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利用分子网络研究喹硫平的代谢。

New insights into quetiapine metabolism using molecular networking.

机构信息

INSERM, INRAE, CHU Rennes, Institut NuMeCan (Nutrition, Metabolism and Cancer), PREVITOX Network, Univ Rennes, 35033, Rennes, France.

Forensic Toxicology Laboratory, Rennes University Hospital, 35033, Rennes, France.

出版信息

Sci Rep. 2020 Nov 16;10(1):19921. doi: 10.1038/s41598-020-77106-x.

Abstract

Metabolism is involved in both pharmacology and toxicology of most xenobiotics including drugs. Yet, visualization tools facilitating metabolism exploration are still underused, despite the availibility of pertinent bioinformatics solutions. Since molecular networking appears as a suitable tool to explore structurally related molecules, we aimed to investigate its interest in in vitro metabolism exploration. Quetiapine, a widely prescribed antipsychotic drug, undergoes well-described extensive metabolism, and is therefore an ideal candidate for such a proof of concept. Quetiapine was incubated in metabolically competent human liver cell models (HepaRG) for different times (0 h, 3 h, 8 h, 24 h) with or without cytochrom P450 (CYP) inhibitor (ketoconazole as CYP3A4/5 inhibitor and quinidine as CYP2D6 inhibitor), in order to study its metabolism kinetic and pathways. HepaRG culture supernatants were analyzed on an ultra-high performance liquid chromatography coupled with tandem mass spectrometry (LC-HRMS/MS). Molecular networking approach on LC-HRMS/MS data allowed to quickly visualize the quetiapine metabolism kinetics and determine the major metabolic pathways (CYP3A4/5 and/or CYP2D6) involved in metabolite formation. In addition, two unknown putative metabolites have been detected. In vitro metabolite findings were confirmed in blood sample from a patient treated with quetiapine. This is the first report using LC-HRMS/MS untargeted screening and molecular networking to explore in vitro drug metabolism. Our data provide new evidences of the interest of molecular networking in drug metabolism exploration and allow our in vitro model consistency assessment.

摘要

代谢在包括药物在内的大多数外源化学物的药理学和毒理学中都有涉及。然而,尽管有相关的生物信息学解决方案可用,但促进代谢探索的可视化工具仍未得到充分利用。由于分子网络似乎是一种探索结构相关分子的合适工具,我们旨在研究其在体外代谢探索中的应用。喹硫平是一种广泛应用的抗精神病药物,经历了广泛描述的广泛代谢,因此是这种概念验证的理想候选药物。喹硫平在代谢功能齐全的人肝细胞模型(HepaRG)中不同时间(0 h、3 h、8 h、24 h)孵育,有无细胞色素 P450(CYP)抑制剂(酮康唑作为 CYP3A4/5 抑制剂,奎尼丁作为 CYP2D6 抑制剂),以研究其代谢动力学和途径。HepaRG 培养上清液用超高效液相色谱-串联质谱(LC-HRMS/MS)进行分析。LC-HRMS/MS 数据上的分子网络方法可快速可视化喹硫平的代谢动力学,并确定参与代谢物形成的主要代谢途径(CYP3A4/5 和/或 CYP2D6)。此外,还检测到两种未知的假定代谢物。从接受喹硫平治疗的患者的血液样本中证实了体外代谢物的发现。这是首次使用 LC-HRMS/MS 非靶向筛选和分子网络来探索体外药物代谢的报告。我们的数据提供了分子网络在药物代谢探索中的应用的新证据,并允许我们评估体外模型的一致性。

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