1932

Abstract

The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.

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2020-01-06
2024-12-11
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Literature Cited

  1. 1. 
    Kaitin KI. 2012. Translational research and the evolving landscape for biomedical innovation. J. Investig. Med. 60:7995–98
    [Google Scholar]
  2. 2. 
    Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S et al. 2018. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 18:141–58
    [Google Scholar]
  3. 3. 
    DiMasi JA, Feldman L, Seckler A, Wilson A 2010. Trends in risks associated with new drug development: success rates for investigational drugs. Clin. Pharmacol. Ther. 87:3272–77
    [Google Scholar]
  4. 4. 
    Collins FS. 2016. Seeking a cure for one of the rarest diseases: progeria. Circulation 134:2126–29
    [Google Scholar]
  5. 5. 
    Ashburn TT, Thor KB. 2004. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3:8673–83
    [Google Scholar]
  6. 6. 
    Mullard A. 2018. Low-cost non-profit drug repurposing. Nat. Rev. Drug Discov. 18:17
    [Google Scholar]
  7. 7. 
    Ghofrani HA, Osterloh IH, Grimminger F 2006. Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat. Rev. Drug Discov. 5:8689–702
    [Google Scholar]
  8. 8. 
    Vargesson N. 2015. Thalidomide‐induced teratogenesis: history and mechanisms. Birth Defects Res. C Embryo Today 105:2140–56
    [Google Scholar]
  9. 9. 
    Millrine D, Kishimoto T. 2017. A brighter side to thalidomide: its potential use in immunological disorders. Trends Mol. Med. 23:4348–61
    [Google Scholar]
  10. 10. 
    Horgan RP, Kenny LC. 2011. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet. Gynaecol. 13:3189–95
    [Google Scholar]
  11. 11. 
    Talevi A. 2018. Drug repositioning: current approaches and their implications in the precision medicine era. Expert Rev. Precis. Med. Drug Dev. 3:149–61
    [Google Scholar]
  12. 12. 
    Plenge RM, Scolnick EM, Altshuler D 2013. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12:8581–94
    [Google Scholar]
  13. 13. 
    Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V et al. 2016. The genetic architecture of type 2 diabetes. Nature 536:761441–47
    [Google Scholar]
  14. 14. 
    Rusu V, Hoch E, Mercader JM, Tenen DE, Gymrek M et al. 2017. Type 2 diabetes variants disrupt function of SLC16A11 through two distinct mechanisms. Cell 170:1199–212.e20
    [Google Scholar]
  15. 15. 
    Kim K, Bang S-Y, Lee H-S, Bae S-C 2017. Update on the genetic architecture of rheumatoid arthritis. Nat. Rev. Rheumatol. 13:113–24
    [Google Scholar]
  16. 16. 
    Netw. Pathw. Anal. Subgr. Psychiatr. Genom. Consort 2015. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci 18:2199–209
    [Google Scholar]
  17. 17. 
    Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J et al. 2015. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat. Genet. 47:4373–80
    [Google Scholar]
  18. 18. 
    Al Olama AA, Kote-Jarai Z, Berndt SI, Conti DV, Schumacher F et al. 2014. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46:101103–9
    [Google Scholar]
  19. 19. 
    McKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R et al. 2017. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat. Genet. 49:71126–32
    [Google Scholar]
  20. 20. 
    Klein AP, Wolpin BM, Risch HA, Stolzenberg-Solomon RZ, Mocci E et al. 2018. Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer. Nat. Commun. 9:556
    [Google Scholar]
  21. 21. 
    Wang Z, Dai J, Hu N, Miao X, Abnet CC et al. 2017. Identification of new susceptibility loci for gastric non-cardia adenocarcinoma: pooled results from two Chinese genome-wide association studies. Gut 66:4581–87
    [Google Scholar]
  22. 22. 
    ENCODE Proj. Consort 2004. The ENCODE (ENCyclopedia Of DNA Elements) project. Science 306:5696636–40
    [Google Scholar]
  23. 23. 
    Corradin O, Saiakhova A, Akhtar-Zaidi B, Myeroff L, Willis J et al. 2014. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res 24:1–13
    [Google Scholar]
  24. 24. 
