Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Drug repurposing: progress, challenges and recommendations


Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Approaches used in drug repurposing.


  1. 1

    Ashburn, T. T. & Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673–683 (2004).

    CAS  Article  Google Scholar 

  2. 2

    Scannell, J. W., Blanckley, A., Boldon, H. & Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 (2012).

    CAS  PubMed  Google Scholar 

  3. 3

    Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 428–438 (2011).

    CAS  PubMed  Google Scholar 

  4. 4

    Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475–486 (2015).

    CAS  PubMed  Google Scholar 

  5. 5

    Roundtable on Translating Genomic-Based Research for Health (Board on Health Sciences Policy) Institute of Medicine in Drug Repurposing and Repositioning: Workshop Summary (eds. Johnson, S. G., Beachy, S. H., Olson, S., Berger, A. C.) (National Academies Press, Washington DC, 2014).

  6. 6

    Breckenridge, A. & Jacob, R. Overcoming the legal and regulatory barriers to drug repurposing. Nat. Rev. Drug Discov. (2018).

    PubMed  Google Scholar 

  7. 7

    Nosengo, N. Can you teach old drugs new tricks? Nature 534, 314–316 (2016).

    PubMed  Google Scholar 

  8. 8

    Phillips, D. J. Pfizer's expiring Viagra patent adversely affects other drugmakers too. Forbes (2013).

  9. 9

    Singhal, S. et al. Antitumor activity of thalidomide in refractory multiple myeloma. N. Engl. J. Med. 341, 1565–1571 (1999).

    CAS  PubMed  Google Scholar 

  10. 10

    Urquhart, L. Market watch: top drugs and companies by sales in 2017. Nat. Rev. Drug Discov. 17, 232 (2018).

    CAS  PubMed  Google Scholar 

  11. 11

    Hurle, M. R. et al. Computational drug repositioning: from data to therapeutics. Clin. Pharmacol. Ther. 93, 335–341 (2013).

    CAS  PubMed  Google Scholar 

  12. 12

    Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Hieronymus, H. et al. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators. Cancer Cell 10, 321–330 (2006).

    CAS  PubMed  Google Scholar 

  14. 14

    Dudley, J. T., Deshpande, T. & Butte, A. J. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform. 12, 303–311 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Iorio, F., Rittman, T., Ge, H., Menden, M. & Saez-Rodriguez, J. Transcriptional data: a new gateway to drug repositioning? Drug Discov. Today 18, 350–357 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl Med. 3, 96ra76 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Sirota, M. et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl Med. 3, 96ra77 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Wagner, A. et al. Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia. Mol. Syst. Biol. 11, 791 (2015).

    PubMed  Google Scholar 

  19. 19

    Hsieh, Y. Y., Chou, C. J., Lo, H. L. & Yang, P. M. Repositioning of a cyclin-dependent kinase inhibitor GW8510 as a ribonucleotide reductase M2 inhibitor to treat human colorectal cancer. Cell Death Discov. 2, 16027 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Huang, C. H. et al. Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells. PeerJ 4, e2478 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. 21

    Kunkel, S. D. et al. mRNA expression signatures of human skeletal muscle atrophy identify a natural compound that increases muscle mass. Cell Metab. 13, 627–638 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Malcomson, B. et al. Connectivity mapping (ssCMap) to predict A20-inducing drugs and their antiinflammatory action in cystic fibrosis. Proc. Natl Acad. Sci. USA 113, E3725–E3734 (2016).

    CAS  PubMed  Google Scholar 

  23. 23

    Mirza, N., Sills, G. J., Pirmohamed, M. & Marson, A. G. Identifying new antiepileptic drugs through genomics-based drug repurposing. Hum. Mol. Genet. 26, 527–537 (2017).

    CAS  PubMed  Google Scholar 

  24. 24

    Shin, E., Lee, Y. C., Kim, S. R., Kim, S. H. & Park, J. Drug signature-based finding of additional clinical use of LC28-0126 for neutrophilic bronchial asthma. Sci. Rep. 5, 17784 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Wei, G. et al. Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell 10, 331–342 (2006).

    CAS  PubMed  Google Scholar 

  26. 26

    Chiang, A. P. & Butte, A. J. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86, 507–510 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Iorio, F., Isacchi, A., di Bernardo, D. & Brunetti-Pierri, N. Identification of small molecules enhancing autophagic function from drug network analysis. Autophagy 6, 1204–1205 (2010).

