An RDF dataset developed for drug repositioning...

Despite a recent resurgence in productivity, Research and Development (R&D) approaches to drug discovery are proving to be less fruitful despite the increased spending1. Complementary approaches, such as drug repositioning offer interesting alternatives. Drug repositioning, or drug repurposing involves the identification of novel uses for existing drugs2.

In order to make more informed drug repositioning inferences, we present DReNIn (Drug Repositioning Network Integration). DReNIn allows us to take an holistic view of a drug and it's interactions at a systems level. The DReNIn dataset includes data integrated from multiple sources and is presented as a Resource Description Framework (RDF) dataset. The dataset is stored in a Virtuoso triple store which can queried using SPARQL. DReNIn is made up of more than 8 million triples.

DReNIn makes use of the DReNIn Ontology, which enables a simplified view of a complicated landscape. Focussing on single protein targets from Homo sapiens, DReNIn allows for drug repositioning opportunities to be identified for both rare and common diseases. Unlike other pharmacological datasets, DReNIn includes drug side-effects, drug indications and clinical trials data; allowing inferred drug repositioning opportunities to be further investigated for validity.

Taking in silico approaches to analysing in vivo and in vitro systems obviously limits accuracy; overly simplified settings innately struggle to reflect real-life problems3,4. We hope that by providing a simplified view of a drugs surroundings, DReNIn can help to reduce the enormous search space that is apparent within the field of drug repositioning.

  1. J. Li, S. Zheng, B. Chen, A. J. Butte, S. J. Swamidass, and Z. Lu, "A survey of current trends in computational drug repositioning", Briefings in Bioinformatics, vol. 17, pp. 2-12, Jan. 2016
  2. T. T. Ashburn and K. B. Thor, "Drug repositioning: identifying and developing new uses for existing drugs", Nature reviews. Drug discovery, vol. 3, pp. 673-83, Aug. 2004.
  3. J. Mullen, S.J. Cockell, H. Tipney, P.M. Woollard and A. Wipat, "Mining integrated semantic networks for drug repositioning opportunities", PeerJ 4:e1558 Jan. 2016
  4. J. Mullen, S.J. Cockell, P.M. Woollard and A. Wipat, "An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations", PLoS ONE 11(5): e0155811. 2016