Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed
Status: Open
Posted date: November 20, 2023
Opportunity ID: 351059
Opportunity number: FOR-FD-24-001
Opportunity category: Discretionary
Agency name: Food and Drug Administration
Agency code: HHS-FDA
Award floor: $0
Award ceiling: $0
Cost sharing required: No
Funding Instrument Types
- Cooperative Agreement
- Grant
Category of Funding Activity
- Food and Nutrition
- Health
Eligible Applicants
- City or township governments
- County governments
- For-profit organizations other than small businesses
- Independent school districts
- Native American tribal governments (Federally recognized)
- 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
- Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education
- Private institutions of higher education
- Public and State controlled institutions of higher education
- Public housing authorities/Indian housing authorities
- Small businesses
- Special district governments
- State governments
Categories (use these for quoted searches)
- agency_code:hhs_fda
- category_of_funding_activity:food_and_nutrition
- category_of_funding_activity:health
- cost_sharing_or_matching_requirement:false
- eligible_applicants:city_or_township_governments
- eligible_applicants:county_governments
- eligible_applicants:for_profit_organizations_other_than_small_businesses
- eligible_applicants:independent_school_districts
- eligible_applicants:native_american_tribal_governments_federally_recognized
- eligible_applicants:native_american_tribal_organizations_other_than_federally_recognized_tribal_governments
- eligible_applicants:nonprofits_having_a_501_c_3_status_with_the_irs_other_than_institutions_of_higher_education
- eligible_applicants:nonprofits_that_do_not_have_a_501_c_3_status_with_the_irs_other_than_institutions_of_higher_education
- eligible_applicants:private_institutions_of_higher_education
- eligible_applicants:public_and_state_controlled_institutions_of_higher_education
- eligible_applicants:public_housing_authoritiesindian_housing_authorities
- eligible_applicants:small_businesses
- eligible_applicants:special_district_governments
- eligible_applicants:state_governments
- funding_instrument_type:cooperative_agreement
- funding_instrument_type:grant
- opportunity_category:discretionary
- status:open
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.