    CAS  PubMed  Google Scholar 

  28. 28

    Hegde, R. N. et al. Unravelling druggable signalling networks that control F508del-CFTR proteostasis. eLife 4, e10365 (2015).

    PubMed  PubMed Central  Google Scholar 

  29. 29

    Iorio, F. et al. A semi-supervised approach for refining transcriptional signatures of drug response and repositioning predictions. PLOS ONE 10, e0139446 (2015).

    PubMed  PubMed Central  Google Scholar 

  30. 30

    Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  Google Scholar 

  31. 31

    Wang, Z. et al. Extraction and analysis of signatures from the gene expression omnibus by the crowd. Nat. Commun. 7, 12846 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Pacini, C. et al. DvD: an R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data. Bioinformatics 29, 132–134 (2013).

    CAS  PubMed  Google Scholar 

  33. 33

    Zhang, S. D. & Gant, T. W. sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics 10, 236 (2009).

    PubMed  PubMed Central  Google Scholar 

  34. 34

    Oprea, T. I., Tropsha, A., Faulon, J. L. & Rintoul, M. D. Systems chemical biology. Nat. Chem. Biol. 3, 447–450 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263–266 (2008).

    CAS  PubMed  Google Scholar 

  36. 36

    Yang, L. & Agarwal, P. Systematic drug repositioning based on clinical side-effects. PLOS ONE 6, e28025 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Kitchen, D. B., Decornez, H., Furr, J. R. & Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935–949 (2004).

    CAS  PubMed  Google Scholar 

  38. 38

    Dakshanamurthy, S. et al. Predicting new indications for approved drugs using a proteochemometric method. J. Med. Chem. 55, 6832–6848 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Cooke, R. M., Brown, A. J., Marshall, F. H. & Mason, J. S. Structures of G protein-coupled receptors reveal new opportunities for drug discovery. Drug Discov. Today 20, 1355–1364 (2015).

    CAS  PubMed  Google Scholar 

  40. 40

    Kharkar, P. S., Warrier, S. & Gaud, R. S. Reverse docking: a powerful tool for drug repositioning and drug rescue. Future Med. Chem. 6, 333–342 (2014).

    CAS  PubMed  Google Scholar 

  41. 41

    Huang, H. et al. Reverse screening methods to search for the protein targets of chemopreventive compounds. Front. Chem. 6, 138 (2018).

    PubMed  PubMed Central  Google Scholar 

  42. 42

    Warren, G. L. et al. A critical assessment of docking programs and scoring functions. J. Med. Chem. 49, 5912–5931 (2006).

    CAS  PubMed  Google Scholar 

  43. 43

    Pagadala, N. S., Syed, K. & Tuszynski, J. Software for molecular docking: a review. Biophys. Rev. 9, 91–102 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Sanseau, P. et al. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30, 317–320 (2012).

    CAS  PubMed  Google Scholar 

  45. 45

    Grover, M. P. et al. Novel therapeutics for coronary artery disease from genome-wide association study data. BMC Med. Genom. 8 (Suppl. 2), S1 (2015).

    Google Scholar 

  46. 46

    Wang, Z. Y. & Zhang, H. Y. Rational drug repositioning by medical genetics. Nat. Biotechnol. 31, 1080–1082 (2013).

    CAS  PubMed  Google Scholar 

  47. 47

    Willyard, C. New human gene tally reignites debate. Nature 558, 354–355 (2018).

    CAS  PubMed  Google Scholar 

  48. 48

    Smith, S. B., Dampier, W., Tozeren, A., Brown, J. R. & Magid-Slav, M. Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis. PLOS ONE 7, e33174 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Greene, C. S. & Voight, B. F. Pathway and network-based strategies to translate genetic discoveries into effective therapies. Hum. Mol. Genet. 25, R94–R98 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Iorio, F., Saez-Rodriguez, J. & di Bernardo, D. Network based elucidation of drug response: from modulators to targets. BMC Syst. Biol. 7, 139 (2013).

    PubMed  PubMed Central  Google Scholar 

  51. 51

    Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    US Preventive Services Task Force. Final recommendation statement. Aspirin use to prevent cardiovascular disease and colorectal cancer: preventive medication. US Preventive Services Task Force (2017).

