Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Briefings in Bioinformatics

Volumn 19, Issue 4, 2018, Pages 656-678

  Shameer, Khader ^a   Glicksberg, Benjamin S ^a   Hodos, Rachel ^a,b   Johnson, Kipp W ^a   Badgeley, Marcus A ^a   Readhead, Ben ^a   Tomlinson, Max S ^a   O'Connor, Timothy ^c   Miotto, Riccardo ^a   Kidd, Brian A ^a   Chen, Rong ^d   Ma'Ayan, Avi ^d   Dudley, Joel T ^a,d  

^a MOUNT SINAI HEALTH SYSTEM   (United States)

^b NEW YORK UNIVERSITY   (United States)

^c BOSTON COLLEGE   (United States)

^d MOUNT SINAI SCHOOL OF MEDICINE   (United States)

Author keywords

drug development; drug discovery; drug repositioning; precision medicine; precision pharmacology; systems pharmacology; translational bioinformatics

Indexed keywords

PROTEIN;

BIOLOGY; DISEASES; DRUG DEVELOPMENT; DRUG REPOSITIONING; FACTUAL DATABASE; GENETICS; HUMAN; METABOLISM; MOLECULAR EPIDEMIOLOGY; PROCEDURES;

COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DISEASE; DRUG DISCOVERY; DRUG REPOSITIONING; HUMANS; MOLECULAR EPIDEMIOLOGY; PROTEINS;

EID: 85019574450     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbw136     Document Type: Article

References (122)

