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A comprehensive map of molecular drug targets

Key Points

  • The definition of a drug target is crucial to the success of mechanism-based drug discovery. It is also increasingly important for efforts to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups.

  • In this article, we synthesized and built on our previous approaches and systematically recompiled and comprehensively annotated the current list of drugs approved by the US FDA. We assigned to each drug their respective efficacy target or target set from the prescribing information and/or the scientific literature.

  • We curated a total of 893 human and pathogen-derived biomolecules through which 1,578 FDA-approved drugs act. These include 667 human-genome-derived proteins targeted by drugs for human disease.

  • We emphasize that even with a well-defined concept of efficacy there are challenges in making a clean unambiguous assignment in many cases, especially regarding how to treat protein complexes or drugs that bind to a number of closely related gene products.

  • We also mapped each drug (and thereby target) to the WHO Anatomical Therapeutic Chemical Classification System code as a way of obtaining a standard therapeutic indication for them.

  • With this mapping, we explored the footprint of target classes across disease areas, investigated the success of privileged target families and compiled a list of drug target orthologues for standard model organisms to develop a foundation for the deeper understanding of species differences, cross-species drug repositioning and applicability of animal models.

Abstract

The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.

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Figure 1: Major protein families as drug targets.
Figure 2: Innovation patterns in therapeutic areas.
Figure 3: Innovation patterns in privileged protein classes.
Figure 4: Promiscuity of privileged protein family classes.
Figure 5: Protein efficacy targets availability across several model organisms.
Figure 6: Overlap of cancer drug targets with cancer drivers.

References

  1. 1

    Raju, T. N. The Nobel chronicles. Lancet 355, 1022 (2000).

    CAS  Article  Google Scholar 

  2. 2

    Drews, J. Genomic sciences and the medicine of tomorrow. Nat. Biotechnol. 14, 1516–1518 (1996). An early and influential review on the prospects for genomics and drug discovery. The associated target list is presented in reference 3.

    CAS  Article  Google Scholar 

  3. 3

    Drews, J. & Ryser, S. Classic drug targets. Nat. Biotechnol. 15, 1350 (1997).

    Article  Google Scholar 

  4. 4

    Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002). The first attempt to define the future druggable genome on the basis of successful drug development programmes.

    CAS  Article  Google Scholar 

  5. 5

    Golden, J. B. Prioritizing the human genome: knowledge management for drug discovery. Curr. Opin. Drug Discov. Devel. 6, 310–316 (2003).

    CAS  PubMed  Google Scholar 

  6. 6

    Imming, P., Sinning, C. & Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov. 5, 821–835 (2006).

    CAS  Article  Google Scholar 

  7. 7

    Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006). A 10-year old review on the then-known landscape of drug targets.

    CAS  Article  Google Scholar 

  8. 8

    Chen, X., Ji, Z. L. & Chen, Y. Z. TTD: Therapeutic Target Database. Nucleic Acids Res. 30, 412–415 (2002).

    CAS  Article  Google Scholar 

  9. 9

    Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34, D668–D672 (2006).

    CAS  Article  Google Scholar 

  10. 10

    Günther, S. et al. SuperTarget and Matador: resources for exploring drug–target relationships. Nucleic Acids Res. 36, D919–D922 (2008).

    Article  Google Scholar 

  11. 11

    Rask-Andersen, M., Almén, M. S. & Schiöth, H. B. Trends in the exploitation of novel drug targets. Nat. Rev. Drug Discov. 10, 579–590 (2011). A more recent review on the landscape of drug targets.

    CAS  Article  Google Scholar 

  12. 12

    Munos, B. A. Forensic analysis of drug targets from 2000 through 2012. Clin. Pharmacol. Ther. 94, 407–411 (2013). A recent overview of drug and target approvals for 2000–2012.

    CAS  Article  Google Scholar 

  13. 13

    Agarwal, P., Sanseau, P. & Cardon, L. R. Novelty in the target landscape of the pharmaceutical industry. Nat. Rev. Drug Discov. 12, 575–576 (2013). This paper addresses the target diversity across large pharmaceutical companies — are all companies pursuing the same targets?

    CAS  Article  Google Scholar 

  14. 14

    Pawson, A. J. et al. The IUPHAR/BPS Guide to pharmacology: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 42, D1098–D1106 (2014).

    CAS  Article  Google Scholar 

  15. 15

    Ursu, O. et al. DrugCentral: online drug compendium. Nucleic Acids Res. 10.1093/nar/gkw993 (2016).

  16. 16

    Tym, J. E. et al. canSAR: an updated cancer research and drug discovery knowledgebase. Nucleic Acids Res. 44, D938–D943 (2016).

    CAS  Article  Google Scholar 

  17. 17

    Bento, A. P. et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 42, D1083–D1090 (2014).

    CAS  Article  Google Scholar 

  18. 18

    Meltzer, H. Y. & Roth, B. L. Lorcaserin and pimavanserin: emerging selectivity of serotonin receptor subtype-targeted drugs. J. Clin. Invest. 123, 4986–4991 (2013).

