In silico methods for drug repurposing and pharmacology

Wiley Interdisciplinary Reviews: Systems Biology and Medicine

Volumn 8, Issue 3, 2016, Pages 186-210

  Hodos, Rachel A ^a,b   Kidd, Brian A ^a   Shameer, Khader ^a   Readhead, Ben P ^a   Dudley, Joel T ^a  

^a MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHOLESTEROL ESTER TRANSFER PROTEIN INHIBITOR; FINASTERIDE; MINOXIDIL; TORCETRAPIB; DRUG; PROTEIN;

ARTICLE; COMPUTER MODEL; DATA MINING; DRUG CLASSIFICATION; DRUG INDICATION; DRUG REPOSITIONING; DRUG STRUCTURE; ELECTRONIC MEDICAL RECORD; GENE EXPRESSION; HUMAN; MOLECULAR INTERACTION; MOLECULAR MODEL; PHARMACOLOGY; PHENOTYPE; SIDE EFFECT; ADVERSE DRUG REACTION; ANIMAL; CHEMISTRY; CLASSIFICATION; DRUG INTERACTION; ECONOMICS; FACTUAL DATABASE; GENETICS; METABOLISM; MOLECULAR DOCKING; PROCEDURES;

ANIMALS; DATABASES, FACTUAL; DRUG INTERACTIONS; DRUG REPOSITIONING; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; GENE EXPRESSION; HUMANS; MOLECULAR DOCKING SIMULATION; PHARMACEUTICAL PREPARATIONS; PROTEINS;

EID: 84963553689     PISSN: 19395094     EISSN: 1939005X     Source Type: Journal    
DOI: 10.1002/wsbm.1337     Document Type: Article

References (168)

1. Horrobin DF . Realism in drug discovery-could Cassandra be right? Nat Biotechnol 2001, 19:1099-1100.
2. Pammolli F, Magazzini L, Riccaboni M . The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discov 2011, 10:428-438.
3. Cressey D . Traditional drug-discovery model ripe for reform. Nature 2011, 471:17-18.
4. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL . How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov 2010, 9:203-214.
5. Kaitin KI . Deconstructing the drug development process: the new face of innovation. Clin Pharmacol Ther 2010, 87:356-361.
6. Barratt MJ, Frail DE . Drug Repositioning: Bringing New Life to Shelved Assets and Existing Drugs. Hoboken, NJ: John Wiley Sons; 2012.
7. Arrowsmith J, Miller P . Trial watch: phase II and phase III attrition rates 2011-2012. Nat Rev Drug Discov 2013, 12:569.
8. Kola I, Landis J . Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004, 3:711-716.
9. NORD. National Organization for Rare Disorders (NORD), 2015. Available at: https://www.rarediseases.org/rare-disease-information. (Accessed March 1, 2016).
10. Denis A, Mergaert L, Fostier C, Cleemput I, Simoens S . A comparative study of European rare disease and orphan drug markets. Health Policy 2010, 97:173-179.
11. Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT . Data analysis and data mining: current issues in biomedical informatics. Methods Inf Med 2011, 50:536.
12. Margolis R, Derr L, Dunn M, Huerta M, Larkin J, Sheehan J, Guyer M, Green ED . The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data. J Am Med Inform Assoc 2014, 21:957-958.
13. Chen B, Greenside P, Paik H, Sirota M, Hadley D, Butte A . Relating chemical structure to cellular response: an integrative analysis of gene expression, bioactivity, and structural data across 11, 000 compounds. CPT Pharmacometrics Syst Pharmacol 2015, 4:576-584.
14. Vilar S, Uriarte E, Santana L, Lorberbaum T, Hripcsak G, Friedman C, Tatonetti NP . Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat Protoc 2014, 9:2147-2163.
15. Žitnik M, Janjić V, Larminie C, Zupan B, Pržulj N . Discovering disease-disease associations by fusing systems-level molecular data. Sci Rep 2013, 3:1-9.
16. Yang L, Agarwal P . Systematic drug repositioning based on clinical side-effects. PLoS One 2011, 6:e28025.
17. Zhang P, Wang F, Hu J, Sorrentino R . Exploring the relationship between drug side-effects and therapeutic indications. AMIA Annu Symp Proc 2013, 2013:1568-1577.