    He B, Chen C, Teng L, Tan K 2014. Global view of enhancer-promoter interactome in human cells. PNAS 111:21E2191–99
    [Google Scholar]
  25. 25. 
    Cotto KC, Wagner AH, Feng Y-Y, Kiwala S, Coffman AC et al. 2018. DGIdb 3.0: a redesign and expansion of the drug–gene interaction database. Nucleic Acids Res 46:D1D1068–73
    [Google Scholar]
  26. 26. 
    Okada Y, Wu D, Trynka G, Raj T, Terao C et al. 2014. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506:7488376–81
    [Google Scholar]
  27. 27. 
    Grover MP, Ballouz S, Mohanasundaram KA, George RA, Goscinski A et al. 2015. Novel therapeutics for coronary artery disease from genome-wide association study data. BMC Med. Genom. 8:Suppl. 2S1
    [Google Scholar]
  28. 28. 
    Kinnersley B, Sud A, Coker EA, Tym JE, Di Micco P et al. 2018. Leveraging human genetics to guide cancer drug development. JCO Clin. Cancer Informat. 2:1–11 Pharma Intell 2019.
    [Google Scholar]
  29. 30. 
    Franke A, McGovern DPB, Barrett JC, Wang K, Radford-Smith GL et al. 2010. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42:121118–25
    [Google Scholar]
  30. 31. 
    Khafipour A. 2017. The effect of Denosumab, the inhibitor for receptor activator of nuclear factor kappa-B ligand (RANKL), on dinitrobenzensulfonic acid (DNBS)-induced experimental model of Crohn's disease MA Thesis: Univ. Manitoba, Winnipeg, Manit.
    [Google Scholar]
  31. 32. 
    Bernstein C. 2014. The efficacy of denosumab in active Crohn's disease. ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT02321280
    [Google Scholar]
  32. 33. 
    Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L et al. 2010. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26:91205–10
    [Google Scholar]
  33. 34. 
    Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R et al. 2013. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31:121102–10
    [Google Scholar]
  34. 35. 
    Diogo D, Tian C, Franklin CS, Alanne-Kinnunen M, March M et al. 2018. Phenome-wide association studies across large population cohorts support drug target validation. Nat. Commun. 9:4285
    [Google Scholar]
  35. 36. 
    Jerome RN, Pulley JM, Roden DM, Shirey-Rice JK, Bastarache LA et al. 2018. Using human “experiments of nature” to predict drug safety issues: an example with PCSK9 inhibitors. Drug Saf 41:3303–11
    [Google Scholar]
  36. 37. 
    Pulley JM, Shirey-Rice JK, Lavieri RR, Jerome RN, Zaleski NM et al. 2017. Accelerating precision drug development and drug repurposing by leveraging human genetics. Assay Drug Dev. Technol. 15:113–19
    [Google Scholar]
  37. 38. 
    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50:91219–24
    [Google Scholar]
  38. 39. 
    Feinberg AP. 2018. The key role of epigenetics in human disease prevention and mitigation. N. Engl. J. Med. 378:141323–34
    [Google Scholar]
  39. 40. 
    Kaminskas E, Farrell AT, Wang Y-C, Sridhara R, Pazdur R 2005. FDA drug approval summary: azacitidine (5-azacytidine, Vidaza™) for injectable suspension. Oncologist 10:3176–82
    [Google Scholar]
  40. 41. 
    Flavahan WA, Gaskell E, Bernstein BE 2017. Epigenetic plasticity and the hallmarks of cancer. Science 357:6348eaal2380
    [Google Scholar]
  41. 42. 
    Mann BS, Johnson JR, Cohen MH, Justice R, Pazdur R 2007. FDA approval summary: vorinostat for treatment of advanced primary cutaneous T-cell lymphoma. Oncologist 12:101247–52
    [Google Scholar]
  42. 43. 
    Raedler LA. 2016. Farydak (Panobinostat): first HDAC inhibitor approved for patients with relapsed multiple myeloma. Am. Health Drug Benefits 9:Spec. Featur.84–87
    [Google Scholar]
  43. 44. 
    Xu Y, Vakoc CR. 2017. Targeting cancer cells with BET bromodomain inhibitors. Cold Spring Harb. Perspect. Med. 7:7a026674
    [Google Scholar]
  44. 45. 