  53. 53

    Cavalla, D. & Singal, C. Retrospective clinical analysis for drug rescue: for new indications or stratified patient groups. Drug Discov. Today 17, 104–109 (2012).

    PubMed  Google Scholar 

  54. 54

    Jensen, P. B., Jensen, L. J. & Brunak, S. Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13, 395–405 (2012).

    CAS  PubMed  Google Scholar 

  55. 55

    Paik, H. et al. Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records. Sci. Rep. 5, 8580 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Huang, Y. H. & Vakoc, C. R. A. Biomarker harvest from one thousand cancer cell lines. Cell 166, 536–537 (2016).

    CAS  PubMed  Google Scholar 

  57. 57

    Weinstein, J. N. Drug discovery: cell lines battle cancer. Nature 483, 544–545 (2012).

    CAS  PubMed  Google Scholar 

  58. 58

    Barretina, J. et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Wei, W. Q. & Denny, J. C. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome Med. 7, 41 (2015).

    PubMed  PubMed Central  Google Scholar 

  63. 63

    Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int. J. Epidemiol. 40, 1652–1666 (2011).

    PubMed  PubMed Central  Google Scholar 

  64. 64

    Millwood, I. Y. et al. A phenome-wide association study of a lipoprotein-associated phospholipase A2 loss-of-function variant in 90 000 Chinese adults. Int. J. Epidemiol. 45, 1588–1599 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. 65

    O'Donoghue, M. L. et al. Effect of darapladib on major coronary events after an acute coronary syndrome: the SOLID-TIMI 52 randomized clinical trial. JAMA 312, 1006–1015 (2014).

    PubMed  Google Scholar 

  66. 66

    White, H. D. et al. Darapladib for preventing ischemic events in stable coronary heart disease. N. Engl. J. Med. 370, 1702–1711 (2014).

    CAS  PubMed  Google Scholar 

  67. 67

    Eisenstein, M. Big data: the power of petabytes. Nature 527, S2–S4 (2015).

    CAS  PubMed  Google Scholar 

  68. 68

    Peplow, M. The 100,000 Genomes Project. BMJ 353, i1757 (2016).

    PubMed  Google Scholar 

  69. 69

    Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Cyranoski, D. China embraces precision medicine on a massive scale. Nature 529, 9–10 (2016).

    PubMed  Google Scholar 

  71. 71

    Juric, D. et al. Phosphatidylinositol 3-kinase α-selective inhibition with alpelisib (BYL719) in PIK3CA-altered solid tumors: results from the first-in-human study. J. Clin. Oncol. 36, 1291–1299 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Venot, Q. et al. Targeted therapy in patients with PIK3CA-related overgrowth syndrome. Nature 558, 540–546 (2018).

    CAS  PubMed  Google Scholar 

  73. 73

    Wicks, P., Vaughan, T. E., Massagli, M. P. & Heywood, J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat. Biotechnol. 29, 411–414 (2011).

    CAS  PubMed  Google Scholar 

  74. 74

    Brehmer, D. et al. Cellular targets of gefitinib. Cancer Res. 65, 379–382 (2005).

    CAS  PubMed  Google Scholar 

  75. 75

    Martinez Molina, D. et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 341, 84–87 (2013).

    PubMed  PubMed Central  Google Scholar 

  76. 76

    Alshareef, A. et al. The use of cellular thermal shift assay (CETSA) to study crizotinib resistance in ALK-expressing human cancers. Sci. Rep. 6, 33710 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Miettinen, T. P. & Bjorklund, M. NQO2 is a reactive oxygen species generating off-target for acetaminophen. Mol. Pharm. 11, 4395–4404 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Eyers, P. A., van den, I. P., Quinlan, R. A., Goedert, M. & Cohen, P. Use of a drug-resistant mutant of stress-activated protein kinase 2a/p38 to validate the in vivo specificity of SB 203580. FEBS Lett. 451, 191–196 (1999).

    CAS  PubMed  Google Scholar 

  79. 79

    Cohen, P. Protein kinases—the major drug targets of the twenty-first century? Nat. Rev. Drug Discov. 1, 309–315 (2002).

