用数据驱动的药物重用策略:见解,建议和案例研究
Susanna Savander1, Nurettin Nusret Curabaz1, Amna Mumtaz Abbasi1
1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
药物重定向提供了一种更快,更便宜的方法,通过探索现有药物来寻找新药. 这项研究提供了一个数据驱动的框架,以指导药物重新用于未满足的医疗需求.
科学领域:
- 药理学和药物发现
- 计算生物学 计算生物学
- 药用化学 医学化学
背景情况:
- 传统的药物发现是漫长的,昂贵的,并且失败率很高.
- 药物再利用是一个可行的策略,可以加速治疗的发展和解决未满足的医疗需求.
- 对药物标相互作用的系统分析对于有效的药物重定向至关重要.
研究的目的:
- 为了比较分析药物向相互作用数据库 (ChEMBL,BindingDB,GtoPdb).
- 开发一种结构化的框架,用于对药物和目标数据的治疗性解释.
- 建立一个计算管道来预测药物重定向机会.
主要方法:
- 对ChEMBL,BindingDB和GtoPdb数据库进行比较分析.
- 目标的手动分类和药物指示的映射到更广泛的类别.
- 物理化学性质的分析和交叉指示药物批准的检查.
- 实现基于路径的计算管道,用于预测药物重新定位.
主要成果:
- 建立了药物和目标数据的结构化框架,将物理化学性质与治疗组联系起来.
- 确定了药物特性和治疗类别之间的关联,指导化合物优先级.
- 通过对交叉指示批准的分析,在特定领域揭示了高的重用潜力.
- 证明了计算管道用于预测瘤学中药物重定向的实用性.
结论:
- 将药物标数据和计算方法整合到药物发现的数据驱动框架中.
- 为特定适应症的化合物优先级和改进重定向研究提供了实际指导.
- 通过对药物重定向的系统方法,通过先进的药物发现和翻译应用.
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