Network-based drug repositioning: A novel strategy for discovering potential antidepressants and their mode of action

Abstract

Current target-oriented paradigm for novel antidepressant discovery has been difficult to succeed and the failures always bring huge economic losses. Although abundant ledge of disease related genes and drug action targets has been accumulated, the successful application of the knowledge for new drug discovery is limited. Here, we predicted and validated potential antidepressants and molecular targets from DrugBank recorded drugs using a novel network-based drug repositioning approach. This approach predicted relationships between drug and targets through network-based integration of drug chemical similarity, therapeutic similarity and protein–protein interactions. We predicted genome-wide relations of drugs and targets, and then screened drugs that connect to depression-related targets of known antidepressants. Six drugs were predicted and experimentally validated to have antidepressant-like effects in the tail suspension test (TST) and forced swimming test (FST) in mice. Alverine, which is a gastrointestinal antispasmodic drug, was further validated to display antidepressant-like effects in the learned helplessness and chronic unpredictable stress models of depression. Four targets, including serotonin transporter, norepinephrine transporter, serotonin 1A receptor and serotonin 2A receptor, were included in the predictable system and confirmed as primary sites of action for alverine. The results suggest that alverine may be an effective antidepressant drug and the network-based drug repositioning may be a promising drug discovery paradigm for complex multi-genetic diseases such as depression.