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

In plain English

Food and Drug Administration (Department of Health and Human Services) has announced this as a forecast: an opportunity it expects to post. It is not open for applications yet.

The agency expects to make 1 award and estimated total program funding is $600,000.

Eligible applicant types include nonprofits, small businesses, for-profit companies, state governments, local governments, tribal governments & organizations, colleges & universities, school districts and public housing authorities.

The listing says cost sharing or matching is not required.

Built only from the fields in the Grants.gov listing. Always confirm on the official listing.

Browse similar: Nonprofits · Small businesses · For-profit companies · State governments · Local governments · Tribal governments & organizations · Colleges & universities · School districts · Public housing authorities

• State governments
• County governments
• City or township governments
• Special district governments
• Independent school districts
• Public and state-controlled institutions of higher education
• Native American tribal governments (federally recognized)
• Public housing authorities / Indian housing authorities
• Native American tribal organizations (other than federally recognized tribal governments)
• Nonprofits with 501(c)(3) status (other than institutions of higher education)
• Nonprofits without 501(c)(3) status (other than institutions of higher education)
• Private institutions of higher education
• For-profit organizations other than small businesses
• Small businesses

Excerpt from the official forecast on Grants.gov (shortened; read the full text on the official listing):

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. …

Read the full forecast on Grants.gov →