Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning
Food and Drug Administration
About This Grant
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.
Grant Summary
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning is a Food and Drug Administration grant providing funding that varies by award for municipality. Applications are accepted on a rolling basis. Check eligibility and apply with FindGrants.
Focus Areas
Eligibility
How to Apply
Up to $0K
Rolling / Open
- 1Confirm your organization is eligible for Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning from Food and Drug Administration, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to Food and Drug Administration before the deadline.
Search & build free — $99 one-time to unlock the export-ready application pack. No subscription.
Don't want to draft it yourself?
We'll draft the complete application against Food and Drug Administration's requirements, run a quality review, and email you a submission-ready PDF plus an editable Word doc within 5 business days. Most orders deliver in 24-48 hours. Flat $399, any grant size.
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning: Frequently Asked Questions
Who is eligible for the Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning?
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning is offered by Food and Drug Administration and is generally open to municipality. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning provide?
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning provides an amount that varies by award per award from Food and Drug Administration. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning deadline?
Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning accepts applications on a rolling or ongoing basis, so there is no single fixed deadline. Confirm current timing with the funder, Food and Drug Administration, before you apply, and submit as early as possible because rolling programs can close once funds are committed.
How do you apply for the Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning?
To apply for Development of a model selection method for population pharmacokinetics analysis by deep-learning based reinforcement learning, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from Food and Drug Administration.