# Development of machine learning approaches to population pharmacokinetic model selection and evaluation of application to model-based bioequivalence analysis.

> **NIH FDA U01** · CERTARA USA, INC. · 2022 · $125,000

## Abstract

PROJECT SUMMARY
 The proposed project is for development an evaluation of a deep learning/reinforcement
learning approach to population pharmacokinetic model selections. We proposed to develop a
command line application using the Python programing language. Python is the current industry
standard for development or artificial intelligence applications, and the required packages for
deep learning/reinforcement learning are readily available (e.g., Pytorch and Tensorflow). The
applicants have previously developed a related machine learning approach using Genetic
Algorithm. For purposes of comparison and to make the resulting application generally available,
the existing Genetic Algorithm solution will be ported to Python. Both applications (Deep
learning/reinforcement learning and Genetic Algorithm) will use NONMEM ® for parameter
estimation for the population pharmacokinetic models examined. A common solution linking the
model selection algorithm (Deep Learning/Reinforcement Learning and Genetic Algorithm) to
NONMEM will be used for both, and is currently under development, with an early version
available on github.com. This common solution will facilitate future work using other algorithms
for model selection, e.g. particle swarm optimization or simulated annealing. This work will be
completed in the first year of the project. All final code will be place in the public domain in
github.com.
 The second year of the project will consist of evaluation of the solutions (Genetic algorithm
and Deep Learning/Reinforcement Learning). This evaluation will include assessment of a range
of measures of the “goodness” of the model (“fitness in Genetic Algorithm and “reward signal”
in Deep Learning/Reinforcement Learning). These measure of model “goodness” may include
objective function value, parsimony penalties, importance of successful covariance step. Within
the scope of this project, these measures will be objective and numerical. Future projects may
include the addition of subjective evaluations of model “goodness” in the model selection
algorithm.
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## Key facts

- **NIH application ID:** 10692787
- **Project number:** 7U01FD007355-02
- **Recipient organization:** CERTARA USA, INC.
- **Principal Investigator:** Mark E Sale
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** FDA
- **Fiscal year:** 2022
- **Award amount:** $125,000
- **Award type:** 7
- **Project period:** 2021-08-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10692787

## Citation

> US National Institutes of Health, RePORTER application 10692787, Development of machine learning approaches to population pharmacokinetic model selection and evaluation of application to model-based bioequivalence analysis. (7U01FD007355-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10692787. Licensed CC0.

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