    CAS  Article  Google Scholar 

  19. 19

    Friedman, J. H. Pimavanserin for the treatment of Parkinson's disease psychosis. Expert Opin. Pharmacother. 14, 1969–1975 (2013).

    CAS  Article  Google Scholar 

  20. 20

    Bandelow, B. & Meier, A. Aripiprazole, a 'dopamine–serotonin system stabilizer' in the treatment of psychosis. German J. Psychiatry 6, 9–16 (2003).

    Google Scholar 

  21. 21

    Mamo, D. et al. Differential effects of aripiprazole on D2, 5-HT2, and 5-HT1A receptor occupancy in patients with schizophrenia: a triple tracer PET study. Am. J. Psychiatry 164, 1411–1417 (2007).

    Article  Google Scholar 

  22. 22

    Tolboom, N. et al. The dopamine stabilizer (–)-OSU6162 occupies a subpopulation of striatal dopamine D2/D3 receptors: an [11C]raclopride PET study in healthy human subjects. Neuropsychopharmacology 40, 472–479 (2015).

    CAS  Article  Google Scholar 

  23. 23

    Rang, H. P., Dale, M. M., Ritter, J. M., Flower, R. J. & Henderson, G. (eds) Rang and Dale's Pharmacology 7th edn (Elsevier Health Sciences UK, 2012). A comprehensive and classic book on pharmacology and drug mode of action.

    Google Scholar 

  24. 24

    Koarai, A. et al. Expression of muscarinic receptors by human macrophages. Eur. Respir. J. 39, 698–704 (2012).

    CAS  Article  Google Scholar 

  25. 25

    Lammers, J. W., Minette, P., McCusker, M. & Barnes, P. J. The role of pirenzepine-sensitive (M1) muscarinic receptors in vagally mediated bronchoconstriction in humans. Am. Rev. Respir. Dis. 139, 446–449 (1989).

    CAS  Article  Google Scholar 

  26. 26

    Krauss, J., van der Linden, M., Grebe, T. & Hakenbeck, R. Penicillin-binding proteins 2x and 2b as primary PBP targets in Streptococcus pneumoniae. Microb. Drug Resist. 2, 183–186 (1996).

    CAS  Article  Google Scholar 

  27. 27

    Wells, S. A. et al. Vandetanib in patients with locally advanced or metastatic medullary thyroid cancer: a randomized, double-blind phase iii trial. J. Clin. Oncol. 30, 134–141 (2012).

    CAS  Article  Google Scholar 

  28. 28

    Santoro, M. et al. Molecular biology of the MEN2 gene. J. Intern. Med. 243, 505–508 (1998).

    CAS  Article  Google Scholar 

  29. 29

    Thollon, C. et al. Use-dependent inhibition of hHCN4 by ivabradine and relationship with reduction in pacemaker activity. Br. J. Pharmacol. 150, 37–46 (2007).

    CAS  Article  Google Scholar 

  30. 30

    Sobrado, L. F. et al. Dronedarone's inhibition of If current is the primary mechanism responsible for its bradycardic effect. J. Cardiovasc. Electrophysiol. 24, 914–918 (2013).

    Article  Google Scholar 

  31. 31

    Bucchi, A. et al. Identification of the molecular site of ivabradine binding to HCN4 channels. PLoS ONE 8, e53132 (2013).

    CAS  Article  Google Scholar 

  32. 32

    Xynogalos, P. et al. Class III antiarrhythmic drug dronedarone inhibits cardiac inwardly rectifying Kir2.1 channels through binding at residue E224. Naunyn. Schmiedebergs. Arch. Pharmacol. 387, 1153–1161 (2014).

    CAS  Article  Google Scholar 

  33. 33

    Gómez, R. et al. Structural basis of drugs that increase cardiac inward rectifier Kir2.1 currents. Cardiovasc. Res. 104, 337–346 (2014).

    Article  Google Scholar 

  34. 34

    Heijman, J., Heusch, G. & Dobrev, D. Pleiotropic effects of antiarrhythmic agents: dronedarone in the treatment of atrial fibrillation. Clin. Med. Insights Cardiol. 7, 127–140 (2013).

    Article  Google Scholar 

  35. 35

    Brayfield, A. Martindale: The Complete Drug Reference 38th edn (Pharmaceutical Press, 2014).

    Google Scholar 

  36. 36

    Swiss Pharmaceutical Society. Index Nominum: International Drug Directory 18th edn (Medpharm Scientific Publishers, 2004).

  37. 37

    Allen, T. M. Ligand-targeted therapeutics in anticancer therapy. Nat. Rev. Cancer 2, 750–763 (2002).

    CAS  Article  Google Scholar 

  38. 38

    International Cancer Genome Consortium. International network of cancer genome projects. Nature 464, 993–998 (2010).

  39. 39

    Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    CAS  Article  Google Scholar 

  40. 40

    Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    CAS  Article  Google Scholar 

  41. 41

    Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    CAS  Article  Google Scholar 

  42. 42

    Soda, M. et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature 448, 561–566 (2007).

