We have discovered a new class of pyrido[b]bindole derivatives that show potent and broad spectrum anticancer activity with IC₅₀ 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, HPAC and Mia-PaCa2 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: R²s of 0.99, 0.99, and 0.98, for training set coefficients of determination, and external test set predictive r²s 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 R² of 0.683, and an external test set predictive r² 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.

中文翻译：

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新型吡啶并[3,4-b]吲哚衍生物作为结肠癌细胞和胰腺癌细胞增殖抑制剂的 QSAR 研究。


我们发现了一类新的吡啶并[ b ]bindole衍生物，其显示出有效且广谱的抗癌活性，IC ₅₀值低至亚微摩尔水平。使用化合物作为针对几种癌细胞系（即人 HCT116 结肠癌细胞系、HPAC 和 Mia-PaCa2 胰腺癌细胞系）的抗增殖剂获得的结构-活性关系数据，采用两种不同的 QSAR 建模方法。使用化学 2D 指纹描述符进行基于核的偏最小二乘 (KPLS) 回归分析，以及与 3D-QSAR 研究的相药效团比对。 KPLS 方法为 HCT116 结肠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：训练时的R ²分别为 0.99、0.99 和 0.98设置确定系数和外部测试集预测r ²分别为 0.70、0.58 和 0.70。在所使用的八个指纹中，HCT116 和 HPAC 数据的最佳 2D 指纹描述符是原子三联指纹；而最适合 Mia-PaCa2 数据的是线性指纹描述符。基于 PHASE 药效团的 3D-QSAR 研究提供了包含一个氢键供体 (D) 和三个环 (R) 元件的四点药效团模型，仅使用具有训练集R 的HCT116 细胞系数据就产生了成功的 3D-QSAR 模型²为 0.683，外部测试集预测r ²为 0.562。 通过 PHASE 3D-QSAR，电子效应和疏水性的影响被可视化，并且与观察到的取代 SAR 一致，而 KPLS 方法则计算了化合物中每个原子对活性的相对贡献程度。这些模型将促进该系列强效抗癌吡啶并[3,4-b]吲哚化合物的先导化合物优化过程。