18. Napolitano F, Zhao Y, Moreira VM, Tagliaferri R, Kere J, D'Amato M, Greco D . Drug repositioning: a machine-learning approach through data integration. J Cheminform 2013, 5:30.
19. English SB, Butte AJ . Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes. Bioinformatics 2007, 23:2910-2917.
20. Bleakley K, Yamanishi Y . Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 2009, 25:2397-2403.
21. Ding H, Takigawa I, Mamitsuka H, Zhu S . Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief Bioinform 2014, 15:734-747.
22. Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P . Drug target identification using side-effect similarity. Science 2008, 321:263-266.
23. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK . Relating protein pharmacology by ligand chemistry. Nat Biotechnol 2007, 25:197-206.
24. Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y . Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 2012, 8:e1002503.
25. Vilar S, Ryan PB, Madigan D, Stang PE, Schuemie MJ, Friedman C, Tatonetti NP, Hripcsak G . Similarity-based modeling applied to signal detection in pharmacovigilance. CPT Pharmacometrics Syst Pharmacol 2014, 3:e137.
26. Ye H, Liu Q, Wei J . Construction of drug network based on side effects and its application for drug repositioning. PLoS One 2014, 9:e87864.
27. Chiang AP, Butte AJ . Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther 2009, 86:507-510.
28. Zhang P, Wang F, Hu J . Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. AMIA Annu Symp Proc 2014, 2014:1258-1267.
29. Gottlieb A, Stein GY, Ruppin E, Sharan R . PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011, 7:496.
30. Weininger D . SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 1988, 28:31-36.
31. Gaiteri C, Ding Y, French B, Tseng GC, Sibille E . Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 2014, 13:13-24.
32. McNaught A . The iupac international chemical identifier. Chem Int 2006, 28:12-14.
33. O'Boyle NM . Towards a Universal SMILES representation-a standard method to generate canonical SMILES based on the InChI. J Cheminform 2012, 4:22.
34. Xue L, Godden JW, Stahura FL, Bajorath J . Design and evaluation of a molecular fingerprint involving the transformation of property descriptor values into a binary classification scheme. J Chem Inf Comput Sci 2003, 43:1151-1157.
35. Durant JL, Leland BA, Henry DR, Nourse JG . Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 2002, 42:1273-1280.
36. Bolton EE, Wang Y, Thiessen PA, Bryant SH . PubChem: integrated platform of small molecules and biological activities. Annu Rep Comput Chem 2008, 4:217-241.
37. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Krüger FA, Light Y, Mak L, McGlinchey S . The ChEMBL bioactivity database: an update. Nucleic Acids Res 2014, 42:D1083-D1090.
38. Yung-Chi C, Prusoff WH . Relationship between the inhibition constant (K i) and the concentration of inhibitor which causes 50 per cent inhibition (I 50) of an enzymatic reaction. Biochem Pharmacol 1973, 22:3099-3108.
39. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J . DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006, 34:D668-D672.
40. Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK . BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 2007, 35:D198-D201.
41. Ben-Dayan MM, MacCarthy T, Schlecht NF, Belbin TJ, Childs G, Smith RV, Prystowsky MB, Bergman A . Cancer as the disintegration of robustness: population-level variance in gene expression identifies key differences between tobacco-and HPV-associated oropharyngeal carcinogenesis. Arch Pathol Lab Med 2015, 139:1362-1372.
42. Hsu C-L, Juan H-F, Huang H-C . Functional analysis and characterization of differential coexpression networks. Sci Rep 2015, 5:13295.
43. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006, 313:1929-1935.
44. Duan Q, Flynn C, Niepel M, Hafner M, Muhlich JL, Fernandez NF, Rouillard AD, Tan CM, Chen EY, Golub TR . LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 2014, 42:W449-W460.
45. Edgar R, Domrachev M, Lash AE . Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30:207-210.
46. Lab Ma . BD2K-LINCS-DCIC crowdsourcing portal. Available at: http://www.maayanlab.net/crowdsourcing. (Accessed November 25, 2015).
47. Cheng J, Xie Q, Kumar V, Hurle M, Freudenberg JM, Yang L, AGARWAL P . Evaluation of analytical methods for connectivity map data. Pac Symp Biocomput 2013, 5-16.
48. Cheng J, Yang L, Kumar V, Agarwal P . Systematic evaluation of connectivity map for disease indications. Genome Med 2014, 6:540.
49. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, Ebright RY, Stewart ML, Ito D, Wang S . An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 2013, 154:1151-1161.
