Protein kinases have emerged as one of the most successful families of drug targets. To date, most selective kinase inhibitors have been discovered serendipitously either through broad selectivity screening or through the discovery of unique binding modes. Here we discuss design strategies that could lead to a broader coverage of the kinome with selective inhibitors and to a more rational approach for developing them.
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References
Knighton, D.R. et al. Science 253, 414–420 (1991).
Fedorov, O., Muller, S. & Knapp, S. Nat. Chem. Biol. 6, 166–169 (2010).
Knapp, S. & Sundstrom, M. Curr. Opin. Pharmacol. 17, 58–63 (2014).
Yang, J. et al. Chem. Biol. 18, 177–186 (2011).
Fabian, M.A. et al. Nat. Biotechnol. 23, 329–336 (2005).
Bantscheff, M. et al. Nat. Biotechnol. 25, 1035–1044 (2007).
Davis, M.I. et al. Nat. Biotechnol. 29, 1046–1051 (2011).
Zhao, Z. et al. ACS Chem. Biol. 9, 1230–1241 (2014).
Debreczeni, J.E. et al. Angew. Chem. Int. Edn. Engl. 45, 1580–1585 (2006).
Koeberle, S.C. et al. Nat. Chem. Biol. 8, 141–143 (2012).
Gammons, M.V. et al. Invest. Ophthalmol. Vis. Sci. 54, 6052–6062 (2013).
Liu, Q. et al. Chem. Biol. 20, 146–159 (2013).
Woyach, J.A. et al. N. Engl. J. Med. 370, 2286–2294 (2014).
Schindler, T. et al. Science 289, 1938–1942 (2000).
Pike, A.C. et al. EMBO J. 27, 704–714 (2008).
Hill, Z.B., Perera, B.G., Andrews, S.S. & Maly, D.J. ACS Chem. Biol. 7, 487–495 (2012).
Axten, J.M. et al. J. Med. Chem. 55, 7193–7207 (2012).
Wood, E.R. et al. Cancer Res. 64, 6652–6659 (2004).
Di Paolo, J.A. et al. Nat. Chem. Biol. 7, 41–50 (2011).
Guimarães, C.R. et al. J. Chem. Inf. Model. 51, 1199–1204 (2011).
Chaikuad, A. et al. Nat. Chem. Biol. 10, 853–860 (2014).
Zhang, J. et al. Nature 463, 501–506 (2010).
Hindie, V. et al. Nat. Chem. Biol. 5, 758–764 (2009).
Yap, T.A. et al. J. Clin. Oncol. 29, 4688–4695 (2011).
Ciceri, P. et al. Nat. Chem. Biol. 10, 305–312 (2014).
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Müller, S., Chaikuad, A., Gray, N. et al. The ins and outs of selective kinase inhibitor development. Nat Chem Biol 11, 818–821 (2015). https://doi.org/10.1038/nchembio.1938
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DOI: https://doi.org/10.1038/nchembio.1938
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