Deorphanization strategies for dark chemical matter

Drug Discovery Today: Technologies

Volumn 23, Issue , 2017, Pages 69-74

  Wassermann, Anne Mai ^a   Tudor, Matthew ^a   Glick, Meir ^a  

^a MERCK RESEARCH LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL ACTIVITY; STATISTICAL MODEL; DRUG DEVELOPMENT; HIGH THROUGHPUT SCREENING; PRECLINICAL STUDY; PROCEDURES;

DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; HIGH-THROUGHPUT SCREENING ASSAYS;

EID: 85013780696     PISSN: None     EISSN: 17406749     Source Type: Journal    
DOI: 10.1016/j.ddtec.2016.11.004     Document Type: Review

References (18)

1. Hughes, J.P., Rees, S., Kalindjian, S.B., Philpott, K.L., Principles of early drug discovery. Br J Pharmacol 162 (2011), 1239–1249.
2. Wassermann, A.M., Lounkine, E., Hoepfner, D., Le Goff, G., King, F.J., Studer, C., et al. Dark chemical matter as a promising starting point for drug lead discovery. Nat Chem Biol 11 (2015), 958–966.
3. Edwards, A.M., Isserlin, R., Bader, G.D., Frye, S.V., Willson, T.M., Yu, F.H., Too many roads not taken. Nature 470 (2011), 163–165.
4. Rask-Andersen, M., Almén, M.S., Schiöth, H.B., Trends in the exploitation of novel drug targets. Nat Rev Drug Discov 10 (2011), 579–590.
5. Muegge, I., Mukherjee, P., Performance of dark chemical matter in high throughput screening. J Med Chem 59 (2016), 9806–9813.
6. Wang, Y., Suzek, T., Zhang, J., Wang, J., He, S., Cheng, T., et al. PubChem BioAssay: 2014 update. Nucleic Acids Res 42 (2014), D1075–D1082.
7. Rix, U., Hantschel, O., Dürnberger, G., Remsing Rix, L.L., Planyavsky, M., Fernbach, N.V., et al. Chemical proteomic profiles of the BCR-ABL inhibitors imatinib, nilotinib, and dasatinib reveal novel kinase and nonkinase targets. Blood 110 (2007), 4055–4063.
8. Annis, D.A., Nickbarg, E., Yang, X., Ziebell, M.R., Whitehurst, C.E., Affinity selection-mass spectrometry screening techniques for small molecule drug discovery. Curr Opin Chem Biol 11 (2007), 518–526, 10.1016/j.cbpa.2007.07.011.
9. Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313 (2006), 1929–1935.
10. Nigsch, F., Hutz, J., Cornett, B., Selinger, D.W., McAllister, G., Bandyopadhyay, S., et al. Determination of minimal transcriptional signatures of compounds for target prediction. EURASIP J Bioinform Syst Biol, 2012, 2012, 2.
11. Peck, D., Crawford, E.D., Ross, K.N., Stegmaier, K., Golub, T.R., Lamb, J., A method for high-throughput gene expression signature analysis. Genome Biol, 7, 2006, R61.
12. De Wolf, H., De Bondt, A., Turner, H., Göhlmann, H.W., Transcriptional characterization of compounds: lessons learned from the public LINCS data. Assay Drug Dev Technol 14 (2016), 252–260.
13. Wang, Y., Cornett, A., King, F.J., Mao, Y., Nigsch, F., Paris, C.G., et al. Evidence-based and quantitative prioritization of tool compounds in phenotypic drug discovery. Cell Chem Biol 23 (2016), 862–874, 10.1016/j.chembiol.2016.05.016.
14. Reisen, F., Sauty de Chalon, A., Pfeifer, M., Zhang, X., Gabriel, D., Selzer, P., Linking phenotypes and modes of action through high-content screen fingerprints. Assay Drug Dev Technol 13 (2015), 415–427.
15. Wawer, M.J., Li, K., Gustafsdottir, S.M., Ljosa, V., Bodycombe, N.E., Marton, M.A., et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc Natl Acad Sci U S A 111 (2014), 10911–10916.
16. McCarthy, A., The NIH Molecular Libraries Program: identifying chemical probes for new medicines. Chem Biol 17 (2010), 549–550.
17. Arrowsmith, C.H., Audia, J.E., Austin, C., Baell, J., Bennett, J., Blagg, J., et al. The promise and peril of chemical probes. Nat Chem Biol 11 (2015), 536–541.
18. Mahoney, M.W., Drineas, P., CUR matrix decompositions for improved data analysis. Proc Natl Acad Sci U S A 106 (2009), 697–702.