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

Abstract

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 .

Acetylenic Synthetic Betulin Derivatives Inhibit Akt and Erk Kinases Activity, Trigger Apoptosis and Suppress Proliferation of Neuroblastoma and Rhabdomyosarcoma Cell Lines

“…The in silico prediction of potential biological targets within the cell or tissue for the analyzed ASBDs may also provide an insight into the molecular mechanisms underlying their anti-cancer activity. In this study, the proteins and signaling pathways which could be therapeutic targets for Bet derivatives were determined with the Balanced Substructure-Drug-Target Network-Based Inference (bSDTNBI) method using the NetInfer web server [79][80][81]. Interestingly, among 10 targets of Bet as well as ASBDs with the highest scores, we found several members of the G protein-coupled receptor 1 (GPCRs) family and nuclear hormone receptor family.…”

“…The prediction of potential target proteins and target pathways for Bet and ASBDs was computed with The Balanced Substructure-Drug-Target Network-Based Inference (bSDTNBI) method using NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/, accessed on 22 August 2021). SDTNBI is the first network-based computational approach that can predict potential targets for new chemical agents on a large scale [79][80][81]. In all in silico studies, Bet was used as a reference compound.…”

“…1 Pathway ID-according to the KEGG PATHWAYS database[83,84]. Method: Balanced Substructure-Drug-Target Network-Based Inference (bSDTNBI) implemented from[79][80][81]. Network: drug-pathway association network for pan-cancer (version 2020).…”

“…Gene symbol and4 Gene ID-according to the NCBI Entrez Gene database. Method: Balanced Substructure-Drug-Target Network-Based Inference (bSDTNBI); implemented from[79][80][81]. Network: global drug-target interaction (DTI) network (version 2020).…”

Drug Repurposing for Newly Emerged Diseases via Network‐based Inference on a Gene‐disease‐drug Network

“…[24,25] In recent years, we have developed a series of networkbased methods, including network-based inference (NBI), [26] substructure-drug-target NBI (SDTNBI), [27] and balanced SDTNBI (bSDTNBI), [28] which are widely used in predictions of drug-target interactions, [26,27] drug-microRNA associations, [29] and drug-pathway associations. [30] These methods have two advantages compared with machine learning models, one is that the negative data are not required, the other is that chemical substructures are used as the bridge to link new compounds with known targets.…”