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.
- Varies
- award range
- Closed
- application status
- 93.103
- CFDA program
Opportunity snapshot. This Grants.gov announcement - Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning - is cataloged under number FOR-FD-20-030 and tied to CFDA assistance listing 93.103, posted by Food and Drug Administration. Grants.gov currently shows the opportunity as closed, first posted on May 25, 2021. The funding category is Discretionary, delivered as a grant.
Award economics. The award range on file is Varies by applicant. Cost sharing is not required, so applicants do not need to commit matching funds to be competitive on this opportunity. Federal award ranges are often upper bounds; actual allocations reflect program appropriations, the strength of the applicant pool, and the evaluation committee's scoring.
Deadline and action path. This opportunity is closed. Monitoring the parent program page is the most reliable way to catch re-announcements in the next funding cycle. Every Grants.gov submission requires an active SAM.gov registration and a Unique Entity ID. Review the Eligibility section below carefully, federal eligibility categories (nonprofit, state or local government, tribal, individual, educational institution, small business) have distinct registration and reporting requirements. Pre-application outreach to the listed agency contact is permitted and often welcomed, it helps clarify scope and scoring priorities. Before acting on the deadline or award figures above, verify them directly on the official Grants.gov listing, amendments can change dates and amounts after this page was last refreshed.
Award Range
Varies by applicant
Close Date
Not specified
Archiving forecast
Posted
May 25, 2021
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.
Eligibility
Grants.gov lists this opportunity under eligibility category codes 01, 02, 04, 05, 06, 07, 08, 11, 12, 13, 20, 22, 23. These codes correspond to applicant types (state/local government, tribal organization, nonprofit, educational institution, individual, small business, etc.) defined in Grants.gov's own eligibility reference. See the current Grants.gov eligibility categories or check the official listing below for this opportunity's exact eligibility statement.
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
Key Dates
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| Sources | the SAM.gov Assistance Listings and Grants.gov |