    CAS  Article  Google Scholar 

  43. 43

    Workman, P. & Al-Lazikani, B. Drugging cancer genomes. Nat. Rev. Drug Discov. 12, 889–890 (2013).

    CAS  Article  Google Scholar 

  44. 44

    Fletcher, J. I., Haber, M., Henderson, M. J. & Norris, M. D. ABC transporters in cancer: more than just drug efflux pumps. Nat. Rev. Cancer 10, 147–156 (2010).

    CAS  Article  Google Scholar 

  45. 45

    Patel, M. N., Halling-Brown, M. D., Tym, J. E., Workman, P. & Al-Lazikani, B. Objective assessment of cancer genes for drug discovery. Nat. Rev. Drug Discov. 12, 35–50 (2013). A review of cancer drug targets and approaches to prioritize target selection.

    CAS  Article  Google Scholar 

  46. 46

    Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136, 823–837 (2009).

    CAS  Article  Google Scholar 

  47. 47

    Weinstein, I. B. & Joe, A. Oncogene addiction. Cancer Res. 68, 3077–3080 (2008).

    CAS  Article  Google Scholar 

  48. 48

    Matteo, J. et al. DNA-repair defects and olaparib in metastatic prostate cancer N. Engl. J. Med. 373, 1697–1708 (2015).

    Article  Google Scholar 

  49. 49

    Shukla, N. Proteasome addiction defined in Ewing sarcoma is effectively targeted by a novel class of 19S proteasome inhibitors Cancer Res. 76, 4525–4534 (2016).

    CAS  Article  Google Scholar 

  50. 50

    Eder, J., Sedrani, R. & Wiesmann, C. The discovery of first-in-class drugs: origins and evolution. Nat. Rev. Drug Discov. 13, 577–587 (2014).

    CAS  Article  Google Scholar 

  51. 51

    Swinney, D. C. & Anthony, J. How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

    CAS  Article  Google Scholar 

  52. 52

    Al-Lazikani, B. & Workman, P. Unpicking the combination lock for mutant BRAF and RAS melanomas. Cancer Discov. 3, 14–19 (2013).

    CAS  Article  Google Scholar 

  53. 53

    Workman, P., Clarke, P. A. & Al-Lazikani, B. Blocking the survival of the nastiest by HSP90 inhibition. Oncotarget 7, 3658–3661 (2016).

    Article  Google Scholar 

  54. 54

    Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S. & Hopkins, A. L. Global mapping of pharmacological space. Nat. Biotechnol. 24, 805–815 (2006).

    CAS  Article  Google Scholar 

  55. 55

    Huttunen, K. M., Raunio, H. & Rautio, J. Prodrugs — from serendipity to rational design. Pharmacol. Rev. 63, 750–771 (2011).

    CAS  Article  Google Scholar 

  56. 56

    Huang, R. et al. The NCGC pharmaceutical collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci. Transl Med. 3, 80ps16 (2011).

    Article  Google Scholar 

  57. 57

    Kinsella, R. J. et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database 2011, bar030 (2011).

    Article  Google Scholar 

  58. 58

    Flicek, P. et al. Ensembl 2012. Nucleic Acids Res. 40, D84–D90 (2012).

    CAS  Article  Google Scholar 

  59. 59

    Sonnhammer, E. L. L. & Östlund, G. InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Res. 43, D234–D239 (2015).

    CAS  Article  Google Scholar 

  60. 60

    United States Pharmacopeial Convention. USP Dictionary of USAN and International Drug Names 2010 (United States Pharmacopeia, 2010).

  61. 61

    Gleeson, M. P., Hersey, A., Montanari, D. & Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat. Rev. Drug Discov. 10, 197–208 (2011).

    CAS  Article  Google Scholar 

  62. 62

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  Article  Google Scholar 

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Acknowledgements

The work of the authors is supported by the following institutes, organizations and grants: (1) Wellcome Trust Strategic Awards WT086151/Z/08/Z, WT104104/Z/14/Z to J.P.O., A.G., A.H. and A.P.B.; (2) the member states of EMBL to R.S., R.S.D, A.G., A.H., A.P.B.); (3) US National Institutes of Health (NIH) grants 1U54CA189205-01 to O.U., A.G., A.H., A.K., C.G.B., T.I.O. and J.P.O., and NIH grants P30CA118100 and UL1TR001449 to T.I.O.; and (4) B.A.-L. is funded by the Institute of Cancer Research. canSAR is funded by The Cancer Research UK grant to the Cancer Research UK Cancer Therapeutics Unit (grant C309/A11566). The authors thank many of their collaborators for discussions and valuable input in the preparation of this manuscript, in particular the members of the Illuminating the Druggable Genome consortium.

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Deconvoluting the average number of drugs per target, and targets per drug. (PDF 169 kb)

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Rate of target innovation. (PDF 160 kb)

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Santos, R., Ursu, O., Gaulton, A. et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16, 19–34 (2017). https://doi.org/10.1038/nrd.2016.230

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