    CAS  PubMed  Google Scholar 

  80. 80

    Blagg, J. & Workman, P. Choose and use your chemical probe wisely to explore cancer biology. Cancer Cell 32, 9–25 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Hall-Jackson, C. A. et al. Paradoxical activation of Raf by a novel Raf inhibitor. Chem. Biol. 6, 559–568 (1999).

    CAS  PubMed  Google Scholar 

  82. 82

    Su, F. et al. RAS mutations in cutaneous squamous-cell carcinomas in patients treated with BRAF inhibitors. N. Engl. J. Med. 366, 207–215 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Duncan, J. S. et al. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer. Cell 149, 307–321 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84

    Klaeger, S. et al. Chemical proteomics reveals ferrochelatase as a common off-target of kinase inhibitors. ACS Chem. Biol. 11, 1245–1254 (2016).

    CAS  PubMed  Google Scholar 

  85. 85

    Troutman, S. et al. Crizotinib inhibits NF2-associated schwannoma through inhibition of focal adhesion kinase 1. Oncotarget 7, 54515–54525 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. 86

    Wisniewski, D. et al. Characterization of potent inhibitors of the Bcr-Abl and the c-kit receptor tyrosine kinases. Cancer Res. 62, 4244–4255 (2002).

    CAS  PubMed  Google Scholar 

  87. 87

    Blanke, C. D. et al. Long-term results from a randomized phase II trial of standard- versus higher-dose imatinib mesylate for patients with unresectable or metastatic gastrointestinal stromal tumors expressing KIT. J. Clin. Oncol. 26, 620–625 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Sloane, D. A. et al. Drug-resistant aurora A mutants for cellular target validation of the small molecule kinase inhibitors MLN8054 and MLN8237. ACS Chem. Biol. 5, 563–576 (2010).

    CAS  PubMed  Google Scholar 

  89. 89

    Bago, R. et al. The hVps34-SGK3 pathway alleviates sustained PI3K/Akt inhibition by stimulating mTORC1 and tumour growth. EMBO J. 35, 1902–1922 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Carter, T. A. et al. Inhibition of drug-resistant mutants of ABL, KIT, and EGF receptor kinases. Proc. Natl Acad. Sci. USA 102, 11011–11016 (2005).

    CAS  PubMed  Google Scholar 

  91. 91

    Davis, M. I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).

    CAS  PubMed  Google Scholar 

  92. 92

    Karaman, M. W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 26, 127–132 (2008).

    CAS  PubMed  Google Scholar 

  93. 93

    Munoz, L. Non-kinase targets of protein kinase inhibitors. Nat. Rev. Drug Discov. 16, 424–440 (2017).

    CAS  PubMed  Google Scholar 

  94. 94

    Xu, M. et al. Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen. Nat. Med. 22, 1101–1107 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95

    Sun, W. et al. Rapid antimicrobial susceptibility test for identification of new therapeutics and drug combinations against multidrug-resistant bacteria. Emerg. Microbes Infect. 5, e116 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96

    Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. & Prunotto, M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat. Rev. Drug Discov. 16, 531–543 (2017).

    CAS  PubMed  Google Scholar 

  97. 97

    Iljin, K. et al. High-throughput cell-based screening of 4910 known drugs and drug-like small molecules identifies disulfiram as an inhibitor of prostate cancer cell growth. Clin. Cancer Res. 15, 6070–6078 (2009).

    CAS  PubMed  Google Scholar 

  98. 98

    Cousin, M. A. et al. Larval zebrafish model for FDA-approved drug repositioning for tobacco dependence treatment. PLOS ONE 9, e90467 (2014).

    PubMed  PubMed Central  Google Scholar 

  99. 99

    Kremer, S. & Jones, R. Repurposed drugs: second time lucky. Life Sciences Intellectual Property Review (2014).

    Google Scholar 

  100. 100

    Murteira, S., Millier, A., Ghezaiel, Z. & Lamure, M. Drug reformulations and repositioning in the pharmaceutical industry and their impact on market access: regulatory implications. J. Mark. Access Health Policy (2014).

    Google Scholar 

  101. 101

    Frail, D. E. et al. Pioneering government-sponsored drug repositioning collaborations: progress and learning. Nat. Rev. Drug Discov. 14, 833–841 (2015).

    CAS  PubMed  Google Scholar 

  102. 102

    Allison, M. NCATS launches drug repurposing program. Nat. Biotechnol. 30, 571–572 (2012).

