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

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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. CONFIDENTIAL Page 1 of 1