Deokar Hemantkumar, Deokar Mrunalini, Wang Wei, Zhang Ruiwen, Buolamwini John K
Department of Pharmaceutical Sciences, College of Pharmacy, Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, 60064.
Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, Tennessee, 38163.
Med Chem Res. 2018 Dec;27(11-12):2466-2481. doi: 10.1007/s00044-018-2250-5. Epub 2018 Oct 3.
We have discovered a new class of pyrido[]bindole derivatives that show potent and broad spectrum anticancer activity with IC values down to submicromolar levels. Structure-activity relationship data acquired with the compounds as antiproliferative agents against several cancer cell lines, i.e. human HCT116 colon cancer cell line, and HPAC, Mia-PaCa2 and Panc-1 pancreatic cancer cell lines, were subjected to two different QSAR modeling methods. A kernel-based partial least squares (KPLS) regression analysis with chemical 2D fingerprint descriptors, and a PHASE pharmacophore alignment with 3D-QSAR study. The KPLS method afforded successful predictive QSAR models for antiproliferative activity of the HCT116 colon cell line and on two of the pancreatic cancer cell lines HPAC and Mia-PaCa2, with the following statistics: s of 0.99, 0.99 and 0.98, for training set coefficients of determination, and external test set predictive s of 0.70, 0.58 and 0.70, respectively. The best 2D fingerprint descriptor for both the HCT116 and HPAC data out of the eight finger prints utilized was the atom triplet fingerprint; whereas the one that worked best for the Mia-PaCa2 data was the linear fingerprint descriptor. The PHASE pharmacophore based 3D-QSAR study afforded a four-point pharmacophore model comprising one hydrogen bond donor (D) and three ring (R) elements, which yielded a successful 3D-QSAR model only with the HCT116 cell line data with training set of 0.683, and an external test set predictive of 0.562. With the PHASE 3D-QSAR, the influence of electronic effects and hydrophobicity were visualized, and were in agreement with the observed SAR of substitutions, while the KPLS method the relative extent of contribution of each atom in a compound to the activity. These models will foster the lead optimization process for this potent series of anticancer pyrido [3,4-b]indole compounds.
我们发现了一类新的吡啶并[]吲哚衍生物，它们表现出强效且广谱的抗癌活性，其半数抑制浓度（IC）值低至亚微摩尔水平。以这些化合物作为抗增殖剂针对几种癌细胞系（即人HCT116结肠癌细胞系以及HPAC、Mia-PaCa2和Panc-1胰腺癌细胞系）获取的构效关系数据，被应用于两种不同的定量构效关系（QSAR）建模方法。一种是使用化学二维指纹描述符的基于核的偏最小二乘法（KPLS）回归分析，另一种是与三维定量构效关系研究相结合的PHASE药效团比对。KPLS方法为HCT116结肠癌细胞系以及两种胰腺癌细胞系HPAC和Mia-PaCa2的抗增殖活性提供了成功的预测QSAR模型，统计数据如下：训练集决定系数的R²分别为0.99、0.99和0.98，外部测试集预测的R²分别为0.70、0.58和0.70。在所使用的八个指纹中，对于HCT116和HPAC数据而言，最佳的二维指纹描述符是原子三联体指纹；而对于Mia-PaCa2数据效果最佳的是线性指纹描述符。基于PHASE药效团的三维定量构效关系研究得到了一个包含一个氢键供体（D）和三个环（R）元素的四点药效团模型，该模型仅利用HCT116细胞系数据得到了一个成功的三维定量构效关系模型，训练集的R²为0.683，外部测试集预测的R²为0.562。通过PHASE三维定量构效关系，电子效应和疏水性的影响得以可视化，并且与观察到的取代构效关系一致，而KPLS方法则显示了化合物中每个原子对活性贡献的相对程度。这些模型将促进对这一系列强效抗癌吡啶并[3,4-b]吲哚化合物的先导优化过程。
