An integrated data driven approach to drug repositioning using gene-disease associations

PLoS ONE

Volumn 11, Issue 5, 2016, Pages

  Mullen, Joseph ^a   Cockell, Simon J ^a   Woollard, Peter ^b   Wipat, Anil ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b GLAXOSMITHKLINE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LISINOPRIL; PAZOPANIB;

ALZHEIMER DISEASE; ARTICLE; BAYESIAN LEARNING; BIOINFORMATICS; CENTRAL NERVOUS SYSTEM DISEASE; DATA MINING; DISEASE ASSOCIATION; DRUG DEVELOPMENT; DRUG REPOSITIONING; DRUG RESEARCH; GENETIC ASSOCIATION; GENETIC DATABASE; HUMAN; INFORMATION SYSTEM; MASTOCYTOSIS; MATHEMATICAL ANALYSIS; METHODOLOGY; MOLECULAR DYNAMICS; ONTOLOGY; SEMANTICS; SINGLE NUCLEOTIDE POLYMORPHISM; ADVERSE DRUG REACTION; ALGORITHM; AREA UNDER THE CURVE; BAYES THEOREM; BIOLOGY; CENTRAL NERVOUS SYSTEM; COMPUTER GRAPHICS; DRUG EFFECTS; MEDICAL SUBJECT HEADINGS; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SOFTWARE;

ALGORITHMS; AREA UNDER CURVE; BAYES THEOREM; CENTRAL NERVOUS SYSTEM; COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; DATA MINING; DRUG DISCOVERY; DRUG REPOSITIONING; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; MEDICAL SUBJECT HEADINGS; ROC CURVE; SEMANTICS; SOFTWARE;

EID: 84991518561     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0155811     Document Type: Article

References (68)

