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Utilizing Real-World Data and Algorithmic Analyses to Assess Post-Market Clinical Outcomes

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 corre

AgencyFood and Drug Administration
StatusForecasted
Opportunity numberFOR-FD-24-003
Posted date11/24/2023
Award ceiling300,000
Award floor300,000
Expected number of awards1
Who can applyApplicant 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 resubmiss
Assistance Listing (CFDA)93.103
DescriptionComplex 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
Agency contactterrin.brown@fda.hhs.gov
Official listinghttps://www.grants.gov/search-results-detail/351125

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