    Subramaniam D, Thombre R, Dhar A, Anant S 2014. DNA methyltransferases: a novel target for prevention and therapy. Front. Oncol. 4:80
    [Google Scholar]
  45. 46. 
    Wei H, Qin ZH, Senatorov VV, Wei W, Wang Y et al. 2001. Lithium suppresses excitotoxicity-induced striatal lesions in a rat model of Huntington's disease. Neuroscience 106:3603–12
    [Google Scholar]
  46. 47. 
    Noble W, Planel E, Zehr C, Olm V, Meyerson J et al. 2005. Inhibition of glycogen synthase kinase-3 by lithium correlates with reduced tauopathy and degeneration in vivo. PNAS 102:196990–95
    [Google Scholar]
  47. 48. 
    US Food Drug Admin 2015. Valproate Information Postmarket Drug Saf. Inf., US Food Drug Admin Silver Spring, MD: https://www.fda.gov/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/ucm192645.htm
    [Google Scholar]
  48. 49. 
    Wu S, Zheng S-D, Huang H-L, Yan L-C, Yin X-F et al. 2013. Lithium down-regulates histone deacetylase 1 (HDAC1) and induces degradation of mutant huntingtin. J. Biol. Chem. 288:4935500–10
    [Google Scholar]
  49. 50. 
    Graça I, Sousa EJ, Costa-Pinheiro P, Vieira FQ, Torres-Ferreira J et al. 2014. Anti-neoplastic properties of hydralazine in prostate cancer. Oncotarget 5:155950–64
    [Google Scholar]
  50. 51. 
    Espinoza R, Rodríguez MA, Zapata NP, Torres JC, Nolasco DB et al. 2016. Phase II study of epigenetic therapy with hydralazine and valproate in cutaneous T-cell lymphoma. Blood 128:225362
    [Google Scholar]
  51. 52. 
    Cetina Pérez L. 2015. Evaluation of TRANSKRIP® plus chemotherapy in recurrent-persistent cervical cancer. ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT02446652
    [Google Scholar]
  52. 53. 
    Qi Y, Wang D, Wang D, Jin T, Yang L et al. 2016. HEDD: the human epigenetic drug database. Database 2016.baw159
    [Google Scholar]
  53. 54. 
    Méndez-Lucio O, Tran J, Medina-Franco JL, Meurice N, Muller M 2014. Toward drug repurposing in epigenetics: olsalazine as a hypomethylating compound active in a cellular context. Chem. Med. Chem. 9:3560–65
    [Google Scholar]
  54. 55. 
    Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S et al. 2008. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:Suppl. 1D901–6
    [Google Scholar]
  55. 56. 
    Knox C, Law V, Jewison T, Liu P, Ly S et al. 2011. DrugBank 3.0: a comprehensive resource for “omics” research on drugs. Nucleic Acids Res 39:Suppl. 1D1035–41
    [Google Scholar]
  56. 57. 
    Law V, Knox C, Djoumbou Y, Jewison T, Guo AC et al. 2014. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1D1091–97
    [Google Scholar]
  57. 58. 
    Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A et al. 2018. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1D1074–82
    [Google Scholar]
  58. 59. 
    Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J 2013. Transcriptional data: a new gateway to drug repositioning. ? Drug Discov. Today 18:7–8350–57
    [Google Scholar]
  59. 60. 
    Karatzas E, Kolios G, Spyrou GM 2019. An application of computational drug repurposing based on transcriptomic signatures. Methods Mol. Biol. 1903:149–77
    [Google Scholar]
  60. 61. 
    Zhao K, So H-C. 2019. Using drug expression profiles and machine learning approach for drug repurposing. Methods Mol. Biol. 1903:219–37
    [Google Scholar]
  61. 62. 
    Iwata M, Yamanishi Y. 2019. The use of large-scale chemically-induced transcriptome data acquired from LINCS to study small molecules. Methods Mol. Biol. 1888:189–203
    [Google Scholar]
  62. 63. 
    Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A 2016. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 13:72524–30
    [Google Scholar]
  63. 64. 
    Setoain J, Franch M, Martínez M, Tabas-Madrid D, Sorzano COS et al. 2015. NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning. Nucleic Acids Res 43:W1W193–99
    [Google Scholar]
  64. 65. 