1. Xavier RJ, Rioux JD. Genome-wide association studies: a new window into immune-mediated diseases. Nature reviews Immunology. 2008Aug; 8(8):631-643. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18654571.
2. Gusella JF, Wexler NS, Conneally PM, Naylor SL, Anderson MA, Tanzi RE, et al. A polymorphic DNA marker genetically linked to Huntington's disease. Nature. 1983 Nov; 306(5940):234-238. Available from: http://www.ncbi.nlm.nih.gov/pubmed/6316146.
3. Kann MG. Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Briefings in bioinformatics. 2010Jan; 11(1):96-110. Available from: http://dx.doi.org/10.1093/bib/bbp048. doi: 10.1093/bib/bbp048 PMID: 20007728
4. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genetics in medicine: official journal of the American College of Medical Genetics. 2002Mar/Apr; 4(2):45-61. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11882781. doi: 10.1097/00125817-200203000-00002
5. Bromberg Y. Chapter 15: disease gene prioritization. PLoS computational biology. 2013Apr; 9(4): e1002902. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23633938. doi: 10.1371/journal.pcbi.1002902 PMID: 23633938
6. Doncheva NT, Kacprowski T, Albrecht M. Recent approaches to the prioritization of candidate disease genes. Wiley interdisciplinary reviews Systems biology and medicine. 2012Sep/Oct; 4(5):429-442. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22689539. doi: 10.1002/wsbm.1177 PMID: 22689539
7. Liu RL, Shih CC. Identification of highly related references about gene-disease association. BMC bioinformatics. 2014Aug; 15:286. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25155502.
8. Bravo A, Piñero J, Queralt-Rosinach NA, Rautschka M, Furlong LI. Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinformatics. 2015Feb; 16(1):55+. Available from: http://dx.doi.org/10.1186/s12859-015-0472-9.
9. Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE, Group TMGD. The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse. Nucleic Acids Research. 2013Nov;p. gkt1225+. Available from: http://dx.doi.org/10.1093/nar/gkt1225.
10. Shimoyama M, De Pons J, Hayman GT, Laulederkind SJF, Liu W, Nigam R, et al. The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease. Nucleic acids research. 2015 Jan; 43(Database issue):D743-D750. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25355511. doi: 10.1093/nar/gku1026
11. Rosenthal N, Brown S. The mouse ascending: perspectives for human-disease models. Nature cell biology. 2007Sep; 9(9):993-999. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17762889.
12. Smedley D, Oellrich A, Köhler S, Ruef B, Sanger Mouse Genetics Project, Westerfield M, et al. Pheno- Digm: analyzing curated annotations to associate animal models with human diseases. Database: the journal of biological databases and curation. 2013May; 2013:bat025. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23660285. doi: 10.1093/database/bat025
13. Cokol M, Iossifov I, Weinreb C, Rzhetsky A. Emergent behavior of growing knowledge about molecular interactions. Nature biotechnology. 2005Oct; 23(10):1243-1247. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16211067.
14. Bauer-Mehren A, Rautschka M, Sanz F, Furlong LI. DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics. 2010Sep; 26(22):2924-2926. Available from: http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btq538.
15. Bauer-Mehren A, Bundschus M, Rautschka M, Mayer MA, Sanz F, Furlong LI. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PloS one. 2011Jun; 6(6):e20284. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21695124. doi: 10.1371/journal.pone.0020284 PMID: 21695124
16. Rappaport N, Twik M, Nativ N, Stelzer G, Bahir I, Stein TI, et al. MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases. Current protocols in bioinformatics / editoral board, Andreas D Baxevanis [et al]. 2014Sep; 47:1.24.1-1.24.19. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25199789. doi: 10.1002/0471250953.bi0124s47
17. Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: Text mining and data integration of disease-gene associations. Methods (San Diego, Calif). 2014Dec; Available from: http://www.ncbi.nlm.nih.gov/pubmed/25484339.
18. Swinney DC. Phenotypic vs. target-based drug discovery for first-in-class medicines. Clinical pharmacology and therapeutics. 2013Apr; 93(4):299-301. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23511784. doi: 10.1038/clpt.2012.236 PMID: 23511784
19. Sams-Dodd F. Target-based drug discovery: is something wrong? Drug discovery today. 2005Jan; 10(2):139-147. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15718163. doi: 10.1016/S1359-6446(04)03316-1 PMID: 15718163
20. Gilbert IH. Drug discovery for neglected diseases: molecular target-based and phenotypic approaches. Journal of medicinal chemistry. 2013Oct; 56(20):7719-7726. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24015767. doi: 10.1021/jm400362b PMID: 24015767
21. Hoelder S, Clarke PA, Workman P. Discovery of small molecule cancer drugs: successes, challenges and opportunities. Molecular oncology. 2012Apr; 6(2):155-176. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22440008. doi: 10.1016/j.molonc.2012.02.004 PMID: 22440008
22. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nature reviews Drug discovery. 2004Aug; 3(8):673-683. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15286734.
23. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, et al. Predicting new molecular targets for known drugs. Nature. 2009Nov; 462(7270):175-181. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19881490.
24. Moriaud F, Richard SB, Adcock SA, Chanas-Martin L, Surgand JS, Jelloul MB, et al. Identify drug repurposing candidates by mining the Protein Data Bank. Briefings in Bioinformatics. 2011; 12(4):336-340. Available from: http://dblp.uni-trier.de/db/journals/bib/bib12.html#MoriaudRACSJD11. doi: 10.1093/bib/bbr017 PMID: 21768131
25. Yang L, Agarwal P. Systematic Drug Repositioning Based on Clinical Side-Effects. PLoS ONE. 2011Dec; 6(12):e28025+. Available from: http://dx.doi.org/10.1371/journal.pone.0028025. doi: 10.1371/journal.pone.0028025 PMID: 22205936
26. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science (New York, NY). 2006Sep; 313(5795):1929-1935. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17008526. doi: 10.1126/science.1132939
27. Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, et al. Use of genome-wide association studies for drug repositioning. Nature biotechnology. 2012Apr; 30(4):317-320. Available from: http://dx.doi.org/10.1038/nbt.2151.
28. Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLoS computational biology. 2012Jul; 8(7):e1002574. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22859915. doi: 10.1371/journal.pcbi.1002574
29. He B, Tang J, Ding Y, Wang H, Sun Y, Shin JH, et al. Mining Relational Paths in Integrated Biomedical Data. PLoS ONE. 2011Dec; 6(12):e27506+. Available from: http://dx.doi.org/10.1371/journal.pone.0027506. doi: 10.1371/journal.pone.0027506 PMID: 22162991
30. Cockell SJ, Weile J, Lord P, Wipat C, Andriychenko D, Pocock M, et al. An integrated dataset for in silico drug discovery. Journal of integrative bioinformatics. 2010; 7(3). PMID: 20375448
31. Chiang AP, Butte AJ. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clinical pharmacology and therapeutics. 2009Nov; 86(5):507-510. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19571805.
32. Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular systems biology. 2011Jun; 7(1). Available from: http://dx.doi.org/10.1038/msb.2011.26.
33. Huang YF, Yeh HY, Soo VW. Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation. BMC medical genomics. 2013Nov; 6 Suppl 3:S4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24565337. doi: 10.1186/1755-8794-6-S3-S4 PMID: 24565337
34. Daminelli S, Haupt VJ, Reimann M, Schroeder M. Drug repositioning through incomplete bi-cliques in an integrated drug-target-disease network. Integrative biology: quantitative biosciences from nano to macro. 2012Jul; 4(7):778-788. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22538435.
35. Lee I, Date SV, Adai AT, Marcotte EM.A Probabilistic Functional Network of Yeast Genes. Science. 2004Nov; 306(5701):1555-1558. Available from: http://dx.doi.org/10.1126/science.1099511. doi: 10.1126/science.1099511
36. Mullen J, Cockell SJ, Tipney H, Woollard PM, Wipat A. Mining integrated semantic networks for drug repositioning opportunities. PeerJ. 2016 Jan; 4:e1558. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26844016. doi: 10.7717/peerj.1558
37. Online Mendelian Inheritance in Man, OMIM". McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, MD), 18-08-2015. World Wide Web URL:;. Available from: http://omim.org/.
38. Davis AP, Grondin CJ, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, et al. The Comparative Toxicogenomics Database's 10th year anniversary: update 2015. Nucleic acids research. 2015 Jan; 43(Database issue):D914-D920. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25326323.
39. UniProt Consortium. Activities at the Universal Protein Resource (UniProt). Nucleic acids research. 2014Jan; 42(Database issue):D191-D198. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24253303. doi: 10.1093/nar/gkt1140 PMID: 24253303
40. ©INSERM. Orphanet: an online rare disease and orphan drug data base.; 1997. [Online; accessed 19-July-2014]. Available from: http://www.orpha.net.
41. SemRep;. Accessed: 02-2015. http://skr3.nlm.nih.gov.
42. Molineris I, Ala U, Provero P, Di Cunto F. Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs). BMC bioinformatics. 2013Oct; 14:288. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24088245.
43. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic acids research. 2014Jan; 42(Database issue): D1001-D1006. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24316577. doi: 10.1093/nar/gkt1229
44. Gray KA, Yates B, Seal RL, Wright MW, Bruford EA. Genenames.org: the HGNC resources in 2015. Nucleic acids research. 2015 Jan; 43(Database issue):D1079-D1085. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25361968. doi: 10.1093/nar/gku1071
45. Rogers F. Medical subject headings. Bulletin of the Medical Library Association. 1963Jan; 51:114-116. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC197951/. PMID: 13982385