    CAS  PubMed  Google Scholar 

  103. 103

    Prague, J. K. et al. Neurokinin 3 receptor antagonism as a novel treatment for menopausal hot flushes: a phase 2, randomised, double-blind, placebo-controlled trial. Lancet 389, 1809–1820 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104

    [No authors listed.] AstraZeneca, Taiwan's NRPB launch drug discovery collaboration. Genetic Engineering & Biotechnology News (2013).

  105. 105

    Drewry, D. H., Willson, T. M. & Zuercher, W. J. Seeding collaborations to advance kinase science with the GSK Published Kinase Inhibitor Set (PKIS). Curr. Top. Med. Chem. 14, 340–342 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106

    Knapp, S. et al. A public-private partnership to unlock the untargeted kinome. Nat. Chem. Biol. 9, 3–6 (2013).

    CAS  PubMed  Google Scholar 

  107. 107

    Billin, A. N. et al. Discovery of novel small molecules that activate satellite cell proliferation and enhance repair of damaged muscle. ACS Chem. Biol. 11, 518–529 (2016).

    CAS  PubMed  Google Scholar 

  108. 108

    Elkins, J. M. et al. Comprehensive characterization of the Published Kinase Inhibitor Set. Nat. Biotechnol. 34, 95–103 (2016).

    CAS  PubMed  Google Scholar 

  109. 109

    Xu, K. & Cote, T. R. Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases. Brief Bioinform. 12, 341–345 (2011).

    CAS  PubMed  Google Scholar 

  110. 110

    Crockett, S. D., Schectman, R., Sturmer, T. & Kappelman, M. D. Topiramate use does not reduce flares of inflammatory bowel disease. Dig. Dis. Sci. 59, 1535–1543 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111

    Auteri, M., Zizzo, M. G. & Serio, R. GABA and GABA receptors in the gastrointestinal tract: from motility to inflammation. Pharmacol. Res. 93, 11–21 (2015).

    CAS  PubMed  Google Scholar 

  112. 112

    Iorio, F. et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl Acad. Sci. USA 107, 14621–14626 (2010).

    CAS  PubMed  Google Scholar 

  113. 113

    Lee, J. K., Shin, J. H., Lee, J. E. & Choi, E. J. Role of autophagy in the pathogenesis of amyotrophic lateral sclerosis. Biochim. Biophys. Acta 1852, 2517–2524 (2015).

    CAS  PubMed  Google Scholar 

  114. 114

    Takata, M. et al. Fasudil, a rho kinase inhibitor, limits motor neuron loss in experimental models of amyotrophic lateral sclerosis. Br. J. Pharmacol. 170, 341–351 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Gunther, R. et al. Rho kinase inhibition with fasudil in the SOD1(G93A) mouse model of amyotrophic lateral sclerosis-symptomatic treatment potential after disease onset. Front. Pharmacol. 8, 17 (2017).

    PubMed  PubMed Central  Google Scholar 

  116. 116

    Tonges, L. et al. Rho kinase inhibition modulates microglia activation and improves survival in a model of amyotrophic lateral sclerosis. Glia 62, 217–232 (2014).

    PubMed  Google Scholar 

  117. 117

    Moschen, A. R. et al. The RANKL/OPG system is activated in inflammatory bowel disease and relates to the state of bone loss. Gut 54, 479–487 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Franke, A. et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nat. Genet. 42, 1118–1125 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119

    University of Manitoba. 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. University of Manitoba MSpace (2017).

  120. 120

    Gligorijevic, V., Malod-Dognin, N. & Przulj, N. Integrative methods for analyzing big data in precision medicine. Proteomics 16, 741–758 (2016).

    CAS  PubMed  Google Scholar 

  121. 121

    Chen, Y., Elenee Argentinis, J. D. & Weber, G. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Ther. 38, 688–701 (2016).

    PubMed  Google Scholar 

  122. 122

    Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A. & Kim, D. Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 85–97 (2015).

    CAS  PubMed  Google Scholar 

  123. 123

    Luo, Y. et al. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8, 573 (2017).

