forecasted
Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed
Food and Drug Administration · FOR-FD-24-001
See source for award amount
Not specified
County governments, Private institutions of higher education, 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), Public and State controlled institutions of higher education, Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education, City or township governments, State governments, Public housing authorities/Indian housing authorities, Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education, Small businesses, Special district governments
Geographic eligibility and all attachment-specific conditions require review of the original announcement.
Matching funds & resources
Source matching-funds field: Not Required.
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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.
Funding geography: Not specified — review source. A funder’s address does not establish eligibility.
Review original source ↗Funding instrument: Cooperative Agreement, Grant. Issuing jurisdiction: Federal. This is not a geographic eligibility determination.
Source fields and evidence
id: 351059
Source ↗ · id
opportunityNumber: FOR-FD-24-001
Source ↗ · opportunityNumber
opportunityTitle: Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed
Source ↗ · opportunityTitle
revision: 0
Source ↗ · revision
agencyDetails: {"code":"FDA","seed":"HHS-FDA","agencyName":"Food and Drug Administration","agencyCode":"HHS-FDA","topAgencyCode":"HHS"}
Source ↗ · agencyDetails
owningAgencyCode: HHS-FDA
Source ↗ · owningAgencyCode
cfdas: [{"id":415546,"opportunityId":351059,"cfdaNumber":"93.103","programTitle":"Food and Drug Administration Research"}]
Source ↗ · cfdas
forecastDesc: <p><span style="color: black;">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. </span></p>
Source ↗ · forecastDesc
applicantTypes: [{"id":"01","description":"County governments"},{"id":"20","description":"Private institutions of higher education"},{"id":"22","description":"For profit organizations other than small businesses"},{"id":"05","description":"Independent school districts"},{"id":"07","description":"Native American tribal governments (Federally recognized)"},{"id":"11","description":"Native American tribal organizations (other than Federally recognized tribal governments)"},{"id":"06","description":"Public and State controlled institutions of higher education"},{"id":"13","description":"Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education"},{"id":"02","description":"City or township governments"},{"id":"00","description":"State governments"},{"id":"08","description":"Public housing authorities/Indian housing authorities"},{"id":"12","description":"Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education"},{"id":"23","description":"Small businesses"},{"id":"04","description":"Special district governments"}]
Source ↗ · applicantTypes
fundingInstruments: [{"id":"CA","description":"Cooperative Agreement"},{"id":"G","description":"Grant"}]
Source ↗ · fundingInstruments
fundingActivityCategories: [{"id":"FN","description":"Food and Nutrition"},{"id":"HL","description":"Health"}]
Source ↗ · fundingActivityCategories
costSharing: false
Source ↗ · costSharing
lastUpdatedDate: Nov 20, 2023 12:55:59 PM EST
Source ↗ · lastUpdatedDate
status: forecasted
Source ↗ · oppHits[id=351059].oppStatus
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