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 Food and Drug Administration (FDA) is seeking applications to develop and test an AI- or ML-based algorithmic RWD model for post-market surveillance of complex generic drug products. The goal is to compare clinical outcomes between complex generic products and their corresponding reference listed drug (RLD) products to monitor for potential issues with therapeutic equivalence and to inform regulatory decision making. Key aspects of the project include: • Developing and testing an AI- or ML-based algorithmic RWD model for post-market surveillance • Using real-world data (RWD) combined with machine learning (ML) and/or artificial intelligence (AI) to identify post-market signals efficiently • Facilitating timely regulatory action • Informing regulatory decision making The FDA will provide funding of up to $300,000 for this project.