    PubMed  PubMed Central  Google Scholar 

  124. 124

    Napolitano, F. et al. Drug repositioning: a machine-learning approach through data integration. J. Cheminform 5, 30 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125

    Hennequin, L. F. et al. N-(5-chloro-1,3-benzodioxol-4-yl)-7-[2-(4-methylpiperazin-1-yl)ethoxy]-5- (tetrahydro-2H-pyran-4-yloxy)quinazolin-4-amine, a novel, highly selective, orally available, dual-specific c-Src/Abl kinase inhibitor. J. Med. Chem. 49, 6465–6488 (2006).

    CAS  PubMed  Google Scholar 

  126. 126

    Fury, M. G. et al. Phase II study of saracatinib (AZD0530) for patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC). Anticancer Res. 31, 249–253 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127

    Gangadhar, T. C., Clark, J. I., Karrison, T. & Gajewski, T. F. Phase II study of the Src kinase inhibitor saracatinib (AZD0530) in metastatic melanoma. Invest. New Drugs 31, 769–773 (2013).

    CAS  PubMed  Google Scholar 

  128. 128

    Kaufman, A. C. et al. Fyn inhibition rescues established memory and synapse loss in Alzheimer mice. Ann. Neurol. 77, 953–971 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129

    Nygaard, H. B. et al. A phase Ib multiple ascending dose study of the safety, tolerability, and central nervous system availability of AZD0530 (saracatinib) in Alzheimer's disease. Alzheimers Res. Ther. 7, 35 (2015).

    PubMed  PubMed Central  Google Scholar 

  130. 130

    De Felice, M., Lambert, D., Holen, I., Escott, K. J. & Andrew, D. Effects of Src-kinase inhibition in cancer-induced bone pain. Mol. Pain 12, 1744806916643725 (2016).

    PubMed  PubMed Central  Google Scholar 

  131. 131

    Tyryshkin, A., Bhattacharya, A. & Eissa, N. T. SRC kinase is a novel therapeutic target in lymphangioleiomyomatosis. Cancer Res. 74, 1996–2005 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132

    Dickerson, J. & Sharp, F. R. Atypical antipsychotics and a Src kinase inhibitor (PP1) prevent cortical injury produced by the psychomimetic, noncompetitive NMDA receptor antagonist MK-801. Neuropsychopharmacology 31, 1420–1430 (2006).

    CAS  PubMed  Google Scholar 

  133. 133

    Stearns, V. et al. Hot flushes. Lancet 360, 1851–1861 (2002).

    CAS  PubMed  Google Scholar 

  134. 134

    Prague, J. K. & Dhillo, W. S. Neurokinin 3 receptor antagonism — the magic bullet for hot flushes? Climacteric 20, 505–509 (2017).

    CAS  PubMed  Google Scholar 

  135. 135

    Jayasena, C. N. et al. Neurokinin B administration induces hot flushes in women. Sci. Rep. 5, 8466 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 136

    Mittelman-Smith, M. A., Williams, H., Krajewski-Hall, S. J., McMullen, N. T. & Rance, N. E. Role for kisspeptin/neurokinin B/dynorphin (KNDy) neurons in cutaneous vasodilatation and the estrogen modulation of body temperature. Proc. Natl Acad. Sci. USA 109, 19846–19851 (2012).

    CAS  PubMed  Google Scholar 

  137. 137

    Rance, N. E. & Young, W. S. 3rd. Hypertrophy and increased gene expression of neurons containing neurokinin-B and substance-P messenger ribonucleic acids in the hypothalami of postmenopausal women. Endocrinology 128, 2239–2247 (1991).

    CAS  PubMed  Google Scholar 

  138. 138

    Dacks, P. A., Krajewski, S. J. & Rance, N. E. Activation of neurokinin 3 receptors in the median preoptic nucleus decreases core temperature in the rat. Endocrinology 152, 4894–4905 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139

    Crandall, C. J. et al. Association of genetic variation in the tachykinin receptor 3 locus with hot flashes and night sweats in the Women's Health Initiative Study. Menopause 24, 252–261 (2017).

    PubMed  PubMed Central  Google Scholar 

  140. 140

    Griebel, G. & Beeske, S. Is there still a future for neurokinin 3 receptor antagonists as potential drugs for the treatment of psychiatric diseases? Pharmacol. Ther. 133, 116–123 (2012).

    CAS  PubMed  Google Scholar 

  141. 141

    Evangelista, S. Talnetant GlaxoSmithKline. Curr. Opin. Investig. Drugs 6, 717–721 (2005).

