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从成人到新生儿的对乙酰氨基酚药代动力学模型的外推:拉丁超立方抽样分析。

Extrapolation for a pharmacokinetic model for acetaminophen from adults to neonates: A Latin Hypercube Sampling analysis.

机构信息

Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand.

Chongqing Institute for Food and Drug Control, Chongqing City, China.

出版信息

Drug Metab Pharmacokinet. 2020 Jun;35(3):329-333. doi: 10.1016/j.dmpk.2020.03.004. Epub 2020 Apr 9.

Abstract

Physiological and drug-specific parameters need to be adjusted when extrapolating a pharmacokinetic (PK) model from adults to neonates, so as to reproduce the time profiles of the studied drug(s) consistent with clinical, in vivo data or in vitro cell line measurements. In this paper we present a parameter analysis method, i.e. the Latin Hypercube Sampling (LHS) method for an acetaminophen (APAP) PK model. The original model consists of two compartments (the blood and the urine) with Michaelis-Menten kinetic parameters determined for APAP and its metabolites. The physiological parameters are scaled through allometric laws from adults to neonates, and APAP-specific parameters are adjusted for enzymatic maturational changes. The LHS method is used to statistically investigate the interplay between these parameters. The results for the extrapolated APAP model are consistent with published APAP PK data in neonates. We found the sulphation clearance parameter played a crucial role in the neonatal PK model, but its influence was weakened if the volume of distribution parameters were included. We suggest that this kind of in silico experiment could be valuable as the first step in PK model extrapolation between different ages.

摘要

当将药代动力学 (PK) 模型从成人外推到新生儿时,需要调整生理和药物特异性参数,以使所研究药物的时间曲线与临床、体内数据或体外细胞系测量结果一致。在本文中,我们提出了一种参数分析方法,即拉丁超立方抽样 (LHS) 方法,用于分析对乙酰氨基酚 (APAP) PK 模型。原始模型由两个隔室(血液和尿液)组成,具有确定的对乙酰氨基酚及其代谢物的米氏动力学参数。生理参数通过体表面积定律从成人缩放至新生儿,并且针对酶成熟变化调整了对乙酰氨基酚的特异性参数。LHS 方法用于统计研究这些参数之间的相互作用。外推的 APAP 模型的结果与已发表的新生儿 APAP PK 数据一致。我们发现,磺化清除率参数在新生儿 PK 模型中起着关键作用,但是如果包括分布体积参数,则其影响会减弱。我们建议,这种计算机实验可能是在不同年龄段之间进行 PK 模型外推的第一步中很有价值。

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