Forecasted

Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed

Federal funding opportunity FOR-FD-24-001 from Food and Drug Administration.

This forecast was posted November 20, 2023, with no update recorded since. A forecast is an agency’s statement of planned funding rather than an open opportunity, and this one has not moved in over a year. Agencies do not always withdraw plans they have dropped, so confirm it on the official listing at Grants.gov before building plans around it.

View forecast on Grants.gov →Forecasted — not yet open · no update since it was posted

Posted: November 20, 2023
Closes: See announcement
Program funding: $600,000
Expected awards: 1
Cost sharing: No
Instrument: Cooperative Agreement, Grant
Assistance listing: 93.103
Category: Food and Nutrition, Health

Program funding history

Awards made under Assistance Listing 93.103 across FY2025–FY2027, from public federal spending records.

FY2025 obligated: $213M
FY2026 obligated: $215.3M
FY2027 (to date) obligated: $0
Awards in window: 849

Top recipients: The Critical Path Institute, National Environmental Health Association, Association of Food & Drug Officials, Yale Univ, Food & Agriculture, California Department of

Source: USAspending.gov · refreshed October 2026 · Program page: awards, recipients and the opportunities under this listing →

Synopsis

Computational fluid dynamics (CFD) has played a crucial role in providing an alternative bioequivalence (BE) approach for generic orally inhaled drug products (OIDPs), in addition to comparative clinical endpoint or pharmacodynamic BE studies, as a relatively cost- and time-efficient complement to benchtop and clinical experiments that has been widely used in developing and assessing generic inhaler devices. However, despite the advances in the power of modern computers, there are still some bottlenecks in using CFD due to computational time, limited grid resolution, pre- and post-processing of large simulation data sets, model parameter estimations, and uncertainty quantifications. Machine learning (ML) has been gaining more attention as a potential tool to alleviate such limitations that arise in CFD. The purpose of this grant is to develop a methodology to integrate ML with CFD models of OIDPs to promote alternative BE studies to enhance and accelerate the development and approval of generic OIDPs.

Who can apply

Public and State controlled institutions of higher education
Private institutions of higher education
Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education
Special district governments
For profit organizations other than small businesses
State governments
City or township governments
Public housing authorities/Indian housing authorities
Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education
Native American tribal governments (Federally recognized)
Small businesses
County governments
Native American tribal organizations (other than Federally recognized tribal governments)
Independent school districts

How to apply

Applications go through the official government listing. Grants Radar links you straight to the source — applying is free.

View on Grants.gov

Agency contact: Terrin Brown Grants Management Specialist · [email protected] · 240-402-7610

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