    CAS  PubMed  Google Scholar 

  142. 142

    Fraser, G. L. et al. Clinical evaluation of the NK3 receptor antagonist fezolinetant (a.k.a. ESN364) for the treatment of menopausal hot flashes. Endocrine Society (2017).

  143. 143

    Cully, M. Deal watch: neurokinin 3 receptor antagonist revival heats up with Astellas acquisition. Nat. Rev. Drug Discov. 16, 377 (2017).

    CAS  PubMed  Google Scholar 

  144. 144

    Protheroe, A., Edwards, J. C., Simmons, A., Maclennan, K. & Selby, P. Remission of inflammatory arthropathy in association with anti-CD20 therapy for non-Hodgkin's lymphoma. Rheumatology 38, 1150–1152 (1999).

    CAS  PubMed  Google Scholar 

  145. 145

    Storz, U. Rituximab: how approval history is reflected by a corresponding patent filing strategy. mAbs 6, 820–837 (2014).

    PubMed  PubMed Central  Google Scholar 

  146. 146

    Brinkmann, V. et al. Fingolimod (FTY720): discovery and development of an oral drug to treat multiple sclerosis. Nat. Rev. Drug Discov. 9, 883–897 (2010).

    CAS  PubMed  Google Scholar 

  147. 147

    Bezprozvanny, I. The rise and fall of Dimebon. Drug News Perspect. 23, 518–523 (2010).

    PubMed  PubMed Central  Google Scholar 

  148. 148

    Cudkowicz, M. E. et al. Safety and efficacy of ceftriaxone for amyotrophic lateral sclerosis: a multi-stage, randomised, double-blind, placebo-controlled trial. Lancet Neurol. 13, 1083–1091 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149

    Markey, K. A. et al. Assessing the efficacy and safety of an 11beta-hydroxysteroid dehydrogenase type 1 inhibitor (AZD4017) in the Idiopathic Intracranial Hypertension Drug Trial, IIH:DT: clinical methods and design for a phase II randomized controlled trial. JMIR Res. Protoc. 6, e181 (2017).

    PubMed  PubMed Central  Google Scholar 

  150. 150

    Assouline, S. et al. Molecular targeting of the oncogene eIF4E in acute myeloid leukemia (AML): a proof-of-principle clinical trial with ribavirin. Blood 114, 257–260 (2009).

    CAS  PubMed  Google Scholar 

Download references


This paper stems from a workshop hosted by the Medical Research Council (MRC) Centre for Drug Safety Science (CDSS;, University of Liverpool, in conjunction with the UK Pharmacogenetics and Stratified Medicine Network ( in November 2015 to discuss the current status of drug repurposing and evaluate various challenges faced by this field. The workshop was attended by representatives from pharmaceutical and biotechnology companies, including small-and-medium-sized enterprises focusing on drug repurposing, contract research organizations, regulatory agencies, research funding charities and academia. The content of this review has been expanded through literature search and further discussions of the authors and their research networks since the workshop.

Author information



Corresponding author

Correspondence to Munir Pirmohamed.

Ethics declarations

Competing interests

S.H. is an author on this manuscript and works for the Medicines and Healthcare Products Regulatory Agency (MHRA), UK; the opinions expressed in this review are her own and should not be attributed to the MHRA/European Medicines Agency (EMA). A.D. is a director of PharmaKure Ltd.

Related links


Array Express

Association of Medical Research Charities

AstraZeneca Open Innovation platform

AstraZeneca press release 2016

Bayer's Grant4Indications initiative

Connectivity Map

Cures Within Reach

Duchenne UK

European Medicines Agency clinical data

European Medicines Agency. Summary of product characteristics - ketoconazole

EveryLife Foundation for Rare Diseases OPEN ACT press release

Gene Expression Omnibus

GlaxoSmithKline's Center for Excellence for External Drug Discovery

Global Genes RARE Diseases

Lilly reports fourth-quarter and full-year 2015 results


Novartis 2017 product sales

Pfizer 2014 financial report

Pfizer phase III HORIZON trial

Questale minoxidil sales report 2017

UK parliament Off-patent Drugs Bill 2015–16

US Food and Drug Administration

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pushpakom, S., Iorio, F., Eyers, P. et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov 18, 41–58 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing