Summary
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.
Eligibility
- Native American Tribal Organizations Other Than Federally Recognized Tribal Governments
- Special District Governments
- For Profit Organizations Other Than Small Businesses
- Unrestricted
- Independent School Districts
- Native American Tribal Governments Federally Recognized
- County Governments
- Public and Indian Housing Authorities
- State Governments
- Private Institutions of Higher Education
- City Or Township Governments
- Small Businesses
- Nonprofits Having A 501(c)(3) Status With the Irs Other Than Institutions of Higher Education
- Public and State Institutions of Higher Education
- Nonprofits Non Higher Education Without 501c3
Funding categories
- Consumer Protection
- Food and Nutrition
- Agriculture
Official source and attribution
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