Exploiting similarity in drug molecular effects for drug repurposing.

Abstract

Background: Using large data to propose new uses of drugs has potential to rapidly prioritize new treatments for major diseases of public health importance. One comprehensive data set, LINCS L1000 Connectivity Map, profiles gene expression associated with thousands of compounds, including many with known clinical uses. But, some recent studies have questioned the reliability of this data, and the best approach to use this resource for drug repositioning is not well established.

Methods: Here, we develop a novel generalizable approach by hypothesizing that new treatments for a disease should induce similar gene expression to existing treatments for a disease. Using the Drug Repurposing Hub compendium of known treatments, we formulate a combined logistic regression model to predict new drug indications, and we assess generalizability of our findings using independent clinical trials on experimental drug uses.

Results: We support the hypothesis that drugs sharing an indication induce more similar gene expression, additionally demonstrating that the simpler Spearman correlation (p = 7.71e-38), outperforms the popular Connectivity Score (p = 5.2e-6). Our final model, combining predicted drug indications across three diverse cell lines, generalizes to predict experimental clinical trials with AUC of 0.708.

Conclusions: By developing a new approach to using LINCS L1000 data for drug repositioning, we both propose plausible new disease treatments and provide an interpretable rationale for predictions. Our findings not only put forward new drug repositioning candidates, browseable at https://bsultan.shinyapps.io/web-app , but they also provide guidelines for future researchers employing L1000 data for drug repurposing.