一些非小细胞肺癌治疗药物的二维定量构效关系、分子对接、类药性及药物代谢动力学/药物代谢性质预测

2D-QSAR, molecular docking, drug-likeness, and ADMET/pharmacokinetic predictions of some non-small cell lung cancer therapeutic agents.

作者信息

Ibrahim M T, Uzairu A

机构信息

Computational and Theoretical Chemistry, Department of Chemistry, Faculty of Physical Science, Ahmadu Bello University, Zaria, Kaduna State, Nigeria.

出版信息

J Taibah Univ Med Sci. 2022 Sep 16;18(2):295-309. doi: 10.1016/j.jtumed.2022.09.002. eCollection 2023 Apr.

OBJECTIVES

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with nearly 2 million diagnoses and a 17% 5-year survival rate. The aim of this study was to use computer-aided techniques to identify potential therapeutic agents for NSCLC.

METHODS

The two dimensional-quantitative structure-activity relationship (2D-QSAR) modeling was employed on some potential NSCLC therapeutic agents to develop a highly predictive model. Molecular docking-based virtual screening were conducted on the same set of compounds to identify potential hit compounds. The pharmacokinetic features of the best hits were evaluated using SWISSADME and pkCSM online web servers, respectively.

RESULTS

The model generated via 2D-QSAR modeling was highly predictive with R= 0.798, Radj = 0.754, QCV = 0.673, R test = 0.531, and cRp = 0.627 assessment parameters. Molecular docking-based virtual screening identified compounds 25, 32, 15, 21, and 23 with the highest MolDock scores as the best hits, of which compound 25 had the highest MolDock score of -138.329 kcal/mol. All of the identified hits had higher MolDock scores than the standard drug (osimertinib). The best hit compounds were ascertained to be drug-like in nature following the Lipinski's rule of five. Also, their ADMET features displayed average pharmacokinetic profiles.

CONCLUSION

After successful preclinical testing, the hit compounds identified in this study may serve as potential NSCLC therapeutic agents due to their safety and efficacy with the exception of compound 23, which was found to be toxic. They can also serve as a template for designing novel NSCLC therapeutic agents.

目的

非小细胞肺癌（NSCLC）是最常见的肺癌类型，每年有近200万例诊断病例，5年生存率为17%。本研究旨在使用计算机辅助技术识别NSCLC的潜在治疗药物。

方法

对一些潜在的NSCLC治疗药物进行二维定量构效关系（2D-QSAR）建模，以建立一个具有高度预测性的模型。对同一组化合物进行基于分子对接的虚拟筛选，以识别潜在的命中化合物。分别使用SWISSADME和pkCSM在线网络服务器评估最佳命中化合物的药代动力学特征。

结果

通过2D-QSAR建模生成的模型具有高度预测性，评估参数R = 0.798、Radj = 0.754、QCV = 0.673、R test = 0.531和cRp = 0.627。基于分子对接的虚拟筛选确定化合物25、32、15、21和23的MolDock分数最高，为最佳命中化合物，其中化合物25的MolDock分数最高，为-138.329 kcal/mol。所有确定的命中化合物的MolDock分数均高于标准药物（奥希替尼）。根据Lipinski的五规则，确定最佳命中化合物在性质上类似药物。此外，它们的ADMET特征显示出平均药代动力学特征。