Neighbor communities in drug combination networks characterize synergistic effect

Combination therapies are urgently needed for optimal clinical benefit, but an efficient strategy for rational discovery of drug combinations, especially combinations of experimental drugs, is still lacking. Consequently, we proposed here a network-based computational method to identify novel synergistic drug combinations. A large-scale drug combination network (DCN), which provides an alternative way to study the underlying mechanisms of drug combinations, was constructed by integrating 345 drug combination relationships, 1293 drug-target interactions and 15134 target-protein interactions. It was illustrated that synergistic drugs seldom have identical or directly connected targets, while most targets in DCN can be reached from every other by 2 to 4 edges (interactions). Accordingly, the concept 'neighbor community' was introduced to characterize the relationships between synergistic drugs by specifying the interactions between drug targets and their neighbor proteins in the context of DCN. A subsequent study revealed that the integrated topological and functional properties of neighbor communities can be employed to successfully predict drug combinations. It was shown that this method can achieve 88% prediction accuracy and 0.95 AUC (Area Under ROC Curve), demonstrating its good performance in specificity and sensitivity. Moreover, ten predicted synergistic drug combinations unknown to the method were confirmed by recent literature, and three predicted new combinations of experimental drug BI-2536 were validated by in vitro assays. The results suggested that this method provides a means to explore promising drug combinations at an earlier stage of the drug development process.