Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Dr
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
| Agency | Food and Drug Administration |
|---|---|
| Status | Forecasted |
| Opportunity number | FOR-FD-24-001 |
| Posted date | 11/20/2023 |
| Estimated total funding | 600,000 |
| Expected number of awards | 1 |
| Assistance Listing (CFDA) | 93.103 |
| Description | 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 |
| Agency contact | terrin.brown@fda.hhs.gov |
| Official listing | https://www.grants.gov/search-results-detail/351059 |