CORRIGENDUM: Unexplored therapeutic opportunities in the human genome (Nature Reviews Drug Discovery (2018) (10.1038/nrd.2018.14));Unexplored therapeutic opportunities in the human genome

Nature Reviews Drug Discovery

Volumn 17, Issue 5, 2018, Pages 317-332

  Oprea, Tudor I ^a,b,c,d   Bologa, Cristian G ^a   Brunak, Søren ^d   Campbell, Allen ^e   Gan, Gregory N ^b   Gaulton, Anna ^f   Gomez, Shawn M ^g,h   Guha, Rajarshi ⁱ   Hersey, Anne ^f   Holmes, Jayme ^a   Jadhav, Ajit ⁱ   Jensen, Lars Juhl ^d   Johnson, Gary L ^h   Karlson, Anneli ^f,q   Leach, Andrew R ^f   Ma'ayan, Avi ^j   Malovannaya, Anna ^k   Mani, Subramani ^a   Mathias, Stephen L ^a   McManus, Michael T ^l   Meehan, Terrence F ^f   Von Mering, Christian ^m   Muthas, Daniel ⁿ   Nguyen, Dac Trung ⁱ   Overington, John P ^f,r   Papadatos, George ^f,s   Qin, Jun ^k   Reich, Christian ^e   Roth, Bryan L ^h   Schürer, Stephan C ^o   Simeonov, Anton ⁱ   Sklar, Larry A ^a,b   Southall, Noel ⁱ   Tomita, Susumu ^p   Tudose, Ilinca ^f,t   Ursu, Oleg ^a   Vidović, Dušica ^o   Waller, Anna ^b   Westergaard, David ^d   Yang, Jeremy J ^a   Zahoránszky Köhalmi, Gergely ^a,i   more..

^a UNIVERSITY OF NEW MEXICO SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF NEW MEXICO   (United States)

^c UNIVERSITY OF GOTHENBURG   (Sweden)

^d University of Copenhagen   (Denmark)

^e IQVIA   (United States)

^f EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^g USA and North Carolina State University   (United States)

^h UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

ⁱ NATIONAL INSTITUTES OF HEALTH   (United States)

^j MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^k BAYLOR COLLEGE OF MEDICINE   (United States)

^l UNIVERSITY OF CALIFORNIA   (United States)

^m UNIVERSITY OF ZURICH   (Switzerland)

ⁿ ASTRAZENECA R AND D   (Sweden)

^o UNIVERSITY OF MIAMI MILLER SCHOOL OF MEDICINE   (United States)

^p YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^q Wellcome Genome Campus   (United Kingdom)

^r MEDICINES DISCOVERY CATAPULT   (United Kingdom)

^s GLAXOSMITHKLINE   (United Kingdom)

^t GOOGLE   (United Kingdom)

more..

Author keywords

[No Author keywords available]

Indexed keywords

G PROTEIN COUPLED RECEPTOR; ION CHANNEL; PROTEIN KINASE; PROTEOME;

DATA PROCESSING; DRUG TARGETING; EVIDENCE BASED MEDICINE; GENOMICS; HUMAN; HUMAN GENOME; ILLUMINATING THE DRUGGABLE GENOME PROGRAM; INFORMATION PROCESSING; KNOWLEDGE BASE; MEDICAL RESEARCH; NONHUMAN; PRIORITY JOURNAL; PROTEIN FAMILY; PROTEOMICS; REVIEW;

EID: 85045980099     PISSN: 14741776     EISSN: 14741784     Source Type: Journal    
DOI: 10.1038/NRD.2018.52     Document Type: Erratum

References (105)