50. Shoemaker RH . The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 2006, 6:813-823.
51. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR . Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013, 41:D955-D961.
52. Rihel J, Prober DA, Arvanites A, Lam K, Zimmerman S, Jang S, Haggarty SJ, Kokel D, Rubin LL, Peterson RT . Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science 2010, 327:348-351.
53. Swinney D . Phenotypic vs. target-based drug discovery for first-in-class medicines. Clin Pharmacol Ther 2013, 93:299-301.
54. Zheng W, Thorne N, McKew JC . Phenotypic screens as a renewed approach for drug discovery. Drug Discov Today 2013, 18:1067-1073.
55. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA . PubChem's BioAssay database. Nucleic Acids Res 2012, 40:D400-D412.
56. Wang Y, Bolton E, Dracheva S, Karapetyan K, Shoemaker BA, Suzek TO, Wang J, Xiao J, Zhang J, Bryant SH . An overview of the PubChem BioAssay resource. Nucleic Acids Res 2010, 38:D255-D266.
57. Lee JA, Shinn P, Jaken S, Oliver S, Willard FS, Heidler S, Peery RB, Oler J, Chu S, Southall N . Novel phenotypic outcomes identified for a public collection of approved drugs from a publicly accessible panel of assays. PLoS One 2015, 10:e0130796.
58. Huang R, Southall N, Wang Y, Yasgar A, Shinn P, Jadhav A, Nguyen D-T, Austin CP . The NCGC pharmaceutical collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci Transl Med 2011, 3:80ps16.
59. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D . The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012, 483:603-607.
60. Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, Bansal M, Hintsanen P, Khan SA, Mpindi J-P . A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 2014, 32:1202-1212.
61. Organization WH . Guidelines for ATC Classification and DDD Assignment . Geneva: World Health Organization; 1996.
62. DrugBank. ATC classification browser. Available at: http://www.drugbank.ca/atc. (Accessed September 28, 2015).
63. 2015AA UMLS National drug file-reference terminology source information. Available at: https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NDFRT/index.html. (Accessed November 8, 2015).
64. Lipscomb CE . Medical subject headings (MeSH). Bull Med Libr Assoc 2000, 88:265.
65. Liu S, Ma W, Moore R, Ganesan V, Nelson S . RxNorm: prescription for electronic drug information exchange. IT Prof 2005, 7:17-23.
66. Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, Alcántara R, Darsow M, Guedj M, Ashburner M . ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 2008, 36:D344-D350.
67. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M . From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 2006, 34:D354-D357.
68. Pharos. Available at: https://pharos.nih.gov/idg/index. (Accessed September 28, 2015).
69. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, Altman RB, Klein TE . Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012, 92:414-417.
70. Food U, Administration D . Orange book: Approved Drug Products with Therapeutic Equivalence Evaluations. US FDA: Silver Spring, MD; 2010.
71. Administration FaD. FDALabel: full-text search of drug labeling. Available at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm289739.htm - Issues. (Accessed November 8, 2015).
72. Martínez V, Navarro C, Cano C, Fajardo W, Blanco A . DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med 2015, 63:41-49.
73. Health NIo. ClinicalTrials.gov. Available at: http://www.clinicalTrials.gov. (Accessed October 6, 2015).
74. Schriml LM, Arze C, Nadendla S, Chang Y-WW, Mazaitis M, Felix V, Feng G, Kibbe WA . Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res 2012, 40:D940-D946.
75. Bodenreider O . The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32:D267-D270.
76. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P . A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 2010, 6:343.
77. Tatonetti NP, Patrick PY, Daneshjou R, Altman RB . Data-driven prediction of drug effects and interactions. Sci Transl Med 2012, 4:125ra131.
78. Zhou H, Gao M, Skolnick J . Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci Rep 2015, 5:11090.
79. Meng X-Y, Zhang H-X, Mezei M, Cui M . Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 2011, 7:146.
80. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH . Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 2012, 14:133-141.
81. Yang S-Y . Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 2010, 15:444-450.
82. Krüger DM, Evers A . Comparison of structure-and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 2010, 5:148-158.
83. Chen Z, H-l L, Q-j Z, Bao X-g, Yu K-q, Luo X-m, Zhu W-l, H-l J . Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets. Acta Pharmacol Sin 2009, 30:1694-1708.
