Value AD
CAN BE
ALTERNATIVE
INVESTMENT
Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed
FOR-FD-24-001
Terrin Brown Grants Management Specialist
Funding Opportunity Number: FOR-FD-24-001
Opportunity Category: Discretionary
Expected Number of Awards: 1
CFDA Number(s): 93.103 -- Food and Drug Administration Research
Cost Sharing or Matching Requirement: No
Posted Date: Nov 20, 2023 12:00:00 AM EST
Closing Date: N/A
Estimated Total Program Funding: 600000
Eligible Applicants: Public and State controlled institutions of higher education,Independent school districts,Special district governments,Native American tribal governments (Federally recognized),Public housing authorities/Indian housing authorities,Native American tribal organizations (other than Federally recognized tribal governments),Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education,State governments,City or township governments,For profit organizations other than small businesses,County governments,Small businesses,Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education,Private institutions of higher education
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 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.
