Systems approaches to drug repositioning
Joseph Mullen, Simon Cockell, Peter M. Woollard, Anil Wipat · 2016
Despite the recent resurgence in productivity, drug development remains an incredibly costly task. There is a general acceptance that there is a need for complementary approaches to the current paradigm of R&D. One such complementary approach is that of drug repositioning, which focusses on the identification of novel uses for existing drugs. Marketed examples of repositioned drugs include those identified through serendipitous or rational observations, highlighting the need for systematic methodologies. Systems approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but require an integrative approach to biological data. Here, we present DReNIn, an integrated RDF dataset for drug repositioning. DReNIn integrates data from over 20 sources describing drugs in relation to their effect on targets and diseases associated with Homo sapiens. A SPARQL endpoint for querying DReNIn is also provided. Furthermore, we introduce DReSMin, an exact exhaustive algorithm developed for the identification of connected sub-components within a target graph, semantic subgraphs. Instances of semantic subgraphs allow for the inference of novel interactions not immediately evident in a network. Finally, we introduce two applications that make use of DReSMin. The first of these approaches infers novel drug-target associations, whilst the second uses gene-disease associations, along with other relevant data, to infer novel drug-disease associations. It is hoped that the work presented here will provide useful data sources and tools for the wider community, enabling the identification of drug repositioning opportunities and providing novel disease treatments.