    Lee BKB, Tiong KH, Chang JK, Liew CS, Abdul Rahman ZA et al. 2017. DeSigN: connecting gene expression with therapeutics for drug repurposing and development. BMC Genom 18:Suppl. 1934
    [Google Scholar]
  65. 66. 
    Iskar M, Zeller G, Blattmann P, Campillos M, Kuhn M et al. 2013. Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol. Syst. Biol. 9:662
    [Google Scholar]
  66. 67. 
    Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S et al. 2011. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3:9696ra76
    [Google Scholar]
  67. 68. 
    Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau W-C et al. 2005. NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res 33:Suppl. 1D562–66
    [Google Scholar]
  68. 69. 
    Crockett SD, Schectman R, Stürmer T, Kappelman MD 2014. Topiramate use does not reduce flares of inflammatory bowel disease. Dig. Dis. Sci. 59:71535–43
    [Google Scholar]
  69. 70. 
    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P et al. 2010. Discovery of drug mode of action and drug repositioning from transcriptional responses. PNAS 107:3314621–26
    [Google Scholar]
  70. 71. 
    Carrella D, Napolitano F, Rispoli R, Miglietta M, Carissimo A et al. 2014. Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis. Bioinformatics 30:121787–88
    [Google Scholar]
  71. 72. 
    Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ 2010. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLOS Genet 6:4e1000888
    [Google Scholar]
  72. 73. 
    Consort GTEx 2015. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 348:6235648–60
    [Google Scholar]
  73. 74. 
    Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K et al. 2015. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47:91091–98
    [Google Scholar]
  74. 75. 
    Rognan D. 2013. Proteome-scale docking: myth and reality. Drug Discov. Today Technol. 10:3e403–9
    [Google Scholar]
  75. 76. 
    Sawada R, Iwata H, Mizutani S, Yamanishi Y 2015. Target-based drug repositioning using large-scale chemical-protein interactome data. J. Chem. Inform. Model. 55:122717–30
    [Google Scholar]
  76. 77. 
    Huber KVM, Superti-Furga G. 2016. Profiling of small molecules by chemical proteomics. Proteomis in Systems Biology J Reinders 211–18 New York: Humana Press
    [Google Scholar]
  77. 78. 
    Wright MH, Sieber SA. 2016. Chemical proteomics approaches for identifying the cellular targets of natural products. Nat. Prod. Rep. 33:5681–708
    [Google Scholar]
  78. 79. 
    Cui T, Hou H, Sun Y, Cang H, Wang X 2017. Uncovering drug mechanism of action by proteome wide-identification of drug-binding proteins. Med. Chem. 13:6526–35
    [Google Scholar]
  79. 80. 
    Zhou H, Cao H, Skolnick J 2018. FINDSITEcomb2.0: a new approach for virtual ligand screening of proteins and virtual target screening of biomolecules. J. Chem. Inform. Model. 58:112343–54
    [Google Scholar]
  80. 81. 
    Somody JC, MacKinnon SS, Windemuth A 2017. Structural coverage of the proteome for pharmaceutical applications. Drug Discov. Today 22:121792–99
    [Google Scholar]
  81. 82. 
    Ozdemir ES, Halakou F, Nussinov R, Gursoy A, Keskin O 2019. Methods for discovering and targeting druggable protein-protein interfaces and their application to repurposing. Computational Methods for Drug Repurposing Q Vanhaelen 1–21 New York: Springer
    [Google Scholar]
  82. 83. 
    Cousins EM, Goldfarb D, Yan F, Roques J, Darr D et al. 2018. Competitive kinase enrichment proteomics reveals that abemaciclib inhibits GSK3β and activates WNT signaling. Mol. Cancer Res. 16:2333–44
    [Google Scholar]
  83. 84. 
    Klaeger S, Heinzlmeir S, Wilhelm M, Polzer H, Vick B et al. 2017. The target landscape of clinical kinase drugs. Science 358:6367eaan4368
    [Google Scholar]
  84. 85. 
    Mangione W, Samudrala R. 2018. Identifying protein subsets and features responsible for improved drug repurposing accuracies using the CANDO platform. bioRxiv 405837 https://doi.org/10.1101/405837
    [Crossref] [Google Scholar]
  85. 86. 
