Grant opportunity · Food and Drug Administration

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

~$600,000 total

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 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 may apply

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

Deadline

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