A physarum-inspired prize-collecting steiner tree approach to identify subnetworks for drug repositioning.

Yahui Sun,Saman Halgamuge,Pathima Nusrath Hameed,Karin Verspoor

doi:10.1186/s12918-016-0371-3

Abstract

BackgroundDrug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. It is challenging to reposition drugs as pharmacological data is large and complex. Subnetwork identification has already been used to simplify the visualization and interpretation of biological data, but it has not been applied to drug repositioning so far. In this paper, we fill this gap by proposing a new Physarum-inspired Prize-Collecting Steiner Tree algorithm to identify subnetworks for drug repositioning.ResultsDrug Similarity Networks (DSN) are generated using the chemical, therapeutic, protein, and phenotype features of drugs. In DSNs, vertex prizes and edge costs represent the similarities and dissimilarities between drugs respectively, and terminals represent drugs in the cardiovascular class, as defined in the Anatomical Therapeutic Chemical classification system. A new Physarum-inspired Prize-Collecting Steiner Tree algorithm is proposed in this paper to identify subnetworks. We apply both the proposed algorithm and the widely-used GW algorithm to identify subnetworks in our 18 generated DSNs. In these DSNs, our proposed algorithm identifies subnetworks with an average Rand Index of 81.1%, while the GW algorithm can only identify subnetworks with an average Rand Index of 64.1%. We select 9 subnetworks with high Rand Index to find drug repositioning opportunities. 10 frequently occurring drugs in these subnetworks are identified as candidates to be repositioned for cardiovascular diseases.ConclusionsWe find evidence to support previous discoveries that nitroglycerin, theophylline and acarbose may be able to be repositioned for cardiovascular diseases. Moreover, we identify seven previously unknown drug candidates that also may interact with the biological cardiovascular system. These discoveries show our proposed Prize-Collecting Steiner Tree approach as a promising strategy for drug repositioning.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0371-3) contains supplementary material, which is available to authorized users.

Highlights

Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs
The Drug Similarity Networks (DSN) in the first group (D_01_a to D_10_a) are generated using the first proposed sparse graph generation algorithm (Fig. 1), while the DSNs in the second group (D_01_b to D_10_b) are generated using the second proposed sparse graph generation algorithm (Fig. 2). These DSNs are publicly available at https:// github.com/YahuiSun/Drug-Similarity-Network
Nine most suitable subnetworks are selected for drug repositioning, and ten drug candidates are identified from these subnetworks

Summary

Introduction

Drug repositioning can reduce the time, costs and risks of drug development by identifying new therapeutic effects for known drugs. Drug repositioning aims to identify new therapeutic effects for known drugs. Drug development time, costs and risks can be reduced significantly [1,2,3]. The data-driven methods reposition drugs by analyzing pharmacological data using statistical and machine learning concepts such as statistical estimations, classification and clustering [1, 6, 10]. Network-based methods are emerging methods that use networks to represent pharmacological data [10] These methods typically reposition drugs by identifying drug candidates in multiple decomposed subnetworks [10,11,12]. Even though multiple therapeutic effects are expected to be found, it requires a long time to analyze these multiple decomposed subnetworks

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Discussion

Conclusion