Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed
Food and Drug Administration
Classification and identifiers
- Solicitation number
- FOR-FD-24-001
- Assistance listing (CFDA)
- 93.103
Amount
Amount not published by the funder
Who can apply
State governments, Independent school districts, County governments, Private institutions of higher education, Public and State controlled institutions of higher education, Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education, For profit organizations other than small businesses, Public housing authorities/Indian housing authorities, Small businesses, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education, City or township governments, Native American tribal governments (Federally recognized), Special district governments…
About this opportunity
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...
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