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通过基因表达的总结变量来改善人类肝毒性潜力的识别

Wiebke Albrecht¹, Tim Brecklinghaus¹, Marieke Stolte²

¹Leibniz Research Centre for Working Environment and Human Factors at the Technical University of Dortmund (IfADo), Dortmund, Germany.

|February 16, 2025

概括

基因表达数据显著提高了人类药物诱导的肝损伤 (肝毒性) 的预测. 将基因表达与细胞毒性分析相结合,为识别有毒化合物提供了更高的准确性.

科学领域:

毒理学毒理学毒理学
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

从 in vitro/in silico 方法预测人类的肝毒性是一项挑战.
基因表达数据提供了比单独细胞毒性更好的肝毒性预测的潜力.

研究的目的:

评估基因表达数据是否与细胞毒性分析相比改善了肝毒性预测.
开发用于总结高维基因表达数据的方法,用于毒性预测.

主要方法:

通过细胞毒性和基因表达试验分析了60种肝毒性和非肝毒性物质.
应用毒性分离指数 (TSI) 来量化总结变量的预测能力.
研究的总结变量来自3524个受肝毒药影响的基因表达探针.

主要成果:

最佳方法涉及确定导致两个基因差异表达的最低化合物度.
基于基因表达的总结变量在毒性预测方面表现优于基于细胞毒性的变量.
将基因表达和细胞毒性数据结合起来,产生了最高的毒性预测准确性 (TSI).

结论:

导出基因表达总结变量的方法对于准确的肝毒性预测至关重要.
改进的基因表达分析可以根据血液度改善预测人类肝毒性风险.

关键词:

药物引起的肝损伤 (DILI)基于生理学的药理动力学 (PBPK) 建模.主要的人类肝细胞 (PHH)有针对性的测序.

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