    Minie M, Chopra G, Sethi G, Horst J, White G et al. 2014. CANDO and the infinite drug discovery frontier. Drug Discov. Today 19:91353–63
    [Google Scholar]
  86. 87. 
    Mangione W, Samudrala R. 2019. Identifying protein features responsible for improved drug repurposing accuracies using the CANDO platform: implications for drug design. Molecules 24:1167
    [Google Scholar]
  87. 88. 
    Chopra G, Kaushik S, Elkin P, Samudrala R 2016. Combating Ebola with repurposed therapeutics using the CANDO platform. Molecules 21:121537
    [Google Scholar]
  88. 89. 
    Kouznetsova J, Sun W, Martínez-Romero C, Tawa G, Shinn P et al. 2014. Identification of 53 compounds that block Ebola virus-like particle entry via a repurposing screen of approved drugs. Emerg. Microbes Infect. 3:12e84
    [Google Scholar]
  89. 90. 
    Johansen LM, DeWald LE, Shoemaker CJ, Hoffstrom BG, Lear-Rooney CM et al. 2015. A screen of approved drugs and molecular probes identifies therapeutics with anti-Ebola virus activity. Sci. Transl. Med. 7:290290ra89
    [Google Scholar]
  90. 91. 
    Wilson JL, Racz R, Liu T, Adeniyi O, Sun J et al. 2018. PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development. PLOS Comput. Biol. 14:12e1006614
    [Google Scholar]
  91. 92. 
    Velez G, Bassuk AG, Colgan D, Tsang SH, Mahajan VB 2017. Therapeutic drug repositioning using personalized proteomics of liquid biopsies. JCI Insight 2:24e97818
    [Google Scholar]
  92. 93. 
    Velez G, Tang PH, Cabral T, Cho GY, Machlab DA et al. 2018. Personalized proteomics for precision health: identifying biomarkers of vitreoretinal disease. Transl. Vis. Sci. Technol. 7:512
    [Google Scholar]
  93. 94. 
    Velez G, Roybal CN, Colgan D, Tsang SH, Bassuk AG, Mahajan VB 2016. Precision medicine: personalized proteomics for the diagnosis and treatment of idiopathic inflammatory disease. JAMA Ophthalmol 134:4444–48
    [Google Scholar]
  94. 95. 
    Mahajan VB, Skeie JM. 2014. Translational vitreous proteomics. Proteom. Clin. Appl. 8:3–4204–8
    [Google Scholar]
  95. 96. 
    Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K et al. 2018. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46:D1D608–17
    [Google Scholar]
  96. 97. 
    Kanehisa M, Goto S. 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:127–30
    [Google Scholar]
  97. 98. 
    UniProt Consort 2017. UniProt: the universal protein knowledgebase. Nucleic Acids Res 45:D1D158–69
    [Google Scholar]
  98. 99. 
    Zhang M, Schmitt-Ulms G, Sato C, Xi Z, Zhang Y et al. 2016. Drug repositioning for Alzheimer's disease based on systematic ‘omics’ data mining. PLOS ONE 11:12e0168812
    [Google Scholar]
  99. 100. 
    Zhang M, Luo H, Xi Z, Rogaeva E 2015. Drug repositioning for diabetes based on “omics” data mining. PLOS ONE 10:5e0126082
    [Google Scholar]
  100. 101. 
    Kobayashi Y, Kashima H, Rahmanto YS, Banno K, Yu Y et al. 2017. Drug repositioning of mevalonate pathway inhibitors as antitumor agents for ovarian cancer. Oncotarget 8:4272147–56
    [Google Scholar]
  101. 102. 
    Liu X, Romero IL, Litchfield LM, Lengyel E, Locasale JW 2016. Metformin targets central carbon metabolism and reveals mitochondrial requirements in human cancers. Cell Metab 24:5728–39
    [Google Scholar]
  102. 103. 
    Spanogiannopoulos P, Bess EN, Carmody RN, Turnbaugh PJ 2016. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14:5273–87
    [Google Scholar]
  103. 104. 
    Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A et al. 2018. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555:7698623–28
    [Google Scholar]
  104. 105. 
    Ooijevaar RE, Terveer EM, Verspaget HW, Kuijper EJ, Keller JJ 2019. Clinical application and potential of fecal microbiota transplantation. Annu. Rev. Med. 70:335–51
    [Google Scholar]
  105. 106. 
