Bayesian Tools for PBPK Models in Drug Interaction Res.

描述（由申请人提供）：在药物-药物相互作用（DDI）研究中，为了充分了解多种药物的药代动力学及其相互作用，基于生理的药代动力学模型（PBPK）是研究和量化所有相互作用因素（活性部位或器官）的唯一可行方法。PBPK模型因其复杂的结构而存在模型识别问题；因此，它通常借用先前的生理信息使模型可识别。然而，传统的基于确定性PBPK模型的DDI研究忽略了PK参数的群体变化、实验噪声和先验知识的不确定性，从而导致主观结论的可靠性未知。本资助申请提出了一个贝叶斯工具系统，通过探索三个目标来满足PBPK模型的药理学、统计学和计算方面的挑战。在Aim 1中，基于已发表的动物、体外和体内数据，建立了单个药物的抑制剂-底物组合的PBPK模型；系统地讨论了由信息有限的简单模型向知识丰富的复杂模型过渡的策略；和贝叶斯元分析方法。在Aim 2中，基于临床DDI研究，开发了一种用于联合抑制剂-底物PBPK模型的两阶段贝叶斯方法；提出了预测假阴性率来评价DDI预测；采用贝叶斯模型选择程序，从竞争模型中选择最佳PBPK模型。在Aim 3中，所有规定的贝叶斯工具都是在R中实现的，R是一个统计和计算免费软件；其基于网络的应用程序的开发是为了方便一般研究科学家的使用。在这个资助申请中，我们意识到DDI是酶依赖的事实。因此，选择一种CYP3A特异性抑制剂-底物组合，酮康唑-咪达唑仑作为起始例子。它将作为基于多酶和多药物的DDI PBPK模型的构建块。

DESCRIPTION (provided by applicant): In drug-drug interaction (DDI) research, to fully understand multiple drugs' pharmacokinetics and their interactions, a physiologically based pharmacokinetic model (PBPK) is the only viable means of investigating and quantifying all the interacting factors (active sites or organs). A PBPK model is known for its model identification problem because of its complex structure; hence, it usually borrows prior physiological information to make the model identifiable. However, conventional DDI researches based on deterministic PBPK models ignore population variations in PK parameters, experimental noise and uncertainties in prior knowledge, which in turn leads to subjective conclusions with unknown reliability. This grant application proposes a system of Bayesian tools to meet these pharmacological, statistical and computational challenges of PBPK models by exploring three aims. In Aim 1, individual drug's PBPK models of the inhibitor-substrate combination is established based on their published animal, in-vitro, and in-vivo data; the strategy of starting a simple model with limited information to a complex model with rich knowledge is systematically discussed; and Bayesian meta-analysis methods are proposed. In Aim 2, based on clinical DDI studies, a two-stage Bayesian method for a joint inhibitor-substrate PBPK model is developed; a predictive false negative rate is proposed to evaluate the DDI prediction; and Bayesian model selection procedures are implemented to select the best PBPK model among competitive ones. In Aim 3, all the prescribed Bayesian tools are implemented in R, a statistical and computational freeware; and its web-based application is developed to facilitate its usage for general research scientists. In this grant application, we realize the fact that the DDI is enzyme-dependent. Hence, a CYP3A specific inhibitor-substrate combination, ketoconazole-midazolam, is chosen as a starting example. It will serve as a building block for multi-enzyme and multi-drug based DDI PBPK models.