84. Alvarez J, Shoichet B . Virtual Screening in Drug Discovery. Boca Raton, FL: CRC press; 2005.
85. Gao M, Skolnick J . A comprehensive survey of small-molecule binding pockets in proteins. PLoS Comput Biol 2013, 9:e1003302.
86. Paolini GV, Shapland RH, van Hoorn WP, Mason JS, Hopkins AL . Global mapping of pharmacological space. Nat Biotechnol 2006, 24:805-815.
87. Nidhi GM, Davies JW, Jenkins JL . Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 2006, 46:1124-1133.
88. Jacob L, Vert J-P . Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 2008, 24:2149-2156.
89. Bock JR, Gough DA . Virtual screen for ligands of orphan G protein-coupled receptors. J Chem Inf Model 2005, 45:1402-1414.
90. Cao D-S, Liang Y-Z, Deng Z, Hu Q-N, He M, Xu Q-S, Zhou G-H, Zhang L-X, Deng Z, Liu S . Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach. PLoS One 2013, 8:e57680.
91. Liu H, Sun J, Guan J, Zheng J, Zhou S . Improving compound-protein interaction prediction by building up highly credible negative samples. Bioinformatics 2015, 31:i221-i229.
92. Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V . Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072 2015.
93. Dahl GE, Jaitly N, Salakhutdinov R. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231 2014.
94. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, et al. Predicting new molecular targets for known drugs. Nature 2009, 462:175-181.
95. Bottini AA, Hartung T . Food for thought on the economics of animal testing. ALTEX 2009, 26:3-16.
96. Jucker M . The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nat Med 2010, 16:1210-1214.
97. Mak IW, Evaniew N, Ghert M . Lost in translation: animal models and clinical trials in cancer treatment. Am J Transl Res 2014, 6:114.
98. Lounkine E, Keiser M, Whitebread S, Mikhailov D, Hamon J, Jenkins J, Lavan P, Weber E, Doak A, Côté S, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 2012, 486:361-367.
99. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci 2010, 107:14621-14626.
100. Lum PY, Derry JM, Schadt EE . Integrative genomics and drug development. Pharmacogenomics 2009, 10:203-12.
101. Yom-Tov E, Gabrilovich E . Postmarket drug surveillance without trial costs: discovery of adverse drug reactions through large-scale analysis of web search queries. J Med Internet Res 2013, 15:e124.
102. White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E . Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Inform Assoc 2013, 20:404-408.
103. Bender A, Scheiber J, Glick M, Davies JW, Azzaoui K, Hamon J, Urban L, Whitebread S, Jenkins JL . Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure. ChemMedChem 2007, 2:861-873.
104. Azzaoui K, Hamon J, Faller B, Whitebread S, Jacoby E, Bender A, Jenkins JL, Urban L . Modeling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem 2007, 2:874-880.
105. Zhou H, Skolnick J . FINDSITEcomb: a threading/structure-based, proteomic-scale virtual ligand screening approach. J Chem Inf Model 2012, 53:230-240.
106. Barter PJ, Caulfield M, Eriksson M, Grundy SM, Kastelein JJ, Komajda M, Lopez-Sendon J, Mosca L, Tardif J-C, Waters DD . Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med 2007, 357:2109-2122.
107. Chang RL, Xie L, Xie L, Bourne PE, Palsson BØ . Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol. 2010, 6:e1000938.
108. Kim HU, Sohn SB, Lee SY . Metabolic network modeling and simulation for drug targeting and discovery. Biotechnol J 2012, 7:330-342.
109. Fan S, Geng Q, Pan Z, Li X, Tie L, Pan Y, Li X . Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach. BMC Syst Biol 2012, 6:152.
110. Scheiber J, Jenkins JL, Sukuru SCK, Bender A, Mikhailov D, Milik M, Azzaoui K, Whitebread S, Hamon J, Urban L . Mapping adverse drug reactions in chemical space. J Med Chem 2009, 52:3103-3107.
111. Liu M, Cai R, Hu Y, Matheny ME, Sun J, Hu J, Xu H . Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. J Am Med Inform Assoc 2014, 21:245-251.
112. Koch U, Hamacher M, Nussbaumer P . Cheminformatics at the interface of medicinal chemistry and proteomics. Biochim Biophys Acta 2014, 1844:156-161.
113. Glicksberg BS, Li L, Cheng WY, Shameer K, Hakenberg J, Castellanos R, Ma M, Shi L, Shah H, Dudley JT, et al. An integrative pipeline for multi-modal discovery of disease relationships. Pac Symp Biocomput 2015, 20:407-418.
