Last verified
Jul 6, 2026

At a glance

This funding opportunity is offered by Food and Drug Administration. Awards are up to $300,000.

Classification and identifiers

Solicitation number
FOR-FD-24-003
Assistance listing (CFDA)
93.103

Amount

$300,000

Who can apply

Local governmentsCounty governmentsSmall businessSpecial districtsPublic universitiesPrivate universities

Applicant organizations may submit more than one application, provided that each application is scientifically distinct. The FDA will not accept duplicate or highly overlapping applications under review at the same time per 2.3.7.4 Submission of Resubmission Application. This means that the NIH or FDA will not accept:•A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.•A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.•An application that has substantial overlap with another application pending appeal of initial peer review (see 2.3.9.4 Similar, Essentially Identical, or Identical Application...

About this opportunity

Complex generic drug products represent an increasing share of the generic marketplace and may have distinct user interface differences compared to reference listed drug (RLD) products. A modernized post-market surveillance approach is needed to compare clinical outcomes between complex generic products and their corresponding RLD products to monitor for potential issues with therapeutic equivalence and to inform regulatory decision making. Real-world data (RWD) combined with machine learning (ML) and/or artificial intelligence (AI) could help to identify post-market signals efficiently in an automated and repeatable fashion, facilitating timely regulatory action. The purpose of this funding opportunity is to develop and test an AI- or ML-based algorithmic RWD model for post-market surveil...

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