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
This grant seeks to develop AI/ML models using real-world data to monitor clinical outcomes when patients switch between complex generics and brand-name drugs.
Funder: Food and Drug Administration
Due Dates: Forecasted (official due date not yet posted)
Funding Amounts: $300,000 (single award; cooperative agreement)
Summary: Supports development and testing of AI/ML models using real-world data to monitor clinical outcomes in patients switching between complex generic and reference drugs.
Key Information: Clinical trials are not allowed; opportunity is currently forecasted, not open.
Description
This funding opportunity from the FDA aims to modernize post-market surveillance of complex generic drug products by leveraging real-world data (RWD) and advanced algorithmic analyses. The goal is to develop and test artificial intelligence (AI) or machine learning (ML) models that can efficiently and automatically identify clinical outcome signals when patients switch between therapeutically equivalent complex generics and their reference listed drugs (RLDs). The results are intended to inform regulatory decision-making and ensure ongoing therapeutic equivalence in the marketplace.
Complex generics often have unique user interface characteristics compared to their brand-name counterparts, which may impact patient outcomes. This initiative seeks to address the need for robust, automated post-market monitoring tools that can detect potential issues in a timely and repeatable manner.