1. Lyman GH, Moses HL. Biomarker tests for molecularly targeted therapies?the key to unlocking precision medicine. N Engl J Med 2016;375:4?6.
2. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;372:793?5.
3. Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med 2012;366:489?91.
4. Xie L, Draizen EJ, Bourne PE. Harnessing big data for systems pharmacology. Annu Rev Pharmacol Toxicol 2016.
5. Shameer K, Readhead B, Dudley JT. Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. Curr Top Med Chem 2015;15:5?20.
6. Dudley JT, Deshpande T, Butte AJ. Exploiting drug disease relationships for computational drug repositioning. Brief Bioinform 2011;12:303?11.
7. Readhead B, Dudley J. Translational bioinformatics approaches to drug development. Adv Wound Care (New Rochelle) 2013;2:470?89.
8. Suthram S, Dudley JT, Chiang AP, et al. Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 2010;6:e1000662.
9. Sirota M, Dudley JT, Kim J, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 2011;3:96ra77.
10. Jahchan NS, Dudley JT, Mazur PK, et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors, Cancer. Discov 2013;3:1364?77.
11. Lamb J, Crawford ED, Peck D, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006;313:1929?35.
12. Kidd BA, Readhead BP, Eden C, et al. Integrative network modeling approaches to personalized cancer medicine. Personalized Med 2015;12:245?57.
13. Blackwell AD. Measuring cognitive effects: cognition in drug development and repositioning. Drug Discov Today 2015;20:391?2.
14. Power A, Berger AC, Ginsburg GS. Genomics-enabled drug repositioning and repurposing: insights from an IOM Roundtable activity. JAMA 2014;311:2063?4.
15. Wang W, Yang S, Zhang X, et al. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 2014;30:2923?30.
16. Xu M, Lee EM, Wen Z, et al. Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen. Nat Med 2016;22:1101?7.
17. Lau QY, Tan YY, Goh VC, et al. An FDA-Drug library screen for compounds with bioactivities against Meticillin- Resistant Staphylococcus aureus (MRSA). Antibiotics (Basel) 2015;4:424?34.
18. Irie H, Banno K, Yanokura M, et al. Metformin: a candidate for the treatment of gynecological tumors based on drug repositioning. Oncol Lett 2016;11:1287?93.
19. Amelio I, Gostev M, Knight RA, et al. DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information. Cell Death Dis 2014;5:e1051.
20. von Eichborn J, Murgueitio MS, Dunkel M, et al. PROMISCUOUS: a database for network-based drug repositioning. Nucleic Acids Res 2011;39:D1060?6.
21. Huang H, Nguyen T, Ibrahim S, et al. DMAP: a connectivity map database to enable identification of novel drug repositioning candidates. BMC Bioinformatics 2015;16 (Suppl 13):S4.
22. Keiser MJ, Irwin JJ, Shoichet BK. The chemical basis of pharmacology. Biochemistry 2010;49:10267?76.
23. Chan SY, Loscalzo J. The emerging paradigm of network medicine in the study of human disease. Circ Res 2012;111:359?74.
24. Li Y, Agarwal P, Rajagopalan D. A global pathway crosstalk network. Bioinformatics 2008;24:1442?7.
25. Natarajan M, Lin KM, Hsueh RC, et al. A global analysis of cross-talk in a mammalian cellular signalling network. Nat Cell Biol 2006;8:571?80.
26. Hu Y, Bajorath J. Monitoring drug promiscuity over time. F1000Res 2014;3:218.
27. Tarcsay A, Keseru GM. Contributions of molecular properties to drug promiscuity. J Med Chem 2013;56:1789?95.
28. Basu MK, Carmel L, Rogozin IB, et al. Evolution of protein domain promiscuity in eukaryotes. Genome Res 2008;18:449?61.
29. Law V, Knox C, Djoumbou Y, et al. DrugBank 4. 0: shedding new light on drug metabolism. Nucleic Acids Res 2014;42:D1091?1097.
30. Xu K, Cote TR. Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases. Brief Bioinform 2011;12:341?5.
31. O?Boyle NM, Morley C, Hutchison GR. Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit. Chem Cent J 2008;2:5.
32. Wegner J, JOELib, JOELib2: http://www-ra. informatik. uni-tue bingen. de/software/joelib/index. html 2005.
33. Steinbeck C, Han Y, Kuhn S, et al. The chemistry development kit (CDK): an open-source Java library for Chemo- and Bioinformatics. J Chem Inf Comput Sci 2003;43:493?500.
34. Guha R, Howard MT, Hutchison GR, et al. The blue obeliskinteroperability in chemical informatics. J Chem Inf Model 2006;46:991?8.
35. Cao Y, Charisi A, Cheng LC, et al. ChemmineR: a compound mining framework for R. Bioinformatics 2008;24:1733?4.
36. O?Boyle NM, Banck M, James CA, et al. Open Babel: An open chemical toolbox. J Cheminform 2011; 3:33.
37. Tetko IV, Gasteiger J, Todeschini R, et al. Virtual computational chemistry laboratory?design and description. J Comput Aided Mol Des 2005;19:453?63.
38. van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2003;2:192?204.
39. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA2005;102:15545?50.
40. Xia J, Wishart DS. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 2010;38:W71?7.
41. Varin T, Gubler H, Parker CN, et al. Compound set enrichment: a novel approach to analysis of primary HTS data. J Chem Inf Model 2010;50:2067?78.
