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

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.
Related Programs

Food and Drug Administration_Research

Department of Health and Human Services


Agency: Department of Health and Human Services

Office: Food and Drug Administration

Estimated Funding: $300,000


Relevant Nonprofit Program Categories





Obtain Full Opportunity Text:
http://Click on Application Tab above to download full announcement instructions and application package.

Additional Information of Eligibility:
Agricultural Producers, Agricultural Producer Groups, Farmer or Rancher Cooperatives, or Majority-Controlled Producer-Based Business

Full Opportunity Web Address:
http://Click on Application Tab above to download full announcement instructions and application package.

Contact:


Agency Email Description:
terrin.brown@fda.hhs.gov

Agency Email:


Date Posted:
2024-01-15

Application Due Date:


Archive Date:
2024-04-30


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