Jones Hannah M, Gardner Iain B, Watson Kenny J
Pfizer Global R&D, Department of Pharmacokinetics, Dynamics and Metabolism, IPC 654, Ramsgate Road, Sandwich, Kent, CT13 9NJ, UK.
AAPS J. 2009 Mar;11(1):155-66. doi: 10.1208/s12248-009-9088-1. Epub 2009 Mar 12.
Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and "first in human" stages for the prediction of human PK.
基于生理的药代动力学(PBPK)模型由一系列微分方程组成,并已在许多商业软件包中实现。这些模型需要物种特异性和化合物特异性的输入参数,并能够预测化合物静脉内和口服给予动物和人类后的血浆和组织浓度-时间曲线。PBPK模型允许将各种临床前数据早期整合到一个机械定量框架中。使用PBPK模型使实验者能够深入了解化合物的性质,有助于在药物发现的早期阶段指导实验工作,并能够在最少(在某些情况下无需)动物数据的情况下预测人体血浆浓度-时间曲线。在本综述中,将讨论PBPK技术在药物发现中的应用和局限性。具体提及了其在(1)先导化合物开发阶段用于确定动物PK研究化合物优先级以及(2)临床候选物选择和“首次人体试验”阶段用于预测人体PK方面的效用。