Combining properties of different classes of PI3Kα inhibitors to understand the molecular features that confer selectivity

BIOCHEMICAL JOURNAL(2017)

引用 6|浏览33
暂无评分
摘要
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. PI3K alpha 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 PI3K alpha 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 mu M for PI3K alpha yet were selective for PI3K alpha over the other Class I isoforms (beta, delta and gamma). 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 similar to 30-fold. Cell signalling analysis showed that some of the new PI3K alpha 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 PI3K alpha-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 PI3K alpha inhibitors.
更多
查看译文
关键词
drug discovery and design,medicinal chemistry,molecular docking,phosphoinositide 3-kinase,signalling
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要