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

开发机器学习方法来选择群体药代动力学模型并评估基于模型的生物等效性分析的应用。

• 批准号: 10375078
• 负责人: Mark E Sale
• 金额: $ 7.29万
• 依托单位: NUVENTRA, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2022-08-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10375078
• 关键词: Development; machine; learning; approaches; population

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

项目摘要 拟议的项目是开发一个深度学习/强化的评估 群体药代动力学模型选择的学习方法。我们建议发展一个 使用Python编程语言的命令行应用程序。Python是当前的行业 开发或人工智能应用程序的标准，以及 深度学习/强化学习是容易获得的（例如，Pytorch和Tensorflow）。的 申请人之前已经开发了一种相关的机器学习方法， 算法为了比较的目的并使得到的应用程序普遍可用， 现有的遗传算法解决方案将被移植到Python。两种应用程序（深度 学习/强化学习和遗传算法）将使用NONMEM ®作为参数 所检查的群体药代动力学模型的估计。一种常见的解决方案， 模型选择算法（深度学习/强化学习和遗传算法）， NONMEM将用于这两种情况，目前正在开发早期版本 在github.com上可以找到。这种通用的解决方案将促进未来使用其他算法的工作 用于模型选择，例如粒子群优化或模拟退火。这项工作将 在项目的第一年完成。所有最终的代码将被放置在公共领域， github.com. 该项目的第二年将包括评估的解决方案（遗传算法 和深度学习/强化学习）。这一评价将包括评估一系列 模型的“优度”的度量（“遗传算法中的适应度”和“奖励信号”） 深度学习/强化学习（Deep Learning/Reinforcement Learning）这些模型“优良性”的度量可以包括 目标函数值，简约惩罚，成功协方差步骤的重要性。内 这些措施将是客观和数字化的。未来的项目可能 包括在模型选择中增加对模型“优度”的主观评价 算法 机密第1页，共1页