    Lee P, Yacyshyn BR, Yacyshyn MB 2019. Gut microbiota and obesity: an opportunity to alter obesity through faecal microbiota transplant (FMT). Diabetes Obes Metab 21:3479–90
    [Google Scholar]
  106. 107. 
    Zhu W, Winter MG, Byndloss MX, Spiga L, Duerkop BA et al. 2018. Precision editing of the gut microbiota ameliorates colitis. Nature 553:7687208–11
    [Google Scholar]
  107. 108. 
    Moreira GV, Azevedo FF, Ribeiro LM, Santos A, Guadagnini D et al. 2018. Liraglutide modulates gut microbiota and reduces NAFLD in obese mice. J. Nutr. Biochem. 62:143–54
    [Google Scholar]
  108. 109. 
    Iqbal J, Yuen T, Sun L, Zaidi M 2016. From the gut to the strut: where inflammation reigns, bone abstains. J. Clin. Investig. 126:62045–48
    [Google Scholar]
  109. 110. 
    Kaiser J. 2017. Your gut bacteria could determine how you respond to cutting-edge cancer drugs. Science Mag Novemb. 2. https://www.sciencemag.org/news/2017/11/your-gut-bacteria-could-determine-how-you-respond-cutting-edge-cancer-drugs
    [Google Scholar]
  110. 111. 
    Taroncher-Oldenburg G, Jones S, Blaser M, Bonneau R, Christey P et al. 2018. Translating microbiome futures. Nat. Biotechnol. 36:1037–42
    [Google Scholar]
  111. 112. 
    Drews J. 2000. Drug discovery: a historical perspective. Science 287:54601960–64
    [Google Scholar]
  112. 113. 
    Cook D, Brown D, Alexander R, March R, Morgan P et al. 2014. Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13:6419–31
    [Google Scholar]
  113. 114. 
    Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z 2016. A survey of current trends in computational drug repositioning. Brief. Bioinform. 17:12–12
    [Google Scholar]
  114. 115. 
    Pulley J, Clayton E, Bernard GR, Roden DM, Masys DR 2010. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin. Transl. Sci. 3:142–48
    [Google Scholar]
  115. 116. 
    McCullough AR. 2002. Four-year review of sildenafil citrate. Rev Urol 4:Suppl. 3S26–38
    [Google Scholar]
  116. 117. 
    Rastegar-Mojarad M, Ye Z, Kolesar JM, Hebbring SJ, Lin SM 2015. Opportunities for drug repositioning from phenome-wide association studies. Nat. Biotechnol. 33:4342–45
    [Google Scholar]
  117. 118. 
    Millwood IY, Bennett DA, Walters RG, Clarke R, Waterworth D et al. 2016. Lipoprotein-associated phospholipase A2 loss-of-function variant and risk of vascular diseases in 90,000 Chinese adults. J. Am. Coll. Cardiol. 67:2230–31
    [Google Scholar]
  118. 119. 
    Duarte JH. 2014. Acute coronary syndromes: Risk of major coronary events not reduced by darapladib therapy. Nat. Rev. Cardiol. 11:11621
    [Google Scholar]
  119. 120. 
    Blehar MC, Spong C, Grady C, Goldkind SF, Sahin L, Clayton JA 2013. Enrolling pregnant women: issues in clinical research. Womens Health Issues 23:1e39–45
    [Google Scholar]
  120. 121. 
    Schachter AD, Kohane IS. 2011. Drug target-gene signatures that predict teratogenicity are enriched for developmentally related genes. Reprod. Toxicol. 31:4562–69
    [Google Scholar]
  121. 122. 
    Goldstein JA, Bastarache LA, Denny JC, Roden DM, Pulley JM, Aronoff DM 2018. Calcium channel blockers as drug repurposing candidates for gestational diabetes: mining large scale genomic and electronic health records data to repurpose medications. Pharmacol. Res. 130:44–51
    [Google Scholar]
  122. 123. 
    Mesci P, Macia A, Moore SM, Shiryaev SA, Pinto A et al. 2018. Blocking Zika virus vertical transmission. Sci. Rep. 8:11218
    [Google Scholar]
  123. 124. 
