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用于研究慢性药物性肝损伤的体外模型。

In Vitro Models for Studying Chronic Drug-Induced Liver Injury.

机构信息

Unidad de Hepatología Experimental, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain.

Departamento de Bioquímica y Biología Molecular, Facultad de Medicina y Odontología, Universidad de Valencia, 46010 Valencia, Spain.

出版信息

Int J Mol Sci. 2022 Sep 28;23(19):11428. doi: 10.3390/ijms231911428.

Abstract

Drug-induced liver injury (DILI) is a major clinical problem in terms of patient morbidity and mortality, cost to healthcare systems and failure of the development of new drugs. The need for consistent safety strategies capable of identifying a potential toxicity risk early in the drug discovery pipeline is key. Human DILI is poorly predicted in animals, probably due to the well-known interspecies differences in drug metabolism, pharmacokinetics, and toxicity targets. For this reason, distinct cellular models from primary human hepatocytes or hepatoma cell lines cultured as 2D monolayers to emerging 3D culture systems or the use of multi-cellular systems have been proposed for hepatotoxicity studies. In order to mimic long-term hepatotoxicity in vitro, cell models, which maintain hepatic phenotype for a suitably long period, should be used. On the other hand, repeated-dose administration is a more relevant scenario for therapeutics, providing information not only about toxicity, but also about cumulative effects and/or delayed responses. In this review, we evaluate the existing cell models for DILI prediction focusing on chronic hepatotoxicity, highlighting how better characterization and mechanistic studies could lead to advance DILI prediction.

摘要

药物性肝损伤(DILI)是一个主要的临床问题,涉及患者的发病率和死亡率、医疗保健系统的成本以及新药开发的失败。需要一致的安全策略,能够在药物发现管道的早期识别潜在的毒性风险,这是关键。人类 DILI 在动物中预测不佳,可能是由于药物代谢、药代动力学和毒性靶标在物种间存在明显差异。出于这个原因,已经提出了从原代人肝细胞或肝癌细胞系培养的 2D 单层到新兴的 3D 培养系统的不同细胞模型,或使用多细胞系统进行肝毒性研究。为了在体外模拟长期肝毒性,应使用能够维持适当长时间肝表型的细胞模型。另一方面,重复剂量给药是更相关的治疗方案,不仅提供关于毒性的信息,还提供关于累积效应和/或延迟反应的信息。在这篇综述中,我们评估了现有的用于 DILI 预测的细胞模型,重点关注慢性肝毒性,强调了更好的表征和机制研究如何有助于提高 DILI 的预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2092/9569683/a19c15750a7b/ijms-23-11428-g001.jpg

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