基于临床前模型和人类之间的基因型-表型差异的药物毒性预测
Minhyuk Park1, Woomin Song2, Hyunsoo Ahn3
1Department of Life Sciences, Pohang University of Science and Technology, Pohang, Republic of Korea; ImmunoBiome Inc., Pohang, Republic of Korea.
EBioMedicine
|October 29, 2025
概括
这项研究引入了一种新的机器学习模型,该模型使用基因型-表型差异来预测药物毒性,提高药物开发中的准确性和患者安全性.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算生物学 计算生物学
- 药物开发 药物开发
背景情况:
- 药物开发面临挑战,原因是临床前毒性发现对人类结果的翻译能力不佳.
- 人类和模型生物之间的生物学差异导致高临床试验消耗和药物退出.
- 目前的毒性预测方法往往忽略了物种间的差异,主要关注化学性质.
研究的目的:
- 开发一种机器学习框架,通过结合基因型-表型差异 (GPD) 来预测人类药物毒性.
- 通过考虑临床前模型和人类之间的生物变异来提高毒性预测的准确性.
- 通过早期识别高风险药物,降低药物开发成本并提高患者安全.
主要方法:
- 开发了一个机器学习框架,将GPD集成到临床前模型 (细胞系,小鼠) 和人类之间.
- 在基因基本性,组织表达和网络连接方面评估药物点的GPD.
- 基于GPD的模型与使用独立数据集和时间验证的最先进的预测器进行了对比.
主要成果:
- 在434种危险药物和790种已批准药物的数据集中,GPD特征与由于严重不良事件导致的药物失败有显著的关联.
- 结合GPD和化学特征的随机森林模型显示了增强的预测准确性 (AUPRC = 0.63,AUROC = 0.75).
- 该模型在神经毒性和心血管毒性方面表现出卓越的表现,超越了基于化学结构的模型,并预测了现实世界的药物戒断.
结论:
- 纳入基因型-表型关系为药物毒性预测提供了一个生物学基础的策略.
- 开发的框架使高风险药物的早期识别成为可能,有望降低开发成本和提高患者安全.
- 这种方法可以通过提高临床结果的预测来提高治疗批准的成功率.
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