完善药物诱导胆固醇症预测:一个可解释的共识模型,整合化学和生物指纹
Palle S Helmke1, Gerhard F Ecker1
1Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria.
Journal of chemical information and modeling
|May 27, 2025
概括
这项研究开发了一种计算模型,用于预测一种肝损伤类型的药物诱导胆固醇症 (DIC). 该模型整合了化学结构,肝脏点和传送器数据,改善了早期药物安全评估,减少了动物试验.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学的计算化学
- 系统生物学 系统生物学
背景情况:
- 药物诱导性肝损伤 (DILI),特别是药物诱导性胆固醇 (DIC),在早期药物开发中构成了重大挑战.
- 通过3R原则 (取代,减少,改进) 尽量减少动物试验对于道德和高效的药物安全性评估至关重要.
研究的目的:
- 开发和验证一种用于预测药物诱导胆固醇形成 (DIC) 的计算方法.
- 整合多种数据源,包括化学子结构,肝脏表达的标,途径和肝脏输送器抑制,以提高预测准确度.
主要方法:
- 使用了PubChem基结构指纹和来自肝脏表达的点和途径的生物数据.
- 整合了九个肝转运器抑制模型,并采用了下面的样本,以解决公共胆固醇定位数据中的类不平衡问题.
- 应用目标预测工具来丰富化合物-目标相互作用矩阵,并使用扩展的共识模型与概率范围过.
主要成果:
- 结合化学物质,途径和传送器数据的基线模型通过10倍交叉验证实现了0.29的马修斯相关系数 (MCC) 和0.79的灵敏度.
- 功能重要性分析确定了白蛋白作为与胆固醇定位相关的潜在标.
- 使用扩展共识模型和概率过的精细方法,改善了MCC为0.38的预测,灵敏度为0.80.
结论:
- 开发的计算方法通过整合化学和生物描述符,有效地预测药物诱导的胆固醇形成.
- 该模型为早期药物安全性评估提供了可靠和可解释的工具,支持决策,并可能减少对动物试验的依赖.
- 进一步研究像白蛋白这样的已识别的标是有必要的,以加深对胆固醇形成机制的理解.
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