Inferring new drug indications using the complementarity between clinical disease signatures and drug effects

Journal of Biomedical Informatics

Volumn 59, Issue , 2016, Pages 248-257

  Jang, Dongjin ^a,b   Lee, Sejoon ^b   Lee, Jaehyun ^a,b   Kim, Kiseong ^a,b   Lee, Doheon ^a,b  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b BioProcess Engineering Research Center   (South Korea)

Author keywords

Clinical disease signatures; Clinical drug effects; Drug repositioning; Electronic clinical information

Indexed keywords

MEDICAL COMPUTING; PHYSIOLOGY; POINT CONTACTS; VECTORS;

CLINICAL DISEASE; CLINICAL DRUGS; CLINICAL INFORMATION; COMPUTATIONAL FRAMEWORK; DRUG REPOSITIONING; PHYSIOLOGICAL ACTIVITY; STATISTICAL EXPERIMENTS; STATISTICAL SIGNIFICANCE;

CLINICAL RESEARCH;

EDETIC ACID; GLUTATHIONE;

ARTICLE; ASTHMA; BIOLOGICAL ACTIVITY; CLINICAL STUDY; CLINICAL TRIAL (TOPIC); COMPARATIVE TOXICOGENOMICS DATABASE; DATA BASE; DATA MINING; DISEASES; DRUG EFFECT; DRUG INDICATION; DRUG REPOSITIONING; DRUG RESEARCH; ELECTRONIC MEDICAL RECORD; HUMAN; MATHEMATICAL ANALYSIS; PREDICTION; PRIORITY JOURNAL; STATISTICAL ANALYSIS; SYSTEMATIC REVIEW; ELECTRONIC HEALTH RECORD; MEDICAL INFORMATICS; MEDICAL RESEARCH; PROCEDURES;

BIOMEDICAL RESEARCH; DATA MINING; DRUG REPOSITIONING; ELECTRONIC HEALTH RECORDS; HUMANS; MEDICAL INFORMATICS APPLICATIONS;

EID: 84961653866     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.12.003     Document Type: Article

References (67)