1. Knowles, J. & Gromo, G. Target selection in drug discovery. Nat. Rev. Drug Discov. 2, 63-69 (2003).
2. Edwards, A. M. et al. Too many roads not taken. Nature 470, 163-165 (2011).
3. Alberts, B., Kirschner, M. W., Tilghman, S. & Varmus, H. Rescuing US biomedical research from its systemic flaws. Proc. Natl Acad. Sci. USA 111, 5773-5777 (2014).
4. Kim, S. et al. PubChem Substance and Compound databases. Nucleic Acids Res. 44, D1202-D1213 (2016).
5. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945-D954 (2017).
6. Tomczak, K., Czerwińska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19, A68-A77 (2015).
7. Munaf, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).
8. Nickerson, R. S. Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175-220 (1998).
9. Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19-34 (2017).
10. Ursu, O. et al. Drug Central: online drug compendium. Nucleic Acids Res. 45, D932-D939 (2017).
11. Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793-D796 (2009).
12. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25-29 (2000).
13. Pletscher-Frankild, S., Pallej, A., Tsafou, K., Binder, J. X. & Jensen, L. J. Diseases: text mining and data integration of disease-gene associations. Methods 74, 83-89 (2015).
14. Kiermer, V. Antibodypedia. Nat. Methods 5, 860-861 (2008).
15. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204-D212 (2015).
16. Papadatos, G. et al. SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res. 44, D1220-1228 (2016).
17. Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, baw100 (2016).
18. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447-D452 (2015).
19. Hajduk, P. J., Huth, J. R. & Tse, C. Predicting protein druggability. Drug Discov. Today 10, 1675-1682 (2005).
20. Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 1, 727-730 (2002).
21. Surade, S. & Blundell, T. L. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem. Biol. 19, 42-50 (2012).
22. Kubinyi, H. Drug research: myths, hype and reality. Nat. Rev. Drug Discov. 2, 665-668 (2003).
23. Yang, X. et al. Widespread expansion of protein interaction capabilities by alternative splicing. Cell 164, 805-817 (2016).
24. Mestres, J., Gregori-Puigjan, E., Valverde, S. & Sol, R. V. Data completeness-the Achilles heel of drug-target networks. Nat. Biotechnol. 26, 983-984 (2008).
25. Schreiber, S. L. et al. Advancing biological understanding and therapeutics discovery with small-molecule probes. Cell 161, 1252-1265 (2015).
26. Austin, C. P., Brady, L. S., Insel, T. R. & Collins, F. S. NIH molecular libraries initiative. Science 306, 1138-1139 (2004).
27. Southan, C. et al. The IUPHAR/BPS guide to pharmacology in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res. 44, D1054-D1068 (2016).
28. 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).
29. Hunter, S. et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306-312 (2012).
30. Kruger, F. A., Gaulton, A., Nowotka, M. & Overington, J. P. PPDMs a resource for mapping small molecule bioactivities from ChEMBL to Pfam A protein domains. Bioinformatics 31, 776-778 (2015).
31. Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263-266 (2008).
32. Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175-181 (2009).
33. Huang, X. & Dixit, V. M. Drugging the undruggables: exploring the ubiquitin system for drug development. Cell Res. 26, 484-498 (2016).
34. Lai, A. C. & Crews, C. M. Induced protein degradation: an emerging drug discovery paradigm. Nat. Rev. Drug Discov. 16, 101-114 (2017).
35. Sakamoto, K. M. et al. Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc. Natl Acad. Sci. 98, 8554-8559 (2001).
36. Gadd, M. S. et al. Structural basis of PROTAC cooperative recognition for selective protein degradation. Nat. Chem. Biol. 13, 514-521 (2017).
37. Mungall, C. J. et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res. 45, D712-D722 (2017).
38. Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508-514 (2016).
39. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896-D901 (2017).
40. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580-585 (2013).
41. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204-213 (2017).
42. Uhén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
