DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome

TLDR

Drug repositioning maximizes drug potential, while adverse drug reactions are a leading cause of death, and both arise from unexpected chemical‑protein interactions that can be predicted by mining the chemical‑protein interactome, a task now facilitated by the DRAR‑CPI web server. The study aims to predict drug indications and adverse reactions by mining the chemical‑protein interactome. The server contains a library of drug molecules and targetable human proteins, and it computes association scores between a submitted molecule and library drugs from their interaction profiles, enabling users to infer potential indications or adverse reactions. The predictions matched gene‑expression‑based associations with a 74% rate, and the tool uncovered links between antipsychotics and anti‑infectives, suggesting antipsychotics may treat infections. The server is freely available at http://cpi.bio-x.cn/drar/.

Abstract

Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical-protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical-protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user's molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug-drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/.