1. O'Connor K.A., Roth B.L. Finding new tricks for old drugs: an efficient route for public-sector drug discovery. Nat. Rev. Drug Discovery 2005, 4:1005-1014.
2. Chong C.R., Sullivan D.J. New uses for old drugs. Nature 2007, 448:645-646.
3. Keiser M.J., Setola V., Irwin J.J., Laggner C., Abbas A.I., Hufeisen S.J., et al. Predicting new molecular targets for known drugs. Nature 2009, 462:175-181.
4. Chang M.W., Ayeni C., Breuer S., Torbett B.E. Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina. PLoS ONE 2010, 5:e11955.
5. Kinnings S.L., Liu N., Buchmeier N., Tonge P.J., Xie L., Bourne P.E. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput. Biol. 2009, 5:e1000423.
6. Zahler S., Tietze S., Totzke F., Kubbutat M., Meijer L., Vollmar A.M., et al. Inverse in silico screening for identification of kinase inhibitor targets. Chem. Biol. 2007, 14:1207-1214.
7. Yamanishi Y., Kotera M., Kanehisa M., Goto S. Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 2010, 26:i246-i254.
8. Mei J.-P., Kwoh C.-K., Yang P., Li X.-L., Zheng J. Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics 2013, 29:238-245.
9. Yamanishi Y., Araki M., Gutteridge A., Honda W., Kanehisa M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008, 24:i232-i240.
10. Iorio F., Bosotti R., Scacheri E., Belcastro V., Mithbaokar P., Ferriero R., et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. 2010, 107:14621-14626.
11. Lamb J., Crawford E.D., Peck D., Modell J.W., Blat I.C., Wrobel M.J., et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006, 313:1929-1935.
12. Sirota M., Dudley J.T., Kim J., Chiang A.P., Morgan A.A., Sweet-Cordero A., et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Trans. Med. 2011, 3. 96ra77-96ra77.
13. Dudley J.T., Deshpande T., Butte A.J. Exploiting drug-disease relationships for computational drug repositioning. Briefings Bioinform. 2011, 12:303-311.
14. Tatonetti N.P., Fernald G.H., Altman R.B. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J. Am. Med. Inform. Assoc. 2012, 19:79-85.
15. Campillos M., Kuhn M., Gavin A.-C., Jensen L.J., Bork P. Drug target identification using side-effect similarity. Science 2008, 321:263-266.
16. Yang L., Agarwal P. Systematic drug repositioning based on clinical side-effects. PLoS ONE 2011, 6:e28025.
17. Holmes A.B., Hawson A., Liu F., Friedman C., Khiabanian H., Rabadan R. Discovering disease associations by integrating electronic clinical data and medical literature. PLoS ONE 2011, 6:e21132.
18. Jung J., Lee D. Inferring disease association using clinical factors in a combinatorial manner and their use in drug repositioning. Bioinformatics 2013, 29:2017-2023.
19. Roque F.S., Jensen P.B., Schmock H., Dalgaard M., Andreatta M., Hansen T., et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput. Biol. 2011, 7:e1002141.
20. Jensen P.B., Jensen L.J., Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 2012, 13:395-405.
21. Jung K., LePendu P., Chen W.S., Iyer S.V., Readhead B., Dudley J.T., et al. Automated detection of off-label drug use. PLoS ONE 2014, 9:e89324.
22. Jung K., LePendu P., Shah N. Automated detection of systematic off-label drug use in free text of electronic medical records. AMIA Summits Trans. Sci. Proc. 2013, 2013:94.
23. Y. Li, H. Salmasian, R. Harpaz, H. Chase, C. Friedman, Determining the reasons for medication prescriptions in the EHR using knowledge and natural language processing, in: AMIA Annual Symposium Proceedings: American Medical Informatics Association, 2011, p. 768.
24. C. Cdc, National Health and Nutrition Examination Survey. NCFHS (NCHS), in: M.D. Hyattsville (Ed.), US Department of Health and Human Services, Centers for Disease Control and Prevention, 2005.
25. Davis A.P., Grondin C.J., Lennon-Hopkins K., Saraceni-Richards C., Sciaky D., King B.L., et al. The comparative toxicogenomics Database's 10th year anniversary: update 2015. Nucleic Acids Res. 2015, 43:D914-D920.
26. National Institutes of Health US. ClinicalTrials.gov, 2000, available: <>. https://clinicaltrials.gov.
27. Wishart D.S., Knox C., Guo A.C., Cheng D., Shrivastava S., Tzur D., et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008, 36:D901-D906.
28. Suresh K., Chandrashekara S. Sample size estimation and power analysis for clinical research studies. J. Human Reprod. Sci 2012, 5:7.
29. Zodpey S.P. Sample size and power analysis in medical research. Indian J. Dermatol., Venereol. Leprol. 2004, 70.
30. J.A. Osborne, Estimating the false Discovery Rate using SAS, SAS Users Group International Proceedings, 2006, p. 1-10.
31. Storey J.D., Tibshirani R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. 2003, 100:9440-9445.
32. Jenssen T.-K., Lægreid A., Komorowski J., Hovig E. A literature network of human genes for high-throughput analysis of gene expression. Nat. Genet. 2001, 28:21-28.
33. C. Giuliano, A. Lavelli, L. Romano, Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature, EACL, 2006, p. 98-113.
34. Segura-Bedmar I., Martinez P., de Pablo-Sánchez C. Using a shallow linguistic kernel for drug-drug interaction extraction. J. Biomed. Inform. 2011, 44:789-804.
35. Tikk D., Thomas P., Palaga P., Hakenberg J., Leser U. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature. PLoS Comput. Biol. 2010, 6:e1000837.
36. Fundel K., Küffner R., Zimmer R. RelEx - relation extraction using dependency parse trees. Bioinformatics 2007, 23:365-371.
37. Miller G.A. WordNet: a lexical database for English. Commun. ACM 1995, 38:39-41.
