Using SAFEPATH to Uncover a Novel Mechanism of Hepatotoxicity for Gefitinib

Layla Hosseini-Gerami, Sara Masarone, Jordan Lane
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Abstract

This white paper details the research conducted by Ignota Labs using their advanced causal and explainable AI technology, SAFEPATH, to analyse the mechanisms of hepatotoxicity for two EGFR-TKI inhibitors, Erlotinib and Gefitinib, the latter having an as yet unknown mechanism of toxicity. The known mechanism of UGT1A1-mediated toxicity of Erlotinib was recovered, and a novel sphingolipid metabolism mechansim of toxicity of Gefitinib was hypothesised and subsequently experimentally validated. Crucially, we were also able to suggest the reason for the observed heterogeneous toxicity response to Gefitinib. This study exemplifies the potential of integrating AI tools with comprehensive datasets to improve drug safety and patient management.
利用 SAFEPATH 发现吉非替尼肝脏毒性的新机制
本白皮书详细介绍了 Ignota 实验室利用其先进的因果和可解释人工智能技术 SAFEPATH 进行的研究,该技术用于分析两种表皮生长因子受体-TKI 抑制剂厄洛替尼和吉非替尼的肝毒性机制,后者的毒性机制尚不清楚。我们复原了厄洛替尼由 UGT1A1 介导的已知毒性机制,并假设了吉非替尼的新型鞘脂代谢毒性机制,随后进行了实验验证。最重要的是,我们还提出了吉非替尼毒性反应异质性的原因。这项研究充分体现了将人工智能工具与综合数据集相结合,改善药物安全性和患者管理的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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