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The Food and Drug Administration (FDA) has announced a grant opportunity under the number FOR-FD-24-003, focusing on the utilization of real-world data (RWD) and algorithmic analyses to assess post-market clinical outcomes in patients switching among complex generic drug products and reference listed drugs. This cooperative agreement is designed to modernize post-market surveillance approaches for complex generic drugs, a growing segment in the pharmaceutical market. It aims to ensure therapeutic equivalence and inform regulatory decision-making by using advanced technologies like machine learning (ML) and artificial intelligence (AI).
Eligibility for this funding opportunity is broad, encompassing a variety of entities including state governments, educational institutions, small businesses, non-profits, and others. The primary objective of this grant is to develop and test an AI- or ML-based algorithmic RWD model that can efficiently and automatically identify post-market signals, thus facilitating timely regulatory actions. With an award ceiling of $300,000, this funding initiative underscores the FDA’s commitment to leveraging cutting-edge technology in ensuring the safety and efficacy of generic drug products. This approach is critical in the context of the complex generic drug market, where subtle differences in user interfaces compared to reference drugs might impact clinical outcomes. The initiative represents a significant step in enhancing the FDA’s capabilities in monitoring and ensuring public health safety in the rapidly evolving landscape of generic medications.
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Opportunity ID: 351125
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General Information
| Document Type: | Grants Notice |
| Opportunity Number: | FOR-FD-24-003 |
| Opportunity Title: | 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 |
| Opportunity Category: | Discretionary |
| Opportunity Category Explanation: | |
| Funding Instrument Type: | Cooperative Agreement |
| Category of Funding Activity: | Agriculture Consumer Protection Food and Nutrition |
| Category Explanation: | |
| Expected Number of Awards: | 1 |
| CFDA Number(s): | 93.103 — Food and Drug Administration Research |
| Cost Sharing or Matching Requirement: | No |
| Version: | Forecast 3 |
| Forecasted Date: | Nov 24, 2023 |
| Last Updated Date: | Nov 24, 2023 |
| Estimated Post Date: | |
| Estimated Application Due Date: | |
| Estimated Award Date: | |
| Estimated Project Start Date: | |
| Fiscal Year: | 2024 |
| Archive Date: | |
| Estimated Total Program Funding: | |
| Award Ceiling: | $ 300,000 |
| Award Floor: | $ 300,000 |
Eligibility
| Eligible Applicants: | State governments Small businesses Native American tribal governments (Federally recognized) Native American tribal organizations (other than Federally recognized tribal governments) City or township governments Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education Special district governments Private institutions of higher education Public housing authorities/Indian housing authorities Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled “Additional Information on Eligibility” County governments For profit organizations other than small businesses Public and State controlled institutions of higher education Independent school districts |
| Additional Information on Eligibility: | Applicant organizations may submit more than one application, provided that each application is scientifically distinct. The FDA will not accept duplicate or highly overlapping applications under review at the same time per 2.3.7.4 Submission of Resubmission Application. This means that the NIH or FDA will not accept:•A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.•A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.•An application that has substantial overlap with another application pending appeal of initial peer review (see 2.3.9.4 Similar, Essentially Identical, or Identical Applications). |
Additional Information
| Agency Name: | 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. |
| Link to Additional Information: | |
| Grantor Contact Information: |
Terrin Brown
Grantor 2403387494
|
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