Food and Drug Administration

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.

Who can apply

• State governments
• County governments
• City or township governments
• Special district governments
• Independent school districts
• Public and State controlled institutions of higher education
• Native American tribal governments (Federally recognized)
• Public housing authorities / Indian housing authorities
• Native American tribal organizations (other than Federally recognized)
• Nonprofits with 501(c)(3) status (other than higher education)
• Nonprofits without 501(c)(3) status (other than higher education)
• Private institutions of higher education
• For-profit organizations other than small businesses
• Small businesses
• Unrestricted

Contact

terrin.brown@fda.hhs.gov