Postgraduate Program in Biotechnology and Biodiversity-Network BIONORTE, Federal University of the Para, Belem, Brazil.
Laboratory of Modeling and Computational Chemistry, Federal University of Amapa, Department of Biological Sciences. Rod. Juscelino Kubitschek, Km 02, s/n, Jardim Marco Zero, 68902-280 Macapa-AP, Brazil.
Curr Pharm Des. 2018;24(5):576-594. doi: 10.2174/1381612823666170711112510.
About 132 thousand cases of melanoma (more severe type of skin cancer) were registered in 2014 according to the World Health Organization. This type of cancer significantly affects the quality of life of individuals. Caffeine has shown potential inhibitory effect against epithelial cancer. In this study, it was proposed to obtain new caffeine-based molecules with potential epithelial anticancer activity. For this, a training set of 21 molecules was used for pharmacophore perception procedures. Multiple linear regression analyses were used to propose mono-, bi-, tri-, and tetra-parametric models applied in the prediction of the activity. The generated pharmacophore was used to select 350 molecules available at the ZINCpharmer server, followed by reduction to 24 molecules, after selection using the Tanimoto index, yielding 10 molecules after final selection by predicted activity values > 1.5229. These ten molecules had better pharmacokinetic properties than the other ones used as reference and within the clinically significant limits. Only two molecules show minor hits of toxicity and were submitted to molecular docking procedures, showing BFE (binding free energy) values lower than the reference values. Statistical analyses indicated strong negative correlations between BFE and pharmacophoric properties (high influence on BFE lowering) and practically null correlation between BFE and BBB. The two most promising molecules can be indicated as candidates for further in vitro and in vivo analyzes.
根据世界卫生组织的数据，2014 年登记了约 13.2 万例黑色素瘤（一种更严重的皮肤癌）。这种癌症严重影响个人的生活质量。咖啡因已显示出对上皮癌有潜在的抑制作用。在这项研究中，提出了获得具有潜在上皮抗癌活性的新型咖啡因类分子。为此，使用了 21 个分子的训练集进行药效团感知程序。采用多元线性回归分析方法，提出了单、双、三、四参数模型，用于预测活性。生成的药效团用于从 ZINCpharmer 服务器上选择 350 个可用分子，然后使用 Tanimoto 指数进行选择后减少到 24 个分子，最后根据预测活性值>1.5229 进行最终选择，得到 10 个分子。这十个分子具有比其他用作参考的分子更好的药代动力学特性，并且在临床显著范围内。只有两个分子显示出较小的毒性命中，并提交进行分子对接程序，显示出比参考值低的 BFE（结合自由能）值。统计分析表明，BFE 与药效团性质之间存在强烈的负相关（对降低 BFE 有很大影响），而 BFE 与 BBB 之间几乎没有相关性。这两个最有前途的分子可以作为进一步的体外和体内分析的候选物。
