Functional evaluation of CYP2C19 and CYP3A4 gene polymorphism on ibuprofen metabolism

CYP3A4型 CYP2C19型 代谢物 布洛芬 代谢途径 药物代谢 新陈代谢 化学 药理学 药物遗传学 基因型 药代动力学 人口 微粒体 生物化学 生物 细胞色素P450 基因 医学 环境卫生
作者
Ling-jing Yuan,Xiangyu Li,Jinhuan Ni,Jing Wang,Xiaoyu Xu,Jian-Chao Luo,Qi Zhou,Guo‐Xin Hu,Jianping Cai,Jianchang Qian
出处
期刊:Toxicology and Applied Pharmacology [Elsevier]
卷期号:475: 116653-116653
标识
DOI:10.1016/j.taap.2023.116653
摘要

Ibuprofen is the most commonly used analgesic. CYP polymorphisms are mainly responsible for the differences in drug metabolism among individuals. Variations in the ability of populations to metabolize ibuprofen can lead to drug exposure events. The aim of this study was to evaluate the effects of CYP2C19 and CYP3A4 polymorphisms on ibuprofen metabolism in a Chinese population.First, 31 CYP2C19 and 12 CYP3A4 microsomal enzymes were identified using an insect expression system. Then, variants were evaluated using a mature incubation system. Moreover, ibuprofen metabolite content was determined via ultra-performance liquid chromatography-tandem mass spectrometry analysis. Finally, kinetic parameters of CYP2C19 and CYP3A4 genotypes were determined via Michaelis-Menten curve fitting.Most variants exhibited significantly altered intrinsic clearance compared to the wild type. In the CYP2C19 metabolic pathway, seven variants exhibited no significant alterations in intrinsic clearance (CLint), six variants exhibited significantly high CLint (121-291%), and the remaining 15 variants exhibited substantially reduced CLint (1-71%). In the CYP3A4 metabolic pathway, CYP3A4*30 was not detected in the metabolite content due to the absence of activity, and 10 variants exhibited significantly reduced CLint.To the best of our knowledge, this is the first study to assess the kinetic characteristics of 31 CYP2C19 and 12 CYP3A4 genotypes on ibuprofen metabolism. However, further studies are needed on poor metabolizers as they are more susceptible to drug exposure. Our findings suggest that the kinetic characteristics in combination with artificial intelligence to predict the toxicity of ibuprofen and reduce any adverse drug reactions.
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