Phosphoinositide 3-kinases (PI3Ks) are major regulators of many cellular functions, and hyperactivation of PI3K cell signalling pathways is a major target for anticancer drug discovery. PI3Kα is the isoform most implicated in cancer, and our aim is to selectively inhibit this isoform, which may be more beneficial than concurrent inhibition of all Class I PI3Ks. We have used structure-guided design to merge high-selectivity and high-affinity characteristics found in existing compounds. Molecular docking, including the prediction of water-mediated interactions, was used to model interactions between the ligands and the PI3Kα affinity pocket. Inhibition was tested using lipid kinase assays, and active compounds were tested for effects on PI3K cell signalling. The first-generation compounds synthesized had IC50 (half maximal inhibitory concentration) values >4 μM for PI3Kα yet were selective for PI3Kα over the other Class I isoforms (β, δ and γ). The second-generation compounds explored were predicted to better engage the affinity pocket through direct and water-mediated interactions with the enzyme, and the IC50 values decreased by ∼30-fold. Cell signalling analysis showed that some of the new PI3Kα inhibitors were more active in the H1047R mutant bearing cell lines SK-OV-3 and T47D, compared with the E545K mutant harbouring MCF-7 cell line. In conclusion, we have used a structure-based design approach to combine features from two different compound classes to create new PI3Kα-selective inhibitors. This provides new insights into the contribution of different chemical units and interactions with different parts of the active site to the selectivity and potency of PI3Kα inhibitors.
Combining properties of different classes of PI3Kα inhibitors to understand the molecular features that confer selectivity
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Grace Q. Gong, Jackie D. Kendall, James M.J. Dickson, Gordon W. Rewcastle, Christina M. Buchanan, William A. Denny, Peter R. Shepherd, Jack U. Flanagan; Combining properties of different classes of PI3Kα inhibitors to understand the molecular features that confer selectivity. Biochem J 1 July 2017; 474 (13): 2261–2276. doi: https://doi.org/10.1042/BCJ20161098
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