Opportunity Information: Apply for RFA FD 16 026
Apply for RFA FD 16 026
- The HHS-FDA in the consumer protection, food and nutrition, health sector is offering a public funding opportunity titled "Implementing Population Pharmacokinetic Modeling Algorithm in Physiologically-based Pharmacokinetic Models to Allow Parameter Estimation at Individual Data Level (U01)" 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 Mar 18, 2016 and posted on Mar 18, 2016.
- Applicants must submit their applications by May 16, 2016. (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 2 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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Opportunity Summary:
This funding opportunity, RFA FD 16 026, is a cooperative agreement (U01) offered by HHS-FDA under CFDA 93.103 in the broad area of consumer protection, food and nutrition, and health. Its central goal is to advance the way physiologically based pharmacokinetic (PBPK) models are analyzed by adding a practical, reliable population pharmacokinetic (PopPK) parameter-estimation capability that works at the individual data level. In plain terms, the agency is looking for a team to build and implement a robust optimization algorithm that can fit complex PBPK models to data from individuals, then use those individual-level fits to learn how key physiological and drug-related parameters vary across a population.
The scientific problem this opportunity targets is that PBPK models can be highly detailed and computationally heavy, which makes standard population modeling approaches difficult to apply. Traditional PopPK tools are often designed around simpler compartment models and may not scale well to PBPK structures that include many organs, blood flows, tissue partitions, enzyme and transporter processes, and other mechanistic components. FDA is seeking an algorithmic solution that can handle the computational intensity while still delivering statistically defensible population inferences, meaning it should enable estimation of parameter distributions (for example, means, variances, and potentially correlations) for the population(s) of interest based on individual-level observations.
A key deliverable implied by the announcement is an implemented method, not just a theoretical concept. The work is expected to produce a robust optimization approach that can be used in practice to perform population-based statistical analysis with PBPK models. That typically means the algorithm should be stable, reproducible, and capable of converging under realistic conditions, including sparse or noisy clinical pharmacokinetic datasets, variability between individuals, and the high dimensionality common in PBPK parameter sets. While the notice does not prescribe the exact statistical framework, the intent is clear: enable population-level inference from individual-level parameter estimation in a way that is computationally feasible for PBPK.
The reason FDA is investing in this capability is regulatory impact, especially in the context of generic drug development. The PBPK models produced using the developed algorithm are intended to generate predictions that can inform bioequivalence assessments and support regulatory decision-making. Bioequivalence evaluation often requires understanding how formulation differences might affect exposure across diverse patients, not just an average subject. By estimating parameter distributions in relevant populations, PBPK predictions can better reflect real-world variability and help answer questions that matter for generic approvals, such as performance under different physiological conditions or in subpopulations where clinical studies may be limited.
On eligibility and administrative details, the opportunity was open broadly to many organization types, including federal-recognized tribal governments and organizations, state and local governments, public and private institutions of higher education, nonprofits (with and without 501(c)(3) status), public housing authorities/Indian housing authorities, and for-profit entities (including small businesses). The announcement listed an award ceiling of $250,000 and anticipated making two awards. The original and current closing date were both May 16, 2016, and the opportunity was created and posted on March 18, 2016.
Overall, this U01 focuses on building a bridge between mechanistic PBPK modeling and population-statistical inference at the individual level, with the practical end goal of producing modeling tools and workflows that FDA and the broader scientific community can use to support bioequivalence determinations and generic drug regulatory evaluations.
Frequently Asked Questions (FAQ)
What is the funding opportunity number and who is offering it?
The opportunity is RFA FD 16 026. It is offered by HHS-FDA and is funded as a cooperative agreement (U01) under CFDA 93.103.
What kind of award mechanism is this?
This is a cooperative agreement (U01). That means it is a grant-type award where substantial involvement from the funding agency is expected compared with a standard grant.
What is the broad topic area of this opportunity?
The opportunity is in the broad area of consumer protection, food and nutrition, and health, with a specific scientific focus on advancing PBPK and population PK analysis methods.
What is the main goal of the project described in this announcement?
The central goal is to improve how physiologically based pharmacokinetic (PBPK) models are analyzed by adding a practical, reliable population pharmacokinetic (PopPK) parameter-estimation capability that works at the individual data level.
In practical terms, what is FDA asking applicants to build?
FDA is looking for a team to build and implement a robust optimization algorithm that can fit complex PBPK models to individual-level data and then use those individual fits to learn how key physiological and drug-related parameters vary across a population.
What problem is FDA trying to solve with this funding?
The problem is that PBPK models are often highly detailed and computationally intensive, making standard population modeling approaches hard to apply. Traditional PopPK tools are typically built for simpler compartment models and may not scale well to PBPK structures with many organs, blood flows, and mechanistic processes.