42. Hastings J, de Matos P, Dekker A, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2013;41:D456?63.
43. Moreno P, Beisken S, Harsha B, et al. BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology. BMC Bioinformatics 2015;16:56.
44. UniProt C. UniProt: a hub for protein information. Nucleic Acids Res 2015;43:D204?12.
45. Finn RD, Bateman A, Clements J, et al. Pfam: the protein families database. Nucleic Acids Res 2014;42:D222?30.
46. Fox NK, Brenner SE, Chandonia JM. SCOPe: structural classification of proteins?extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Res 2014;42:D304?9.
47. Das S, Lee D, Sillitoe I, et al. Functional classification of CATH superfamilies: a domain-based approach for protein function annotation. Bioinformatics 2016;32:2889.
48. Croft D, Mundo AF, Haw R, et al. The Reactome pathway knowledgebase. Nucleic Acids Res 2014;42:D472?7.
49. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27?30.
50. Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 2013;14:128.
51. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44?57.
52. Kamburov A, Stelzl U, Lehrach H, et al. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res 2013;41:D793?800.
53. Glicksberg BS, Li L, Cheng WY, et al. An integrative pipeline for multi-modal discovery of disease relationships. Pac Symp Biocomput 2015;20:407?18.
54. Glicksberg BS, Li L, Badgeley MA, et al. Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. Bioinformatics 2016;32:i101?10.
55. Chute CG. Invited commentary: Observational research in the age of the electronic health record. Am J Epidemiol 2014;179:759?61.
56. Harispe S, Sanchez D, Ranwez S, et al. A framework for unifying ontology-based semantic similarity measures: a study in the biomedical domain. J Biomed Inform 2014;48:38?53.
57. Menche J, Sharma A, Kitsak M, et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 2015;347:1257601.
58. Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care 2006;29:725?31.
59. Arain FA, Ye Z, Bailey KR, et al. Survival in patients with poorly compressible leg arteries. J AmCollCardiol 2012;59:400?7.
60. Lang CC, Mancini DM. Non-cardiac comorbidities in chronic heart failure. Heart 2007;93:665?71.
61. Hidalgo CA, Blumm N, Barabasi AL, et al. A dynamic network approach for the study of human phenotypes. PLoS Comput Biol 2009;5:e1000353.
62. Robbins AS, Chao SY, Fonseca VP. What?s the relative risk? A method to directly estimate risk ratios in cohort studies of common outcomes. Ann Epidemiol 2002;12:452?4.
63. Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet 2015;47:856?60.
64. Li L, Ruau DJ, Patel CJ, et al. Disease risk factors identified through shared genetic architecture and electronic medical records. Sci Transl Med 2014;6:234ra257.
65. Li L, Ruau D, Chen R, et al. Systematic identification of risk factors for Alzheimer?s disease through shared genetic architecture and electronic medical records. Pac Symp Biocomput 2013;224?35.
66. Amberger JS, Bocchini CA, Schiettecatte F, et al. OMIM. org: Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res 2015;43:D789?798.
67. Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014;42:D1001?6.
68. Li MJ, Liu Z, Wang P, et al. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res 2016;44:D869?876.
69. Li MJ, Wang P, Liu X, et al. GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res 2012;40:D1047?54.
70. Yu W, Gwinn M, Clyne M, et al. A navigator for human genome epidemiology. Nat Genet 2008;40:124?5.
71. Neves M, Leser U. A survey on annotation tools for the biomedical literature. Brief Bioinform 2014;15:327?40.
72. Hahn U, Cohen KB, Garten Y, et al. Mining the pharmacogenomics literature?a survey of the state of the art. Brief Bioinform 2012;13:460?94.
73. Harispe S, Ranwez S, Janaqi S, et al. The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics 2014;30:740?2.
74. Food-drug interactions could lower required dose of anticancer drug. Expert Rev Pharmacoecon Outcomes Res 2007;7:315?7.
75. McCabe BJ. Prevention of food-drug interactions with special emphasis on older adults. Curr Opin Clin Nutr Metab Care 2004;7:21?6.
76. Juurlink DN, Mamdani M, Kopp A, et al. Drug drug interactions among elderly patients hospitalized for drug toxicity. Jama 2003;289:1652?8.
77. Vilar S, Uriarte E, Santana L, et al. Similarity-based modeling in large-scale prediction of drug drug interactions. Nat Protoc 2014;9:2147?63.
78. Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010;38:W214?20.
79. Montojo J, Zuberi K, Rodriguez H, et al. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics 2010;26:2927?8.
80. Cao Y, Jiang T, Girke T. A maximum common substructurebased algorithm for searching and predicting drug like compounds. Bioinformatics 2008;24:i366?374.
81. Chen X, Reynolds CH. Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. J Chem Inf Comput Sci 2002;42:1407?14.
82. Loging W, Harland L, Williams-Jones B. High-throughput electronic biology: mining information for drug discovery. Nat Rev Drug Discov 2007;6:220?30.
83. Bredel M, Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 2004;5:262?75.
