Closed · FOR-FD-20-030 · CFDA 93.103 · Discretionary
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning
Federal grant opportunity posted by Food and Drug Administration, cataloged on Grants.gov.
- Varies
- Award range
- Closed
- Status
- -
- Close date
- -
- Expected awards
The verdict
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning is a closed discretionary listing from Food and Drug Administration that offered Varies by applicant. Future funding cycles may be published under the same CFDA number. PlainGrants indexes 481 other opportunities from Food and Drug Administration.
- Varies
- award range
- Closed
- application status
- 93.103
- CFDA program
- 481
- other opportunities from Food and Drug Administration
Archived opportunity record
Development of a model selection method for p… is closed. The parent program page for CFDA 93.103 is the reliable place to catch re-announcements. Food and Drug Administration still indexes 481 other opportunities here.
According to Grants.gov (extract vintage 2026-08-10), this record reflects the officially published listing. See methodology.
Deadline tracker
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Instrument
Grant
Description
For generic drug development, population pharmacokinetics (popPK) analysis is a critical part of the emerging technology of model-based bioequivalence (BE) analysis. PopPK models provide support for generalizing the conclusion of BE to groups that were not included in a BE study. The popPK model selection is essentially a multiple-objectives/variables optimization problem. Recent years have witnessed the overwhelming success of the reinforcement learning (RL) approaches in addressing optimization problem. Thus, the objective of this project is to develop a model selection method for the popPK analysis using the deep-learning based RL algorithm. Specific Aim 1: Develop a model selection method using a deep-learning based RL algorithm. A thorough survey should be conducted to gain a good understanding of the current state of the art for deep-learning based RL algorithms and their applications. The most appropriate algorithm/pipeline should be adopted to develop the model selection method. Specific Aim 2: Design simulations reflecting different scenarios of PK data, such as independent/correlated covariates, simple/complex (e.g., multiple peaks) time-concentration profiles and sparse-sampling design. The simulated datasets should be used to conduct systematic performance checks. Specific Aim 3: Identify proper metrics for performance evaluation. The selected metrics should be unbiased and mathematically/statistically meaningful. Specific Aim 4: Conduct performance evaluation. The developed model selection method and at least a stepwise regression and a genetic algorithm-based approach should be applied to the simulated datasets to perform popPK model building. The selected performance evaluation metrics should be used to compare the performance of the different methods. Specific Aim 5: Use real PK dataset(s) to demonstrate the applicability and advantage of using the developed method in popPK model building.
Wide-open applicant board · 13 Grants.gov doors
Development of a model selection method for p… prints 13 distinct Grants.gov eligibility doors (County governments; City or township governments; Special district governments; Independent school districts; +9 more). Filter fit before reading award size as demand — most applicant classes appear on the row. The extract does not mark applicant cost-sharing as required. Instrument: Grant. Funding-activity codes span Consumer Protection · Health · Science and Technology / R&D (CP;HL;ST). Inside Food and Drug Administration's indexed book this exact eligibility-code string is unique to this listing.
- Code 01
- County governments
- Code 02
- City or township governments
- Code 04
- Special district governments
- Code 05
- Independent school districts
- Code 06
- Public / state-controlled institutions of higher education
- Code 07
- Federally recognized tribal governments
- Code 08
- Public / Indian housing authorities
- Code 11
- Native American tribal organizations (other than Federally recognized tribal governments)
- Code 12
- Nonprofits with 501(c)(3) status (other than IHEs)
- Code 13
- Nonprofits without 501(c)(3) status (other than IHEs)
- Code 20
- Private institutions of higher education
- Code 22
- For-profit organizations other than small businesses
- Code 23
- Small businesses
Codes are Grants.gov eligibility category values from the extract vintage 2026-08-10. Confirm the full eligibility statement on the official listing or the Grants.gov eligibility reference.
Official Listing on Grants.gov
View full details, application forms, and submission instructions.
Parent Grant Program
Food and Drug Administration Research
U.S. Department of Health and Human Services
Agency Contact
Shashi Malhotra Grants Management Specialist
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What to do next
Development of a model selection method for p… is closed; use the parent program and agency catalog for the next funding cycle.
- Parent CFDA 93.103 may publish future NOFOs under the same assistance listing. Open program
- Food and Drug Administration Research indexes 42 Grants.gov listings in this extract. Program profile
- Food and Drug Administration lists 481 other indexed opportunities on PlainGrants. Agency catalog
Opportunity deadlines and eligibility change on Grants.gov; PlainGrants mirrors the extract and is not the submission system.
Disclaimer: This information is sourced from Grants.gov and SAM.gov and is for informational purposes only. Opportunity details, deadlines, and eligibility requirements change frequently. Always verify current information directly on Grants.gov before applying. PlainGrants is not affiliated with any federal agency.
Read our methodology - how this data is sourced, computed, and verified.
| Publisher | PlainGrants |
| Sources | the SAM.gov Assistance Listings and Grants.gov |