114. Piro RM . Network medicine: linking disorders. Hum Genet 2012, 131:1811-1820.
115. Dudley JT, Deshpande T, Butte AJ . Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 2011, 12:303-311.
116. Ashburn TT, Thor KB . Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 2004, 3:673-683.
117. Elvidge S . Getting the drug repositioning genie out of the bottle. Life Sci Leader 2016, 14-18.
118. Ekins S, Williams AJ, Krasowski MD, Freundlich JS . In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 2011, 16:298-310.
119. Muthyala R . Orphan/rare drug discovery through drug repositioning. Drug Discov Today 2011, 8:71-76.
120. Sardana D, Zhu C, Zhang M, Gudivada RC, Yang L, Jegga AG . Drug repositioning for orphan diseases. Brief Bioinform 2011, 12:346-356.
121. Molineris I, Ala U, Provero P, Di Cunto F . Drug repositioning for orphan genetic diseases through conserved anticoexpressed gene clusters (CAGCs). BMC Bioinform 2013, 14:288.
122. Chavali AK, Blazier AS, Tlaxca JL, Jensen PA, Pearson RD, Papin JA . Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease. BMC Syst Biol 2012, 6:27.
123. Liu Z, Borlak J, Tong W . Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis. Genome Med 2014, 6:94.
124. Andrews KT, Fisher G, Skinner-Adams TS . Drug repurposing and human parasitic protozoan diseases. Int J Parasitol Drugs Drug Resist 2014, 4:95-111.
125. Martínez V, Cano C, Blanco A . ProphNet: a generic prioritization method through propagation of information. BMC Bioinform 2014, 15:S5.
126. Gormley GJ, Stoner E, Bruskewitz RC, Imperato-McGinley J, Walsh PC, McConnell JD, Andriole GL, Geller J, Bracken BR, Tenover JS, et al. The effect of finasteride in men with benign prostatic hyperplasia. The Finasteride Study Group. N Engl J Med 1992, 327:1185-1191.
127. Ferry L, Johnston JA . Efficacy and safety of bupropion SR for smoking cessation: data from clinical trials and five years of postmarketing experience. Int J Clin Pract 2003, 57:224-230.
128. Wu P, Wilson K, Dimoulas P, Mills EJ . Effectiveness of smoking cessation therapies: a systematic review and meta-analysis. BMC Public Health 2006, 6:300.
129. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ . Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 2011, 3:96ra77.
130. McArt DG, Zhang S-D . Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping. PLoS One 2011, 6:e16382.
131. Ramachandran S, Osterhaus SR, Karp PH, Welsh MJ, McCray PB Jr . A genomic signature approach to rescue ΔF508-cystic fibrosis transmembrane conductance regulator biosynthesis and function. Am J Respir Cell Mol Biol 2014, 51:354-362.
132. Hu G, Agarwal P . Human disease-drug network based on genomic expression profiles. PLoS One 2009, 4:e6536.
133. Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J . Transcriptional data: a new gateway to drug repositioning? Drug Discov Today 2013, 18:350-357.
134. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, Morgan AA, Sarwal MM, Pasricha PJ, Butte AJ . Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 2011, 3:96ra76.
135. Iskar M, Zeller G, Blattmann P, Campillos M, Kuhn M, Kaminska KH, Runz H, Gavin AC, Pepperkok R, van Noort V . Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol Syst Biol 2013, 9:662.
136. Parkkinen JA, Kaski S . Probabilistic drug connectivity mapping. BMC Bioinform 2014, 15:113.
137. Yao L, Zhang Y, Li Y, Sanseau P, Agarwal P . Electronic health records: Implications for drug discovery. Drug Discov Today 2011, 16:594-599.
138. Xu H, Aldrich MC, Chen Q, Liu H, Peterson NB, Dai Q, Levy M, Shah A, Han X, Ruan X . Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc 2014, 22:179-191.
139. Maggiora GM . On outliers and activity cliffs why QSAR often disappoints. J Chem Inf Model 2006, 46:1535.
140. Kangas JD, Naik AW, Murphy RF . Efficient discovery of responses of proteins to compounds using active learning. BMC bioinformatics 2014, 15:1.
141. Huang Y-F, Yeh H-Y, Soo V-W . Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation. BMC Med Genomics 2013, 6:S4.
