Synergy evaluation by a pathway–pathway interaction network: a new way to predict drug combination. Issue 2 (21st December 2015)

• Record Type: Journal Article
• Authors: Chen, Di; Zhang, Huamin; Lu, Peng; Liu, Xianli; Cao, Hongxin
• Is Part Of: Molecular bioSystems. Volume 12:Issue 2(2016:Feb.)
• Page Start: 614
• Page End: 623
• Publication Date: 2015-12-21
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; 571.7405
• DOI: 10.1039/c5mb00599j
• Languages: English
• ISSNs: 1742-206X

Abstract

Abstract : Drug combinations have been widely applied to treat complex diseases, like cancer, HIV and cardiovascular diseases. Abstract : Drug combinations have been widely applied to treat complex diseases, like cancer, HIV and cardiovascular diseases. One of the most important characteristics for drug combinations is the synergistic effects among different drugs, that is to say, the combination effects are larger than the sum of individual effects. Although quantitative methods can be utilized to evaluate the synergistic effects based on experimental dose–response data, it is both time and resource consuming to screen all possible combinations by experimental trials. This problem makes it a formidable challenge to recognize synergistic combinations. Various attempts have been made to predict drug synergy by network biology, however, most of them are limited to estimating target associations on the PPI network. Here, we proposed a novel "pathway–pathway interaction" network-based synergy evaluation method to predict the potential synergistic drug combinations. Comparison with previous target-based methods shows that inclusion of systematic pathway–pathway interactions makes this novel method outperform others in predicting drug synergy. Moreover, it can also help to interpret how different drugs in a combination cooperate with each other to implement synergistic therapeutic effects. In general, drugs acting on the same pathway through different targets or drugs regulating a relatively small number of highly-connected pathways are more likely to produce synergistic effects.