43. Kim, M. S. et al. A draft map of the human proteome. Nature 509, 575-581 (2014).
44. Lenat, D. B. & Feigenbaum, E. A. On the thresholds of knowledge. Artif. Intell. 47, 185-250 (1991).
45. Fishilevich, S. et al. Genic insights from integrated human proteomics in GeneCards. Database 2016, baw030 (2016).
46. Smirnov, D. A. et al. Genetic variation in radiation-induced cell death. Genome Res. 22, 332-339 (2012).
47. Garrison, J. L. & Knight, Z. A. Linking smell to metabolism and aging. Science 358, 718-719 (2017).
48. Kliewer, S. A., Lehmann, J. M. & Willson, T. M. Orphan nuclear receptors: shifting endocrinology into reverse. Science 284, 757-760 (1999).
49. Willson, T. M., Jones, S. A., Moore, J. T. & Kliewer, S. A. Chemical genomics: functional analysis of orphan nuclear receptors in the regulation of bile acid metabolism. Med. Res. Rev. 21, 513-522 (2001).
50. Moore, L. B. et al. Orphan nuclear receptors constitutive androstane receptor and pregnane X receptor share xenobiotic and steroid ligands. J. Biol. Chem. 275, 15122-15127 (2000).
51. Pellicciari, R. et al. 6alpha ethyl-chenodeoxycholic acid (6 ECDCA), a potent and selective FXR agonist endowed with anticholestatic activity. J. Med. Chem. 45, 3569-3572 (2002).
52. Hambruch, E., Kinzel, O. & Kremoser, C. On the pharmacology of farnesoid X receptor agonists: give me an 'A', like in 'acid'. Nucl. Recep. Res. 3, 101207 (2016).
53. Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414-427 (2017).
54. Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143-1151 (2017).
55. Roth, B. L., Sheffler, D. J. & Kroeze, W. K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353-359 (2004).
56. Hernandez, P. A. et al. Mutations in the chemokine receptor gene CXCR4 are associated with WHIM syndrome, a combined immunodeficiency disease. Nat. Genet. 34, 70-74 (2003).
57. Sternini, C. Receptors and transmission in the brain-gut axis: potential for novel therapies. III. Mu opioid receptors in the enteric nervous system. Am. J. Physiol. Gastrointest. Liver Physiol. 281, G8-15 (2001).
58. Sternini, C. Taste receptors in the gastrointestinal tract. IV. Functional implications of bitter taste receptors in gastrointestinal chemosensing. Am. J. Physiol. Gastrointest. Liver Physiol. 292, G457-461 (2007).
59. Rockman, H. A., Koch, W. J. & Lefkowitz, R. J. Seven-transmembrane-spanning receptors and heart function. Nature 415, 206-212 (2002).
60. Elphick, G. F. et al. The human polyomavirus, JCV, uses serotonin receptors to infect cells. Science 306, 1380-1383 (2004).
61. Roth, B. L. & Kroeze, W. K. Integrated approaches for genome-wide interrogation of the druggable non-olfactory G protein-coupled receptor superfamily. J. Biol. Chem. 290, 19471-19477 (2015).
62. Elkins, J. M. et al. Comprehensive characterization of the Published Kinase Inhibitor Set. Nat. Biotechnol. 34, 95-103 (2016).
63. Lin, X. et al. Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5 HT receptors. J. Med. Chem. 55, 5749-5759 (2012).
64. Huang, X. P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477-483 (2015).
65. Chan, J. D. et al. The anthelmintic praziquantel is a human serotoninergic G protein-coupled receptor ligand. Nat. Commun. 8, 1910 (2017).
66. Roth, B. L. Drugs and valvular heart disease. N. Engl. J. Med. 356, 6-9 (2007).
67. Kroeze, W. K. et al. PRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome. Nat. Struct. Mol. Biol. 22, 362-369 (2015).
68. Lansu, K. et al. In silico design of novel probes for the atypical opioid receptor MRGPRX2. Nat. Chem. Biol. 13, 529-536 (2017).
69. Pafilis, E. et al. The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. PLoS ONE 8, e65390 (2013).
70. Okajima, D., Kudo, G. & Yokota, H. Antidepressant-like behavior in brain-specific angiogenesis inhibitor 2 deficient mice. J. Physiol. Sci. 61, 47-54 (2011).
71. Katsu, T. et al. The human frizzled 3 (FZD3) gene on chromosome 8p21, a receptor gene for Wnt ligands, is associated with the susceptibility to schizophrenia. Neurosci. Lett. 353, 53-56 (2003).
72. Wei, J. & Hemmings, G. P. Lack of a genetic association between the frizzled 3 gene and schizophrenia in a British population. Neurosci. Lett. 366, 336-338 (2004).