142. Chindelevitch L, Ziemek D, Enayetallah A, Randhawa R, Sidders B, Brockel C, Huang ES . Causal reasoning on biological networks: interpreting transcriptional changes. Bioinformatics 2012, 28:1114-1121.
143. Schmidt BJ, Papin JA, Musante CJ . Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today 2013, 18:116-127.
144. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 2005, 37:710-717.
145. Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Walters GB, Gunnarsdottir S, et al. Genetics of gene expression and its effect on disease. Nature 2008, 452:423-428.
146. Argmann C, Dobrin R, Heikkinen S, Auburtin A, Pouilly L, Cock TA, Koutnikova H, Zhu J, Schadt EE, Auwerx J . Ppargamma2 is a key driver of longevity in the mouse. PLoS Genet 2009, 5:e1000752.
147. Hopkins AL . Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008, 4:682-690.
148. Schadt EE, Friend SH, Shaywitz DA . A network view of disease and compound screening. Nat Rev Drug Discov 2009, 8:286-295.
149. Pinto JP, Machado RS, Xavier JM, Futschik ME . Targeting molecular networks for drug research. Front Genet 2014, 5:160.
150. Li Z, Wang RS, Zhang XS . Two-stage flux balance analysis of metabolic networks for drug target identification. BMC Syst Biol 2011, 5(Suppl 1):S11.
151. Langfelder P, Horvath S . WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 2008, 9:559.
152. Zhang B, Horvath S . A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005, 4:17.
153. Barabási A-L . Scale-free networks: a decade and beyond. Science 2009, 325:412.
154. Zhang B, Gaiteri C, Bodea L-G, Wang Z, McElwee J, Podtelezhnikov AA, Zhang C, Xie T, Tran L, Dobrin R . Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 2013, 153:707-720.
155. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, Lee JC, Schumm LP, Sharma Y, Anderson CA, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 2012, 491:119-124.
156. Daly R, Shen Q, Aitken S . Learning Bayesian networks: approaches and issues. Knowl Eng Rev 2011, 26:99-157.
157. Sima C, Hua J, Jung S . Inference of gene regulatory networks using time-series data: a survey. Curr Genomics 2009, 10:416-429.
158. Dai W, Liu X, Gao Y, Chen L, Song J, Chen D, Gao K, Jiang Y, Yang Y, Chen J . Matrix factorization-based prediction of novel drug indications by integrating genomic space. Comput Math Methods Med 2015, 2015:275045.
159. Cobanoglu MC, Liu C, Hu F, Oltvai ZN, Bahar I . Predicting drug-target interactions using probabilistic matrix factorization. J Chem Inf Model 2013, 53:3399-3409.
160. Li R, Dong Y, Kuang Q, Wu Y, Li Y, Zhu M, Li M . Inductive matrix completion for predicting adverse drug reactions (ADRs) integrating drug-target interactions. Chemometr Intell Lab Syst 2015, 144:71-79.
161. Bell RM, Koren Y, Volinsky C . The BellKor solution to the Netflix prize. Technical Report, AT&T Labs Research, 2007.
162. Töscher A, Jahrer M . The bigchaos solution to the netflix prize 2008. Netflix Prize Report, 2008.
163. Piotte M, Chabbert M . The pragmatic theory solution to the netflix grand prize. Netflix Prize Documentation, 2009.
164. Bennett J, Lanning S . The netflix prize. Proceedings of KDD cup and workshop: In; 2007.
165. Chi EC, Zhou H, Chen GK, Del Vecchyo DO, Lange K . Genotype imputation via matrix completion. Genome Res 2013, 23:509-518.
166. Saporito MS, Lipinski CA, Reaume AG . Phenotypic in vivo screening to identify new, unpredicted indications for existing drugs and drug candidates. In: Barratt MJ, Frail DE, eds. Drug Repositioning: Bringing New Life to Shelved Assets and Existing Drugs. Hoboken, NJ: John Wiley & Sons, Inc; 2012, 253-290.
167. Ho JC, Ghosh J, Steinhubl SR, Stewart WF, Denny JC, Malin BA, Sun J . Limestone: high-throughput candidate phenotype generation via tensor factorization. J Biomed Inform 2014, 52:199-211.
168. Li L, Cheng W-Y, Glicksberg BS, Gottesman O, Tamler R, Chen R, Bottinger EP, Dudley JT . Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 2015, 7:311ra174.