38. Knijnenburg T.A., Wessels L.F., Reinders M.J., Shmulevich I. Fewer permutations, more accurate P-values. Bioinformatics 2009, 25:i161-i168.
39. Fitzpatrick A.M., Jones D.P., Brown L.A.S. Glutathione redox control of asthma: from molecular mechanisms to therapeutic opportunities. Antioxid. Redox Signal. 2012, 17:375-408.
40. Jayawardena R., Ranasinghe P., Galappatthy P., Malkanthi R., Constantine G., Katulanda P. Effects of zinc supplementation on diabetes mellitus: a systematic review and meta-analysis. Diabetol Metab Syndr. 2012, 4:13.
41. Lang C., Murgia C., Leong M., Tan L.-W., Perozzi G., Knight D., et al. Anti-inflammatory effects of zinc and alterations in zinc transporter mRNA in mouse models of allergic inflammation. Am. J. Physiol. - Lung Cell. Mol. Physiol. 2007, 292:L577-L584.
42. Menon V.P., Sudheer A.R. Antioxidant and anti-inflammatory properties of curcumin. The Molecular Targets and Therapeutic uses of Curcumin in Health and Disease 2007, 105-125. Springer.
43. Pratviel G. Oxidative DNA damage mediated by transition metal ions and their complexes. Interplay between Metal Ions and Nucleic Acids 2012, 201-216. Springer.
44. Karaki H., Satake N., Shibata S. Mechanism of barium-induced contraction in the vascular smooth muscle of rabbit aorta. Br. J. Pharmacol. 1986, 88:821-826.
45. Regnier M., Chase P., Martyn D. Contractile properties of rabbit psoas muscle fibres inhibited by beryllium fluoride. J. Muscle Res. Cell Motil. 1999, 20:425-432.
46. Benson E.A., Healey L., Barron E.J. Insulin antibodies in patients receiving penicillamine. Am. J. Med. 1985, 78:857-860.
47. Taylor S., Barbetti F., Accili D., Roth J., Gorden P. Syndromes of autoimmunity and hypoglycemia. Autoantibodies directed against insulin and its receptor. Endocrinol. Metab. Clin. North Am. 1989, 18:123-143.
48. Kauschansky A., Frydman M., Assa S., Kwon O.J., Israel S., Lazard D., et al. D-penicillamine-induced pancreatic islet autoantibody production is independent of the immunogenetic background: a lesson from patients with Wilson's disease. Clin. Immunol. Immunopathol. 1998, 89:279-283.
49. Piwowar A., Knapik-Kordecka M., Warwas M. Ischemia-modified albumin level in type 2 diabetes mellitus - preliminary report. Dis. Markers 2008, 24:311-317.
50. Suksomboon N., Poolsup N., Prasit T. Systematic review of the effect of telmisartan on insulin sensitivity in hypertensive patients with insulin resistance or diabetes. J. Clin. Pharm. Ther. 2012, 37:319-327.
51. Coco M., Perciavalle V. Creatine ingestion effects on oxidative stress in a steady-state test at 75% VO (2max). J. Sports Med. Phys. Fitness 2012, 52:165-169.
52. Ezekowitz J.A., McAlister F.A., Armstrong P.W. Anemia is common in heart failure and is associated with poor outcomes insights from a cohort of 12 065 patients with new-onset heart failure. Circulation 2003, 107:223-225.
53. Karolczak K., Pietruszynski R., Drzewoski J., Kasznicki J., Watala C. Aspirin dose increase from 75 to 150 mg suppresses red blood cell contribution to suboptimal platelet response to aspirin in patients with CAD. Cardiovasc. Drugs Ther. 2013, 27:549-558.
54. Hansen P.R. Role of neutrophils in myocardial ischemia and reperfusion. Circulation 1995, 91:1872-1885.
55. Jordan J.E., Zhao Z.-Q., Vinten-Johansen J. The role of neutrophils in myocardial ischemia-reperfusion injury. Cardiovasc. Res. 1999, 43:860-878.
56. Tanaka J., Tajima S., Asakawa K., Sakagami T., Moriyama H., Takada T., et al. Preventive effect of irbesartan on bleomycin-induced lung injury in mice. Respir. Investigat. 2013, 51:76-83.
57. Kharazmi A., Nielsen H., Schiøtz P. N-acetylcysteine inhibits human neutrophil and monocyte chemotaxis and oxidative metabolism. Int. J. Immunopharmacol. 1988, 10:39-46.
58. Sochman J. N-acetylcysteine in acute cardiology: 10 years later: What do we know and what would we like to know?!. J. Am. Coll. Cardiol. 2002, 39:1422-1428.
59. Osman M.M., Lulic D., Glover L., Stahl C.E., Lau T., van Loveren H., et al. Cyclosporine-A as a neuroprotective agent against stroke: its translation from laboratory research to clinical application. Neuropeptides 2011, 45:359-368.
60. Khan A.A., Faruqi N.A., Ansari M.S. Effects of hydrocortisone on the sciatic nerve crush injury in adult rat - a light microscopic study. Curr. Neurobiol. 2014, 5.
61. Sethi H.S., Schmidley J.W. Cerebral infarcts in the setting of eosinophilia: three cases and a discussion. Arch. Neurol. 2010, 67:1275-1277.
62. Sharma S.K., Kumar S., Seth T., Mishra P., Mahapatra M. Eosinophilia: rare cause of arterial thrombosis and cardioembolic stroke in childhood. World J. Cardiol. 2012, 4:128.
63. Kim S.H., Kwon J.E., Kim H.-B. Successful treatment of steroid-dependent eosinophilic cellulitis with cyclosporine. Allergy, Asthma Immunol. Res. 2013, 5:62-64.
64. Lagente V., Carre C., Kyriacopoulos F., Boichot E., Mencia-Huerta J., Braquet P. Inhibitory effect of Cyclosporin A on eosinophil infiltration in the guinea-pig lung induced by antigen, platelet-activating factor and leukotriene B4. Eur. Respir. J. 1994, 7:921-926.
65. Prevention KCfDCa. Korean Genome and Epidemiology Study (KoGES), 2001, available from: <>. http://cdcgokr/CDC/contents/CdcKrContentViewjsp?cid=24606%26viewType=CDC%26menuIds=HOME001-MNU1136-MNU1223-MNU1348.
66. M. Saeed, C. Lieu, G. Raber, R. Mark, MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring, Computers in Cardiology, IEEE, 2002, pp. 641-4.
67. Weinstein J.N., Collisson E.A., Mills G.B., Shaw K.R.M., Ozenberger B.A., Ellrott K., et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 2013, 45:1113-1120.