OpportunityTitle: 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
AdditionalInformationOnEligibility: 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:&#8226;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.&#8226;A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.&#8226;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 Applications).
Description: 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 surveillance of complex generic drug products.
GrantorContactEmailDescription: terrin.brown@fda.hhs.gov