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. 

Nonprofit

• Other Native American tribal organizations
• Nonprofits non-higher education with 501(c)(3)
• Nonprofits non-higher education without 501(c)(3)

Government

• Special district governments
• Federally recognized Native American tribal governments
• County governments
• Public and Indian housing authorities
• State governments
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Business

• For-profit organizations other than small businesses
• Small businesses

Miscellaneous

• Unrestricted

Education

• Independent school districts
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• Public and state institutions of higher education