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/.