    Ziegelstein R. 2017. Personomics: the missing link in the evolution from precision medicine to personalized medicine. J. Pers. Med. 7:411
    [Google Scholar]
  124. 125. 
    Duran-Frigola M, Aloy P. 2012. Recycling side-effects into clinical markers for drug repositioning. Genome Med 4:13
    [Google Scholar]
  125. 126. 
    Xu H, Aldrich MC, Chen Q, Liu H, Peterson NB et al. 2015. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J. Am. Med. Inform. Assoc. 22:1179–91
    [Google Scholar]
  126. 127. 
    Su EW, Sanger TM. 2017. Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov. PeerJ 5:e3154
    [Google Scholar]
  127. 128. 
    Gonzalez-Hernandez G, Sarker A, O'Connor K, Savova G 2017. Capturing the patient's perspective: a review of advances in natural language processing of health-related text. Yearb. Med. Inform. 26:01214–27
    [Google Scholar]
  128. 129. 
    Cocos A, Fiks AG, Masino AJ 2017. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. J. Am. Med. Informat. Assoc. 24:4813–21
    [Google Scholar]
  129. 130. 
    Tutubalina E, Miftahutdinov Z, Nikolenko S, Malykh V 2018. Medical concept normalization in social media posts with recurrent neural networks. J. Biomed. Informat. 84:93–102
    [Google Scholar]
  130. 131. 
    Ru B, Warner-Hillard C, Ge Y, Yao L 2017. Identifying serendipitous drug usages in patient forum data: a feasibility study. Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017)106–18 Setúbal, Port.: SCITEPRESS
    [Google Scholar]
  131. 132. 
    Rastegar-Mojarad M, Liu H, Nambisan P 2016. Using social media data to identify potential candidates for drug repurposing: a feasibility study. JMIR Res. Protoc. 5:2e121
    [Google Scholar]
  132. 133. 
    Paavola A. 2018. Amazon moves into healthcare: a 2018 timeline. Becker's Health IT & CIO Rep Dec. 20. https://www.beckershospitalreview.com/healthcare-information-technology/amazon-moves-into-healthcare-a-2018-timeline.html
    [Google Scholar]
  133. 134. 
    Allarakhia M. 2015. Engaging patients for drug repurposing: mapping the patient engagement continuum. Clin. Investig. 5:9733–37
    [Google Scholar]
  134. 135. 
    Transparency Life Sciences LLC 2012. FDA clears IND for first clinical trial protocol developed using crowdsourcing. PR Newswire Dec. 18. https://www.prnewswire.com/news-releases/fda-clears-ind-for-first-clinical-trial-protocol-developed-using-crowdsourcing-183922651.html
    [Google Scholar]
  135. 136. 
    Leiter A, Sablinski T, Diefenbach M, Foster M, Greenberg A et al. 2014. Use of crowdsourcing for cancer clinical trial development. J. Natl. Cancer Inst. 106:10dju258
    [Google Scholar]
  136. 137. 
    Calvert M, Kyte D, Mercieca-Bebber R, Slade A, Chan A-W et al. 2018. Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO extension. JAMA 319:5483–94
    [Google Scholar]
  137. 138. 
    Cao C, Moult J. 2014. GWAS and drug targets. BMC Genom 15:Suppl. 4S5
    [Google Scholar]
  138. 139. 
    Hebbring SJ. 2014. The challenges, advantages and future of phenome-wide association studies. Immunology 141:2157–65
    [Google Scholar]
  139. 140. 
    Hernandez JJ, Pryszlak M, Smith L, Yanchus C, Kurji N et al. 2017. Giving drugs a second chance: overcoming regulatory and financial hurdles in repurposing approved drugs as cancer therapeutics. Front. Oncol. 7:273
    [Google Scholar]
  140. 141. 
    Cheng Y-S, Sun W, Xu M, Shen M, Khraiwesh M et al. 2018. Repurposing screen identifies unconventional drugs with activity against multidrug resistant Acinetobacter baumannii. Front. Cell Infect. . Microbiol 8:438
    [Google Scholar]
  141. 142. 
    Soo VWC, Kwan BW, Quezada H, Castillo-Juárez I, Pérez-Eretza B et al. 2017. Repurposing of anticancer drugs for the treatment of bacterial infections. Curr. Top. Med. Chem. 17:101157–76
    [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-010919-023537
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