School of Life Sciences and Technology, Tongji University.
Advanced Institute of Translational Medicine, Tongji University.
Brief Bioinform. 2018 Nov 27;19(6):1172-1182. doi: 10.1093/bib/bbx047.
Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.
积累的经验性临床实践,辅以动物或细胞系模型,已经启动了预测具有协同作用的组合药物的工作,这些药物的效果超过了单个药物的总和。为了构建更好的计算模型,本综述从组合药物的最新更新数据资源开始,然后从药物分子和药理学模式、靶标网络特性和化合物功能注释等方面总结了已知协同组合的报道机制。在此基础上,我们重点关注最近发表的主要的计算策略,包括分子建模方法、数学模拟、组合靶标优化和基于模式的统计/学习模型。还讨论了与天然化合物的作用、药物与免疫疗法的联合以及不良反应管理相关的未来思路。总的来说,本文特别强调药物协同作用的作用机制,可为设计组合药物的改进模型提供快速参考。