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Incorporating receptor theory in mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling.

机译:在基于机制的药代动力学-药效学(PK-PD)建模中纳入受体理论。

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摘要

Pharmacokinetic-Pharmacodynamic (PK-PD) modeling helps to better understand drug efficacy and safety and has, therefore, become a powerful tool in the learning-confirming cycles of drug-development. In translational drug research, mechanism-based PK-PD modeling has been recognized as a tool for bringing forward early insights in drug efficacy and safety into the clinical development. These models differ from descriptive PK-PD models in that they quantitatively characterize specific processes in the causal chain between drug administration and effect. This includes target site distribution, binding and activation, pharmacodynamic interactions, transduction and homeostatic feedback mechanisms. Compared to descriptive models mechanism-based PK-PD models that utilize receptor theory concepts for characterization of target binding and target activation processes have improved properties for extrapolation and prediction. In this respect, receptor theory constitutes the basis for 1) prediction of in vivo drug concentration-effect relationships and 2) characterization of target association-dissociation kinetics as determinants of hysteresis in the time course of the drug effect. This approach intrinsically distinguishes drug- and system specific parameters explicitly, allowing accurate extrapolation from in vitro to in vivo and across species. This review provides an overview of recent developments in incorporating receptor theory in PK-PD modeling with a specific focus on the identifiability of these models.
机译:药代动力学-药效学(PK-PD)建模有助于更好地了解药物功效和安全性,因此已成为确定药物开发过程的学习确认周期的有力工具。在转化药物研究中,基于机理的PK-PD建模已被认为是将药物功效和安全性的早期见解带入临床开发的工具。这些模型与描述性PK-PD模型的不同之处在于,它们定量表征了药物给药和作用之间的因果链中的特定过程。这包括靶位点分布,结合和激活,药效相互作用,转导和稳态反馈机制。与描述性模型相比,利用受体理论概念表征靶标结合和靶标活化过程的基于机制的PK-PD模型具有更好的外推和预测特性。在这方面,受体理论构成了以下方面的基础:1)预测体内药物浓度-效应关系和2)表征靶缔合-解离动力学作为药物作用时程滞后的决定因素。这种方法本质上可以明确地区分药物和系统的特定参数,从而可以从体外到体内以及跨物种进行准确的推断。这篇综述概述了将受体理论纳入PK-PD建模的最新进展,特别关注这些模型的可识别性。

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