Toward a new paradigm for the efficient in vitro–in vivo extrapolation of metabolic clearance in humans from hepatocyte data

体内 肝细胞 体外 外推法 代谢清除率 化学 细胞生物学 生物 计算生物学 药理学 生物化学 药代动力学 遗传学 数学 数学分析
作者
Patrick Poulin,Sami Haddad
出处
期刊:Journal of Pharmaceutical Sciences [Elsevier BV]
卷期号:102 (9): 3239-3251 被引量:47
标识
DOI:10.1002/jps.23502
摘要

The objective of this study was to follow up a previous study on a comparative analysis of diverse in vitro-in vivo extrapolation (IVIVE) methods used for predicting hepatic metabolic clearance (CL) of drugs from intrinsic clearance (CLint ) data determined in microsomal incubations, but using hepatocyte data instead. Six IVIVE methods were compared: the "conventional and conventional bias-corrected methods," the "regression equation method," the "direct scaling method," the "Berezhkovskiy's method," and the "novel IVIVE method of Poulin et al." offering a new paradigm. A large and diverse dataset of 49 drugs were collected from the literature for hepatocyte data in human. Based on all statistical parameters, this study confirms that the novel IVIVE method of Poulin et al. shows the greatest prediction performance among the IVIVE methods tested by using hepatocyte data. The superior prediction performance of this novel IVIVE method is again most pronounced for (a) drugs highly bound in blood, (b) drugs bound to albumin, and (c) low CL drugs. Because the novel IVIVE method has been developed particularly to improve the prediction accuracy for drugs with such properties, this study confirms its utility. Furthermore, the results of the current comparative analysis performed using hepatocyte data confirm the findings of a previous analysis made with microsomal data. Overall, the proposed novel IVIVE method offers a new paradigm for the prediction of hepatic metabolic CL particularly for drugs, which have the aforementioned properties, and, hence, this would contribute to a more accurate CL prediction for small molecules in drug discovery and development, interspecies scaling, and can potentially be used for the optimization of driving factors of CL in an attempt to facilitate the simulation of drug disposition by using the physiologically based pharmacokinetics (PBPK) model.

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