What does "population inference" mean in the context of this opportunity?
Here, population inference refers to estimating population-level parameter distributions (for example, means, variances, and potentially correlations) for the population(s) of interest, using information derived from individual-level observations and individual-level model fits.
Does the announcement require a particular statistical framework?
No specific statistical framework is prescribed in the provided description. The intent is to enable population-level inference from individual-level parameter estimation in a way that is computationally feasible for PBPK models.
Is FDA looking for a theoretical method or an implemented tool?
An implemented method is implied, not just a theoretical concept. The expected output is a robust optimization approach that can be used in practice for population-based statistical analysis with PBPK models.
What does "robust" imply for the optimization algorithm?
Based on the description, "robust" implies the method should be stable, reproducible, and able to converge under realistic conditions, including sparse or noisy clinical pharmacokinetic datasets, variability between individuals, and high-dimensional PBPK parameter sets.
Why are PBPK models challenging for traditional PopPK tools?
PBPK models can include many mechanistic components (organs, blood flows, tissue partitions, enzymes, transporters, and more). That level of detail increases dimensionality and computational load, which can make standard approaches designed for simpler models difficult to apply effectively.
How does individual-level fitting relate to population-level results in this project?
The described approach is to first fit complex PBPK models to data from individuals (individual-level parameter estimation) and then use the collection of individual fits to estimate how parameters vary across the population (population-level parameter distributions).
What is the intended regulatory impact of this work?
The intended impact is to support regulatory decision-making, especially for generic drug development. The PBPK models and methods produced are intended to generate predictions that can inform bioequivalence assessments.
How does this relate to bioequivalence for generic drugs?
Bioequivalence evaluations often need insight into how exposure may differ across diverse patients, not only an average subject. By estimating parameter distributions in relevant populations, PBPK predictions can better reflect real-world variability and help address questions important for generic approvals.
What kinds of datasets or data issues does the method need to handle?
The description indicates the approach should work under realistic data conditions, including sparse or noisy clinical pharmacokinetic datasets and variability between individuals.
How many awards were anticipated under this opportunity?
Two awards were anticipated.
What was the award ceiling for this opportunity?
The listed award ceiling was $250,000.
Who was eligible to apply?
The opportunity was open broadly to many organization types, including federal-recognized tribal governments and organizations, state and local governments, public and private institutions of higher education, nonprofits (with and without 501(c)(3) status), public housing authorities/Indian housing authorities, and for-profit entities (including small businesses).
What were the posting and closing dates?
The opportunity was created and posted on March 18, 2016. The original closing date and the current closing date were both May 16, 2016.
What is CFDA 93.103 in this context?
CFDA 93.103 is the program identifier listed for this FDA funding opportunity. In the provided information, it is associated with the cooperative agreement supporting this PBPK/PopPK method development work.
What is the overall theme of the project in one sentence?
This U01 aims to bridge mechanistic PBPK modeling with population-statistical inference by enabling practical individual-level parameter estimation and population distribution estimation to support FDA bioequivalence and generic drug evaluations.
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| Evaluation of Aberrant Observations and Their Impact on Bioequivalence Assessment (U01) Apply for RFA FD 16 018 Funding Number: RFA FD 16 018 Agency: HHS-FDA Category: Consumer Protection, Food and Nutrition, Health Funding Amount: $200,000 |
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| Bioequivalence of Topical Products: Comparing Dermal Pharmacokinetics by Microdialysis or Microperfusion Techniques (U01) Apply for RFA FD 16 028 Funding Number: RFA FD 16 028 Agency: HHS-FDA Category: Consumer Protection, Food and Nutrition, Health Funding Amount: $500,000 |
| Integrating Supersaturation-precipitation Mechanisms in Mechanistic Oral Absorption Models for Predicting In- vivo Performance of Associated Formulations (U01) Apply for RFA FD 16 025 Funding Number: RFA FD 16 025 Agency: HHS-FDA Category: Consumer Protection, Food and Nutrition, Health Funding Amount: $250,000 |
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| Revision Applications - FSMA Human Foods Preventive Controls Implementation Expansion Supplement to RFA-FD-18-001 (U18 Clinical Trials Not Allowed) Apply for PAR 20 091 Funding Number: PAR 20 091 Agency: Department of Health and Human Services, Food and Drug Administration Category: Consumer Protection, Food and Nutrition, Health Funding Amount: $300,000 |
| CFD Models to aid the development of generic inhalation products Apply for FOR FD 20 023 Funding Number: FOR FD 20 023 Agency: Department of Health and Human Services, Food and Drug Administration Category: Consumer Protection, Food and Nutrition, Health Funding Amount: Case Dependent |
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