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一种基于肝细胞和微粒体体外内在清除率数据预测人体肝脏代谢清除率的统一模型。

A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes.

作者信息

Riley Robert J, McGinnity D F, Austin R P

机构信息

Department of Physical and Metabolic Science, AstraZeneca R&D Charnwood, Loughborough, Leicestershire, LE11 5RH, UK.

出版信息

Drug Metab Dispos. 2005 Sep;33(9):1304-11. doi: 10.1124/dmd.105.004259. Epub 2005 Jun 2.

Abstract

The aim of this study was to evaluate a unified method for predicting human in vivo intrinsic clearance (CL(int, in vivo)) and hepatic clearance (CL(h)) from in vitro data in hepatocytes and microsomes by applying the unbound fraction in blood (fu(b)) and in vitro incubations (fu(inc)). Human CL(int, in vivo) was projected using in vitro data together with biological scaling factors and compared with the unbound intrinsic clearance (CL(int, ub, in vivo)) estimated from clinical data using liver models with and without the various fu terms. For incubations conducted with fetal calf serum (n=14), the observed CL(int, in vivo) was modeled well assuming fu(inc) and fu(b) were equivalent. CL(int, ub, in vivo) was predicted best using both fu(b) and fu(inc) for other hepatocyte data (n=56; r(2)=0.78, p=3.3 x 10(-19), average fold error=5.2). A similar model for CL(int, ub, in vivo) was established for microsomal data (n=37; r(2)=0.77, p=1.2 x 10(-12), average fold error=6.1). Using the model for CL(int, ub, in vivo) (including a further empirical scaling factor), the CL(h) in humans was also calculated according to the well stirred liver model for the most extensive dataset. CL(int, in vivo) and CL(h) were both predicted well using in vitro human data from several laboratories for acidic, basic, and neutral drugs. The direct use of this model using only in vitro human data to predict the metabolic component of CL(h) is attractive, as it does not require extra information from preclinical studies in animals.

摘要

本研究的目的是通过应用血液中未结合分数(fu(b))和体外孵育(fu(inc)),评估一种从肝细胞和微粒体的体外数据预测人体体内固有清除率(CL(int, in vivo))和肝脏清除率(CL(h))的统一方法。使用体外数据以及生物学比例因子来预测人体CL(int, in vivo),并将其与使用包含和不包含各种fu项的肝脏模型从临床数据估算的未结合固有清除率(CL(int, ub, in vivo))进行比较。对于用胎牛血清进行的孵育(n = 14),假设fu(inc)和fu(b)相等时,观察到的CL(int, in vivo)能得到很好的建模。对于其他肝细胞数据(n = 56;r(2)=0.78,p = 3.3×10(-19),平均倍数误差 = 5.2),同时使用fu(b)和fu(inc)时,CL(int, ub, in vivo)预测效果最佳。针对微粒体数据建立了类似的CL(int, ub, in vivo)模型(n = 37;r(2)=0.77,p = 1.2×10(-12),平均倍数误差 = 6.1)。使用CL(int, ub, in vivo)模型(包括一个额外的经验比例因子),还根据最广泛数据集的充分搅拌肝脏模型计算了人体的CL(h)。使用来自多个实验室的体外人体数据,对酸性、碱性和中性药物的CL(int, in vivo)和CL(h)均预测良好。仅使用体外人体数据直接应用此模型来预测CL(h)的代谢成分很有吸引力,因为它不需要来自动物临床前研究的额外信息。

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