Opportunity Information: Apply for RFA FD 21 027
This funding opportunity, RFA-FD-21-027, is a discretionary cooperative agreement (U01) offered by the U.S. Department of Health and Human Services, Food and Drug Administration (FDA), under CFDA 93.103. It focuses on research and development in science and technology areas tied to FDA's broader consumer protection and public health mission. The project is explicitly labeled "Clinical Trials Not Allowed," meaning the work should rely on methodological development, simulations, and analyses of existing or otherwise appropriate pharmacokinetic datasets rather than initiating interventional studies in humans.
The central goal is to create a new model selection method for population pharmacokinetics (popPK) analyses using a deep-learning based reinforcement learning (RL) approach. In popPK, researchers build mathematical models that describe how drug concentrations change over time across a population while accounting for variability between individuals and the influence of covariates like body weight, age, organ function, or concomitant medications. A persistent challenge in popPK is selecting an appropriate structural model (for example, one- vs two-compartment), the residual error model, and the random effects structure, along with covariate relationships, in a way that is both statistically sound and reproducible. This opportunity targets that exact pain point by asking applicants to develop an RL-driven, deep-learning method that can navigate the model space and recommend or converge on models that perform well for a given dataset and analysis objective.
The scope is not just algorithm development on paper. The FDA is looking for a method that is validated and has its performance verified in a credible, transparent way. That includes simulation-based evaluations (where the "true" model is known so you can quantify how often the method recovers it, how stable it is, and how it behaves under different sampling designs, levels of noise, and degrees of between-subject variability) as well as testing on real PK datasets to demonstrate practicality and usefulness under real-world conditions. In other words, the expected deliverable is a working, evidence-backed model selection approach, not only a conceptual framework. Because the award mechanism is a cooperative agreement, the FDA typically anticipates substantial programmatic involvement, which often means iterative feedback, alignment with regulatory science needs, and deliverables that are usable by the broader pharmacometrics community.
Funding is capped at an award ceiling of $250,000, with an expectation of a single award. The opportunity was created on January 7, 2021, with an original closing date of March 18, 2021. Eligible applicants are broad and include state, county, and local governments; public and private institutions of higher education; federally recognized tribal governments and other tribal organizations; public housing authorities/Indian housing authorities; nonprofit organizations with or without 501(c)(3) status (excluding institutions of higher education where specified); for-profit organizations other than small businesses; small businesses; and other entities as described in the full eligibility text. This broad eligibility suggests the FDA was interested in attracting both academic pharmacometrics groups and industry or technology-focused teams capable of implementing modern deep-learning and reinforcement learning methods.
Overall, the opportunity is aimed at advancing regulatory science by modernizing and potentially standardizing how popPK models are selected, using AI methods that can improve efficiency, consistency, and performance compared with manual, trial-and-error workflows. The work is intended to be demonstrated rigorously through both controlled simulations and application to real pharmacokinetic data, producing a method that could ultimately support better-informed dosing and labeling decisions without conducting new clinical trials under this award.Apply for RFA FD 21 027
- The Department of Health and Human Services, Food and Drug Administration in the consumer protection, food and nutrition, science and technology and other research and development sector is offering a public funding opportunity titled "Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning (U01) Clinical Trials Not Allowed" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.103.
- This funding opportunity was created on Jan 07, 2021.
- Applicants must submit their applications by Mar 18, 2021. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $250,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: 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, Others (see text field entitled Additional Information on Eligibility for clarification).
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