Grants / Department of Health and Human Services / Food and Drug Administration

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

Forecast Forecast, no dates yet Opportunity FOR-FD-24-001

• Award ceilingnot stated
• Award floornot stated
• Expected awards1
• Total funding$600,000
• Cost sharingNot required
• Funding typeCooperative Agreement, Grant

Key dates

Who can apply

This opportunity is open to nonprofits, colleges and universities, state and local governments, tribes, small businesses and businesses.

• State governments
• County governments
• City or township governments
• Special district governments
• Independent school districts
• Public and State controlled institutions of higher education
• Native American tribal governments (Federally recognized)
• Public housing authorities/Indian housing authorities
• 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
• For profit organizations other than small businesses
• Small businesses

More grants for nonprofits, colleges and universities, state and local governments, tribes, small businesses, businesses.

Is this a realistic fit?

• No cost sharing or match is required.

What the grant funds

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.

Category: Food and Nutrition, Health.

How to apply

1. Read the full notice and attachments on Grants.gov. The notice is the official source for deadlines and requirements.
2. Make sure your organization has an active SAM.gov registration and Unique Entity ID; registration can take several weeks.
3. Register on Grants.gov and submit through Workspace before the deadline and time given in the notice. Our application guide walks through each step.

Questions go to Terrin Brown, Grants Management Specialist, terrin.brown@fda.hhs.gov, 240-402-7610.

Open the official notice