Grants / Department of Health and Human Services / Food and Drug Administration

FOR-FD-20-030: Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning

Closed Closed Opportunity FOR-FD-20-030 ☆ Save

This grant is closed. A newer notice from the same program is open now: Flexible Funding Model - Advancing Domestic Mutual Reliance Through Strengthened Infrastructure Development and Maintenance for Manufactured Food Regulatory Programs (PAR-27-017). Status: Rolling or no fixed deadline. It is in our archive of Food and Drug Administration notices from 2021.

• Award amountsNot given on Grants.gov
• Cost sharingNot required
• Funding typeGrant

Key dates

Who can apply

This opportunity was open to nonprofits, colleges and universities, state and local governments, tribes, small businesses and businesses.

More grants for nonprofits, colleges and universities, state and local governments, tribes, small businesses, businesses.

Is this a realistic fit?

• Most of the program's money (35% over five years) went to nonprofits.
• No cost sharing or match is required.

What the grant funds

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.

Category: Consumer Protection, Health, Science and Technology and other Research and Development.

Applying now

This notice no longer takes applications. Apply under the newer notice, PAR-27-017, which is open now.

The notice on Grants.gov keeps its full text and attachments, which is useful for planning a resubmission.