84. Keiser MJ, Roth BL, Armbruster BN, et al. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 2007;25:197?206.
85. Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature 2009;462:175?81.
86. Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46:3?26.
87. Hann MM, Keseru GM. Finding the sweet spot: the role of nature and nurture in medicinal chemistry. Nat Rev Drug Discov 2012;11:355?65.
88. Waring MJ, Arrowsmith J, Leach AR, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat Rev Drug Discov 2015;14:475?86.
89. Shameer K, Sowdhamini R. Functional repertoire, molecular pathways and diseases associated with 3D domain swapping in the human proteome. J Clin Bioinforma 2012;2:8.
90. Mendez-Lucio O, Tran J, Medina-Franco JL, et al. Toward drug repurposing in epigenetics: olsalazine as a hypomethylating compound active in a cellular context. ChemMedChem 2014;9:560?5.
91. Besnard J, Ruda GF, Setola V, et al. Automated design of ligands to polypharmacological profiles. Nature 2012;492:215?20.
92. Lappano R, Maggiolini M. G protein-coupled receptors: novel targets for drug discovery in cancer. Nat Rev Drug Discov 2011;10:47?60.
93. Moore JT, Collins JL, Pearce KH. The nuclear receptor superfamily and drug discovery. ChemMedChem 2006;1:504?23.
94. May AC, Johnson MS, Rufino SD, et al. The recognition of protein structure and function from sequence: adding value to genome data. Philos Trans R Soc Lond B Biol Sci 1994;344:373?81.
95. Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov 2006;5:993?6.
96. Edwards DP. The role of coactivators and corepressors in the biology and mechanism of action of steroid hormone receptors. J Mammary Gland Biol Neoplasia 2000;5:307?24.
97. Ahmadian M, Suh JM, Hah N, et al. PPARgamma signaling and metabolism: the good, the bad and the future. Nat Med 2013;19:557?66.
98. Kersten S, Desvergne B, Wahli W. Roles of PPARs in health and disease. Nature 2000;405:421?4.
99. Arnott ID, Watts D, Satsangi J. Azathioprine and anti- TNF alpha therapies in Crohn?s disease: a review of pharmacology, clinical efficacy and safety. Pharmacol Res 2003;47:1?10.
100. Gronemeyer H, Gustafsson JA, Laudet V. Principles for modulation of the nuclear receptor superfamily. Nat Rev Drug Discov 2004;3:950?64.
101. Ji WR, Castellino FJ, Chang Y, et al. Characterization of kringle domains of angiostatin as antagonists of endothelial cell migration, an important process in angiogenesis. FASEB J 1998;12:1731?8.
102. Pan Y, Cheng T, Wang Y, et al. Pathway analysis for drug repositioning based on public database mining. J Chem Inf Model 2014;54:407?18.
103. Jewison T, Su Y, Disfany FM, et al. SMPDB 2. 0: big improvements to the small molecule pathway database. Nucleic Acids Res 2014;42:D478?484.
104. Kelder T, van Iersel MP, Hanspers K, et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res 2012;40:D1301?1307.
105. Schaefer CF, Anthony K, Krupa S, et al. PID: the pathway interaction database. Nucleic Acids Res 2009;37:D674?9.
106. Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 2012;92:414?7.
107. Romero P, Wagg J, Green ML, et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 2005;6:R2.
108. Liu X, Zhu F, Ma XH, et al. Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. Curr Med Chem 2013;20:1646?61.
109. Sanseau P, Agarwal P, Barnes MR, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol 2012;30:317?20.
110. Nelson MR, Johnson T, Warren L, et al. The genetics of drug efficacy: opportunities and challenges. Nat Rev Genet 2016;17:197?206.
111. Rastegar-Mojarad M, Ye Z, Kolesar JM, et al. Opportunities for drug repositioning from phenome-wide association studies. Nat Biotechnol 2015;33:342?5.
112. Vogt I, Prinz J, Campillos M. Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug disease pairs. Genome Med 2014;6:52.
113. Piccirillo JF, Vlahiotis A, Barrett LB, et al. The changing prevalence of comorbidity across the age spectrum. Crit Rev Oncol Hematol 2008;67:124?32.
114. Starfield B, Lemke KW, Bernhardt T, et al. Comorbidity: implications for the importance of primary care in 0case0 management. Ann Fam Med 2003;1:8?14.
115. van Weel C, Schellevis FG. Comorbidity and guidelines: conflicting interests. Lancet 2006;367:550?1.
116. Hammer B, Ashurst P, Naish J. Diseases associated with ulcerative colitis and Crohn?s disease. Gut 1968;9:17?21.
117. Barrett JC, Hansoul S, Nicolae DL, et al. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn?s disease. Nat Genet 2008;40:955?62.
118. Kesselheim AS, Tan YT, Avorn J. The roles of academia, rare diseases, and repurposing in the development of the most transformative drugs. Health Aff 2015;34:286?93.
119. Murteira S, Ghezaiel Z, Karray S, Lamure M. Drug reformulations and repositioning in pharmaceutical industry and its impact on market access: reassessment of nomenclature. J Mark Access Health Policy 2013;1:21131.
120. Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015;43:D447?52.
121. Niedelman ML. Uses and abuses of drugs, new and old, in dermatology. Pa Med J 1954;57:333?8.
122. Hodos RA, Kidd BA, Shameer K, et al. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med 2016;