在毒理学中以人工智能驱动的发现形态分子特征
Guillaume Jaume1,2,3,4, Thomas Peeters1,5, Andrew H Song1,2,3,4
1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
bioRxiv : the preprint server for biology
|August 2, 2024
概括
这项研究介绍了GEESE,一种AI模型,将组织形态与基因表达联系起来,用于早期药物毒性检测. 它可以对药物诱导的肝损伤进行定量分析,并发现生物标志物.
科学领域:
- 计算毒理学计算毒理学
- 临床前药物安全评估 临床前药物安全评估
- 在病理学中的人工智能.
背景情况:
- 早期药物毒性识别在临床前开发中至关重要,但具有挑战性.
- 组织病理学和毒基因组学被使用,但将形态学与基因表达联系起来在规模和定量分析方面是有限的.
- 人工智能 (AI) 为这些关系的大规模定量建模提供了一种新的方法.
研究的目的:
- 介绍GEESE,一种用于赋予毒理学数据中的形态分子特征的AI模型.
- 在临床前毒性研究中量化建模形态变化与基因表达之间的关系.
- 确定药物诱导性肝损伤 (DILI) 的新型病变特异性安全生物标志物.
主要方法:
- 在127项临床前毒性研究中,训练了一种AI模型 (GEESE) 来预测来自8,231个血素和色素染色肝脏部分的1,536个基因标.
- 从模型中生成伪空间解析的基因表达图.
- 相关的基因表达图与六个关键的DILI,分析2500万病变表达对.
主要成果:
- 建立了基因表达 (上下调节) 和形态病变之间的定量关系.
- 对已识别的基因签名与毒基因组数据库,途径丰富分析和人类细胞系进行了验证.
- 证明了AI对大规模,高颗粒度毒性表征的能力.
结论:
- 在毒理学中,GEESE 允许对形态分子特征进行前所未有的定量分析.
- 这项研究为人工智能驱动的新型毒性生物标志物的发现铺平了道路.
- 这种方法提高了对药物诱导毒性机制的理解,并改善了临床前安全性评估.
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