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

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.