Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed
ID: 351803Type: Posted
Overview

Buyer

Food and Drug Administration (HHS-FDA)

Award Range

$0 - $300K

Eligible Applicants

Unrestricted

Funding Category

Food and Nutrition

Funding Instrument

Cooperative Agreement

Opportunity Category

Discretionary

Cost Sharing or Matching Requirement

Yes

Original Source

https://grants.gov/search-results-detail/351803

Additional Information

https://grants.nih.gov/grants/guide/rfa-files/RFA-FD-24-005.html
Timeline
    Description

    The U.S. Food and Drug Administration (FDA) has announced a funding opportunity titled “Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01)”, aimed at enhancing the bioequivalence assessment of generic orally inhaled drug products (OIDPs). This grant seeks to integrate machine learning (ML) with computational fluid dynamics (CFD) to streamline data processing, improve simulation speeds, and develop multiscale models for better prediction of drug deposition, while explicitly stating that clinical trials are not permitted under this funding opportunity. The FDA emphasizes the importance of this initiative in advancing the approval processes for generic OIDPs through innovative technological integration. The funding amount is capped at $300,000 for a single award, with applications due by March 31, 2024, and the application window opening on January 15, 2024. Interested applicants can reach out to Terrin Brown at terrin.brown@fda.hhs.gov for further information.

    Point(s) of Contact
    terrin.brown@fda.hhs.gov
    Files
    Title
    Posted
    RFA-FD-24-005.html
    The Department of Health and Human Services, through the U.S. Food and Drug Administration (FDA), announced a funding opportunity titled “Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01)”. This grant aims to enhance the bioequivalence assessment of generic orally inhaled drug products (OIDPs) by integrating machine learning (ML) with computational fluid dynamics (CFD). The objectives include streamlining data processing, improving simulation speeds, developing multiscale models for better prediction of drug deposition, and enabling uncertainty quantification. Notably, clinical trials are not permitted under this funding opportunity. The application window opens on January 15, 2024, with an application deadline set for March 31, 2024. Funding of up to $300,000 is earmarked for one award, covering a maximum project period of two years. Participating organizations, including higher education institutions and nonprofits, are encouraged to apply, particularly those with a focus on diverse backgrounds and underrepresented groups. This initiative reflects the FDA’s commitment to advancing the approval processes for generic OIDPs through innovative technological integration while establishing clear guidelines for application submissions and requirements.
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