46. Kilicoglu H, Rosemblat G, Fiszman M, Rindflesch TC. Constructing a semantic predication gold standard from the biomedical literature. BMC bioinformatics. 2011Dec; 12:486. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22185221. doi: 10.1186/1471-2105-12-486 PMID: 22185221
47. Riaz F, Ali KM. Applications of Graph Theory in Computer Science. IEEE; 2011. p. 142-145. Available from: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6005872.
48. Betzler N, van Bevern R, Fellows MR, Komusiewicz C, Niedermeier R. Parameterized algorithmics for finding connected motifs in biological networks. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM. 2011Oct; 8(5):1296-1308. doi: 10.1109/TCBB.2011.19 PMID: 21282862
49. Weile J, James K, Hallinan J, Cockell SJ, Lord P, Wipat A, et al. Bayesian integration of networks without gold standards. Bioinformatics. 2012Jun; 28(11):1495-1500. Available from: http://dx.doi.org/10.1093/bioinformatics/bts154. doi: 10.1093/bioinformatics/bts154 PMID: 22492647
50. Curated drug-disease data were retrieved from the Comparative Toxicogenomics Database (CTD), MDI Biological Laboratory, Salisbury Cove, Maine, and NC State University, Raleigh, North Carolina. World Wide Web URL:;. Available from: URL: http://ctdbase.org/.
51. Orphadata: Free access data from Orphanet. ©INSERM 1997. Available on:;. Available from: http://www.orphadata.org.
52. Wishart DS. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research. 2006Jan; 34(90001):D668-D672. Available from: http://nar.oxfordjournals.org/lookup/doi/10.1093/nar/gkj067. doi: 10.1093/nar/gkj067 PMID: 16381955
53. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, et al. The ChEMBL bioactivity database: an update. Nucleic acids research. 2014Jan; 42(Database issue):D1083-D1090. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24214965. doi: 10.1093/nar/gkt1031 PMID: 24214965
54. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Molecular systems biology. 2010Jan; 6:343. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20087340. doi: 10.1038/msb.2009.98
55. NLM. NDR-RT API, 2011;. Available from: http://rxnav.nlm.nih.gov/NdfrtAPIs.html.
56. NDF-RT Release Notes, July 2011;. Available from: URL: http://evs.nci.nih.gov/ftp1/NDF-RT/ReadMe.txt.
57. Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular systems biology. 2011Jun; 7(1). Available from: http://dx.doi.org/10.1038/msb.2011.26.
58. Curated drug-disease data were retrieved from the Comparative Toxicogenomics Database (CTD), MDI Biological Laboratory, Salisbury Cove, Maine, and NC State University, Raleigh, North Carolina. World Wide Web URL:;. Available from: URL: http://ctdbase.org/.
59. Davis AP, Grondin CJ, Lennon-Hopkins K, Saraceni-Richards C, Sciaky D, King BL, et al. The Comparative Toxicogenomics Database's 10th year anniversary: update 2015. Nucleic acids research. 2015 Jan; 43(Database issue):D914-D920. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25326323. doi: 10.1093/nar/gku935 PMID: 25326323
60. Leacock C, Chodorow M. Combining local context and WordNet similarity for word sense identification. In: Fellfaum C, editor. MIT Press. Cambridge, Massachusetts; 1998. p. 265-283.
61. McInnes BT, Pedersen T, Pakhomov SVS. UMLS-Interface and UMLS-Similarity: open source software for measuring paths and semantic similarity. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2009Nov;2009:431-435. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20351894.
62. O'Connor TP, Crystal RG. Genetic medicines: treatment strategies for hereditary disorders. Nature reviews Genetics. 2006Apr; 7(4):261-276. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16543931. doi: 10.1038/nrg1829 PMID: 16543931
63. PharmaADME;. Accessed: 27-05-2015. http://www.PharmaADME.org.
64. University of California, Irvine. Trial of Dasatinib (Sprycel®) in Subjects With Hormone-refractory Prostate Cancer;. In:ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2000-[cited 2016 Mar 2]. Available from: https://clinicaltrials.gov/ct2/show/NCT00570700 NLM Identifier: NCT00570700.
65. University Health Network, Toronto. Study Of Sunitinib In Patients With Recurrent Paraganglioma/ Pheochromocytoma (SNIPP);. In: ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US). 2000- [cited 2016 Mar 2]. Available from: https://clinicaltrials.gov/ct2/show/NCT00843037 NLM Identifier: NCT00843037.
66. Ravaud A, de la Fouchardière C, Asselineau J, Delord JP, Do Cao C, Niccoli P, et al. Efficacy of sunitinib in advanced medullary thyroid carcinoma: intermediate results of phase II THYSU. The oncologist. 2010; 15(2):212-3; author reply 214. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20189981. doi: 10.1634/theoncologist.2009-0303 PMID: 20189981
67. Arock M, Akin C, Hermine O, Valent P. Current treatment options in patients with mastocytosis: status in 2015 and future perspectives. European journal of haematology. 2015 Jun; 94(6):474-490. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25753531. doi: 10.1111/ejh.12544 PMID: 25753531
68. Qiu WQ, Mwamburi M, Besser LM, Zhu H, Li H, Wallack M, et al. Angiotensin converting enzyme inhibitors and the reduced risk of Alzheimer's disease in the absence of apolipoprotein E4 allele. Journal of Alzheimer's disease: JAD. 2013; 37(2):421-428. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23948883. doi: 10.3233/JAD-130716 PMID: 23948883