药物通过途径扰动动态进行重新定位:用于精确瘤学的系统生物学方法
Xianbin Li1, Wuxiang Ruan2, Guoan Lu3
1School of Computer and Big Data Science, Jiujiang University, Jiujiang 332000, China; Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
在癌症中,PathPertDrug 对药物重定向的途径动态进行了定量模型. 这种新的计算框架提高了预测准确度,并通过分析药物和疾病途径之间的功能对抗来识别新的癌症药物候选者.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 在瘤学瘤学.
背景情况:
- 药物重新定位是一种成本效益高的策略,用于发现新的药物应用.
- 目前用于药物重定向的计算方法往往忽略了定量途径扰乱动态,限制了机理理解.
- 精确建模路径动态对于有效的药物重定向至关重要.
研究的目的:
- 开发一种新的计算框架,PathPertDrug,用于识别癌症候选药物.
- 量化建模路径扰动动态,以改善药物重定位.
- 在计算药物发现中增强机械解释性.
主要方法:
- PathPertDrug 集成了药物诱导的基因表达,与疾病相关的基因表达和通路信息.
- 该框架量化了药物诱导和疾病相关途径扰乱 (激活/抑制) 之间的功能对立性.
- 评估途径级功能逆转以预测药物疾病相关性.
主要成果:
- 在整个胰腺癌基准标准中,PathPertDrug 显示出卓越的预测准确性和稳定性.
- 该模型实现了比现有方法更高的中位数AUROC (0.62) 和显著的AUPR改进 (3-23%).
- 该公司重新发现了83%已知的癌症药物,并确定了新的候选药物,如肺癌的利法布丁.
结论:
- "PathPertDrug"以途径为中心的方法将机械学见解与翻译应用联系起来.
- 这一框架为精确瘤学药物发现提供了一个范式的转变.
- 在癌症中,PathPertDrug提供了一个强大的工具,用于识别有效的药物重定向候选人.
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