Network-based in silico drug efficacy screening.

Emre Guney,Jörg Menche,Marc Vidal,Albert-László Barábasi

doi:10.1038/ncomms10331

Abstract

The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.

Highlights

The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction
We start with all 1,489 diseases defined by Medical Subject Headings (MeSH) compiled in a recent study[12] (Methods section)
When we look at the 29 diseases for which there are at least five known drugs, we see that most drugs used for asthma, Alzheimer’s disease (AD), cardiac arrhythmias, cardiovascular diseases, diabetes, epilepsy, hypersensitivity, kidney diseases, liver cirrhosis, systemic lupus erythematosus and ulcerative colitis are proximal to the disease (Fig. 4d, top panel)

Summary

Introduction

The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. The analysis of targets of US Food and Drug Administration (FDA) approved drugs and disease-related genes in Online Mendelian Inheritance in Man (OMIM)[2] revealed that most drug targets are not closer to the disease genes in the protein interaction network than a randomly selected group of proteins[3]. This suggests that traditional drugs lack selectivity towards the genetic cause of the disease, targeting instead the symptoms of the disease. We propose a drug-disease proximity measure that helps us quantify the therapeutic effect of drugs, distinguishing non-causative and palliative from causative and effective treatments and offering an unsupervised approach to uncover novel uses for existing drugs

Methods

Results

Conclusion