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
ID: 351805Type: Posted
Overview

Buyer

Food and Drug Administration (HHS-FDA)

Award Range

$0 - $300K

Eligible Applicants

Unrestricted

Funding Category

Food and Nutrition

Funding Instrument

Cooperative Agreement

Opportunity Category

Discretionary

Cost Sharing or Matching Requirement

Yes

Original Source

https://grants.gov/search-results-detail/351805

Additional Information

https://grants.nih.gov/grants/guide/rfa-files/RFA-FD-24-007.html
Timeline
    Description

    The U.S. Food and Drug Administration (FDA) is offering a funding opportunity aimed at utilizing real-world data (RWD) and machine learning/artificial intelligence (ML/AI) to enhance post-market surveillance of complex generic drug products. The objective is to develop and test an algorithmic RWD model to assess clinical outcomes for patients switching between complex generic drugs and their reference listed drugs (RLDs), addressing the limitations of current surveillance methods that rely heavily on adverse event reporting. This initiative is crucial for modernizing drug safety surveillance and ensuring therapeutic equivalence in an increasingly complex generic marketplace. The funding amount is up to $300,000 for one award, with applications due by April 8, 2024. Interested applicants can reach out to Terrin Brown at terrin.brown@fda.hhs.gov for further information, and additional details can be found in the funding announcement at https://grants.nih.gov/grants/guide/rfa-files/RFA-FD-24-007.html.

    Point(s) of Contact
    terrin.brown@fda.hhs.gov
    Files
    Title
    Posted
    RFA-FD-24-007.html
    The U.S. Food and Drug Administration (FDA) has issued a Notice of Funding Opportunity (NOFO) aiming to utilize real-world data (RWD) and machine learning/artificial intelligence (ML/AI) to enhance post-market surveillance of complex generic drug products. The funding, under Cooperative Agreement U01, targets the development of an RWD algorithmic model to assess clinical outcomes in patients switching between complex generic drugs and reference listed drugs (RLDs). Given the rising complexity and market share of these generic products, current surveillance methods that heavily rely on adverse event reporting have proven limited. The funding opportunity consists of several key deadlines, including application submissions by March 31, 2024, and emphasizes collaboration between awarded institutions and FDA scientists. Participants are encouraged to share generated data with FDA while meeting specific reporting requirements. Up to $300,000 is allocated for one award, with potential for two additional years depending on fund availability and project performance. Eligible applicants include various educational institutions and organizations, with a keen focus on promoting diversity and inclusion in applications. This initiative represents an important step towards modernizing drug safety surveillance methods through innovative technologies and partnerships.
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