Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug–Pathway Associations via Network-Based Approaches

Identification of drug–pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug–pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug–pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug–pathway associations based on substructure–drug–pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug–pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug–pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.