Dessalew Nigus, Bharatam Prasad V
Department of Pharmaceutical Chemistry, School of Pharmacy, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia.
Eur J Med Chem. 2007 Jul;42(7):1014-27. doi: 10.1016/j.ejmech.2007.01.010. Epub 2007 Jan 24.
Selective glycogen synthase kinase 3 (GSK3) inhibition over cyclin dependent kinases such as cyclin dependent kinase 2 (CDK2) and cyclin dependent kinase 4 (CDK4) is an important requirement for improved therapeutic profile of GSK3 inhibitors. The concepts of selectivity and additivity fields have been employed in developing selective CoMFA models for these related kinases. Initially, sets of three individual CoMFA models were developed, using 36 compounds of bisarylmaleimide series to correlate with the GSK3, CDK2 and CDK4 inhibitory potencies. These models showed a satisfactory statistical significance: CoMFA-GSK3 (r(2)(con), r(2)(cv): 0.931, 0.519), CoMFA-CDK2 (0.937, 0.563), and CoMFA-CDK4 (0.892, 0.725). Three different selective CoMFA models were then developed using differences in pIC(50) values. These three models showed a superior statistical significance: (i) CoMFA-Selective1 (r(2)(con), r(2)(cv): 0.969, 0.768), (ii) CoMFA-Selective 2 (0.974, 0.835) and (iii) CoMFA-Selective3 (0.963, 0.776). The selective models were found to outperform the individual models in terms of the quality of correlation and were found to be more informative in pinpointing the structural basis for the observed quantitative differences of kinase inhibition. An in-depth comparative investigation was carried out between the individual and selective models to gain an insight into the selectivity criterion. To further validate this approach, a set of new compounds were designed which show selectivity and were docked into the active site of GSK3, using FlexX based incremental construction algorithm.
相对于细胞周期蛋白依赖性激酶(如细胞周期蛋白依赖性激酶2(CDK2)和细胞周期蛋白依赖性激酶4(CDK4)),选择性抑制糖原合酶激酶3(GSK3)是改善GSK3抑制剂治疗效果的重要条件。选择性和加和性场的概念已被用于为这些相关激酶开发选择性比较分子力场(CoMFA)模型。最初,使用36种双芳基马来酰亚胺系列化合物与GSK3、CDK2和CDK4的抑制效力进行关联,开发了三组单独的CoMFA模型。这些模型显示出令人满意的统计学意义:CoMFA-GSK3(r(2)(con),r(2)(cv):0.931,0.519),CoMFA-CDK2(0.937,0.563)和CoMFA-CDK4(0.892,0.725)。然后利用pIC(50)值的差异开发了三种不同的选择性CoMFA模型。这三种模型显示出更高的统计学意义:(i)CoMFA-Selective1(r(2)(con),r(2)(cv):0.969,0.768),(ii)CoMFA-Selective 2(0.974,0.835)和(iii)CoMFA-Selective3(0.963,0.776)。发现选择性模型在相关性质量方面优于单独的模型,并且在确定观察到的激酶抑制定量差异的结构基础方面更具信息性。对单独模型和选择性模型进行了深入的比较研究,以深入了解选择性标准。为了进一步验证这种方法,设计了一组具有选择性的新化合物,并使用基于FlexX的增量构建算法将其对接至GSK3的活性位点。