There is an unmet need for identifying innovative machine learning (ML) strategies to improve drug treatment regimens and therapeutic outcomes. We investigate Generalized Pharmacometric Modeling (GPM), a novel paradigm that integrates ML algorithms with pharmacokinetic and pharmacodynamic structural models, population covariate modeling, and "big data," and enables identification of patient-specific factors contributing to drug disposition. We hypothesize that GPM will enhance forecasting of drug outcomes in diverse populations. We assessed random forest regression in conjunction with Bayesian networks as the ML methods within GPM and used the National Health and Nutrition Examination Survey population-based study database. GPM was utilized to identify subject-specific factors associated with cholesterol dynamics. Our results demonstrate the utility of GPM to enhance pharmacometrics modeling and its potential for modeling drug outcomes in diverse populations.

中文翻译：

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广义药理学建模，一种集成机器学习算法的新型范例：代谢组学生物标记物的案例研究。

亟需确定创新的机器学习（ML）策略以改善药物治疗方案和治疗效果。我们研究了通用药理模型（GPM），这是一种将ML算法与药代动力学和药效学结构模型，群体协变量模型和“大数据”集成在一起的新颖范例，并能够识别导致患者药物配置的特定患者因素。我们假设GPM将增强对不同人群的药物治疗效果的预测。我们结合GPM中的ML方法，结合贝叶斯网络评估了随机森林回归，并使用了美国国家健康和营养检查调查基于人口的研究数据库。GPM被用于识别与胆固醇动力学相关的特定于受试者的因素。