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Forecasted

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

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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 FY2024–FY2026, from public federal spending records.

FY2024 obligated
$250.8M
FY2025 obligated
$213M
FY2026 (to date) obligated
$65.5M
Awards in window
920

Top recipients: The Critical Path Institute, National Environmental Health Association, Association of Food & Drug Officials, The Medical Device Innovation Consortium, Food & Agriculture, California Department of

Source: USAspending.gov · refreshed August 2026

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. 

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How to apply

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View on Grants.gov

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

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