73. Jeong, S. H., Joo, E. J., Ahn, Y. M., Lee, K. Y. & Kim, Y. S. Investigation of genetic association between human Frizzled homolog 3 gene (FZD3) and schizophrenia: results in a Korean population and evidence from meta-analysis. Psychiatry Res. 143, 1-11 (2006).
74. Wu, P., Nielsen, T. E. & Clausen, M. H. Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discov. Today 21, 5-10 (2016).
75. Zawistowski, J. S. et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P TEFb complex. Cancer Discov. 7, 302-321 (2017).
76. Kullmann, D. M. The neuronal channelopathies. Brain 125, 1177-1195 (2002).
77. Gloyn, A. L. et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568-572 (2003).
78. Marbán, E. Cardiac channelopathies. Nature 415, 213-218 (2002).
79. Berman, R. M. et al. Antidepressant effects of ketamine in depressed patients. Biol. Psychiatry 47, 351-354 (2000).
80. Kirby, T. Ketamine for depression: the highs and lows. Lancet Psychiatry 2, 783-784 (2015).
81. Zanos, P. et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533, 481-486 (2016).
82. Pedersen, S. F., Klausen, T. K. & Nilius, B. The identification of a volume-regulated anion channel: an amazing Odyssey. Acta Physiol. 213, 868-881 (2015).
83. Niemeyer, B. A. Changing calcium: CRAC channel (STIM and Orai) expression, splicing, and posttranslational modifiers. Am. J. Physiol. Cell Physiol. 310, C701-709 (2016).
84. Dauner, K., Lissmann, J., Jeridi, S., Frings, S. & Möhrlen, F. Expression patterns of anoctamin 1 and anoctamin 2 chloride channels in the mammalian nose. Cell Tissue Res. 347, 327-341 (2012).
85. Pandey, A. K., Lu, L., Wang, X., Homayouni, R. & Williams, R. W. Functionally enigmatic genes: a case study of the brain ignorome. PLoS ONE 9, e88889 (2014).
86. Pfeffer, C. & Olsen, B. R. Editorial: Journal of negative results in biomedicine. J. Negat. Results Biomed. 1, 2 (2002).
87. Groth, P., Gibson, A. & Velterop, J. The anatomy of a nanopublication. Inf. Serv. Use 30, 51-56 (2010).
88. Agarwal, P. & Searls, D. B. Can literature analysis identify innovation drivers in drug discovery? Nat. Rev. Drug Discov. 8, 865-878 (2009).
89. Nguyen, D. T. et al. Pharos: Collating protein information to shed light on the druggable genome. Nucleic Acids Res. 45, D995-D1002 (2017).
90. Wishart, D. S. et al. Drug Bank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074-D1082 (2017).
91. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158-D169 (2017).
92. Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170-174 (2017).
93. Koscielny, G. et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 45, D985-D994 (2017).
94. Lin, Y. et al. Drug target ontology to classify and integrate drug discovery data. J. Biomed. Semant. 8, 50 (2017).
95. Maggon, K. Best-selling human medicines 2002-2004. Drug Discov. Today 10, 739-742 (2005).
96. Stebbins, S. The world's 15 top selling drugs. 24/7 Wall St. http://247wallst.com/special-report/2016/04/26/top-selling-drugs-in-the-world/ (2016).
97. Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B. & Gloriam, D. E. Trends in GPCR drug discovery: new agents, targets and indications. Nat. Rev. Drug Discov. 16, 829-842 (2017).
98. Shih, H. P., Zhang, X. & Aronov, A. M. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nat. Rev. Drug Discov. 17, 19-33 (2018).
99. Tartaglia, L. A. et al. Identification and expression cloning of a leptin receptor, OB R. Cell 83, 1263-1271 (1995).
100. Xie, J. et al. Activating Smoothened mutations in sporadic basal-cell carcinoma. Nature 391, 90-92 (1998).
101. Lee, M. J. et al. Sphingosine 1 phosphate as a ligand for the G protein-coupled receptor EDG 1. Science 279, 1552-1555 (1998).
102. Sakurai, T. et al. Orexins and orexin receptors: a family of hypothalamic neuropeptides and G protein-coupled receptors that regulate feeding behavior. Cell 92, 573-585 (1998).
103. Abifadel, M. et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat. Genet. 34, 154-156 (2003).
104. Kojima, M. et al. Ghrelin is a growth-hormone-releasing acylated peptide from stomach. Nature 402, 656-660 (1999).
105. Temel, J. S. et al. Anamorelin in patients with non-small-cell lung cancer and cachexia (ROMANA 1 and ROMANA 2): results from two randomised, double-blind, phase 3 trials. Lancet Oncol. 17, 519-531 (2016).