Ayoub Lahmadi, Aissam El-Aliani, Yassine Kasmi, Said Elantri, Mohammed El Mzibri, Souad Aboudkhil
Laboratory of Biochemistry, Environment and Agri-Food (URAC 36)-Faculty of sciences and techniques - Mohammedia, Hassan II university Casablanca Morocco.
Green Biotechnology Team, Moroccan Foundation for Advanced Science, Innovation and Research (MAScIR), Mohammadia School of Engineering, Rabat Design Center, Mohammed V University, Morocco.
Bioinformation. 2018 Jul 31;14(7):384-392. doi: 10.6026/97320630014384. eCollection 2018.
and are widely used in traditional medicine for the treatment of cancer. Therefore, it is of interest to develop a QSAR model for screening proteasome inhibitors from plant source. Hence, a QSAR model was developed using multiple linear regressions; partial least squares regression and principal component regression methods. Results of QSAR modeling and docking demonstrate that compounds derived from both plants have great potentiality to be proteasome inhibitors. The developed QSAR model highlights a strong structure-effect relationship. The predicted correlation of comparative molecular field analysis, and comparative molecular similarity indexes are 0.963 and 0.919, respectively. Computed absorption, distribution, metabolism, excretion and toxicity studies on these derivatives showed encouraging results with very low toxicity, distribution and absorption.
[前文提到的两种物质]在传统医学中被广泛用于治疗癌症。因此,开发一种用于从植物来源筛选蛋白酶体抑制剂的定量构效关系(QSAR)模型具有重要意义。为此,使用多元线性回归、偏最小二乘回归和主成分回归方法开发了一个QSAR模型。QSAR建模和对接的结果表明,来自这两种植物的化合物具有成为蛋白酶体抑制剂的巨大潜力。所开发的QSAR模型突出了很强的结构-效应关系。比较分子场分析和比较分子相似性指数的预测相关性分别为0.963和0.919。对这些衍生物进行的计算吸收、分布、代谢、排泄和毒性研究显示出令人鼓舞的结果,其毒性、分布和吸收都非常低。