Abstract
The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.
Highlights
Finding novel ways to treat and cure diseases is a fundamental challenge in biomedical research
Drug Target Prediction and Repositioning Workflow Here, we suggest an analysis workflow for drug target prediction and repositioning for a disease of interest based on network analysis of a disease-specific gene expression signature (Figure 1)
Computing the correlation between the distance matrices obtained from gene expression signatures and drug target predictions, we found a significant correlation between differential expression and target based clustering (p-value 0.008), indicating that similar disease gene signatures lead to similar drug target predictions
Summary
Finding novel ways to treat and cure diseases is a fundamental challenge in biomedical research. Many advances have been made over the last decades, drug discovery is still a very lengthy, increasingly risky and costly process [1]. There is a lack of reliable drug target prediction methods as reflected by the low clinical target validation success rate. New bioinformatics approaches are required, which are able to accurately predict drug targets for a disease [2]. These predicted drug targets can be of two types. 1. Novel drug targets: unexploited targets that can be used for developing first in class drugs and combination therapies
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