Lung cancer has been reported to be among the leading cancer cases in the world. It was also reported to have caused a lot of death every year and accounted for about one-third of the whole cancer deaths in the globe. The main subset of lung cancers that accounts for about 85% of the problems of lung cancer raised above was non-small cell lung cancer (NSCLC). The most common cause of NSCLCs that mostly affects women and cigarette smokers was recognized to be overexpression of epidermal growth factor receptor tyrosine kinase (EGFR TK). Five models on thirty five (35) NSCLC therapeutic agents were developed via quantitative structure-activity relationship (QSAR) technique. The best model among them was selected and reported due to its fitness statistically with the following validation parameters: R2 of 0.8764, R2adj of 0.8370, Qcv2 of 0.7655, R2test of 0.7024, and LOF of 0.3312. Molecular docking was used to elucidate the mode of binding interactions between the thirty five (35) NSCLC therapeutic agents and the binding pose of EGFR tyrosine kinase receptor (3IKA) in this research. Compound 29 was recognized to have the most excellent binding affinity of − 8.8 kcal/mol among others. The drug-likeness and pharmacokinetic properties of all the NSCLC therapeutic agents were predicted using SWISSADME, and none among the molecules under investigation violated more than the permissible limit of the conditions stated by Lipinski’s RO5 filters. Five hit compounds were identified using molecular docking virtual screening. The five (5) hit compounds were further screened and identified compound 16 and 27 as excellent among them using their pharmacokinetic profiles and drug-likeness properties. QSAR technique was used to build five models on thirty five (35) NSCLC therapeutic agents. The best model among them was reported because it is statistically significant with good validation parameters. The molecular docking result has identified five (5) hit compounds. The most common amino acid residues to all hit compounds under investigation were Glu762, Leu718, Lys745, and Val726 which might be responsible for the higher inhibitory activities/binding affinities of the compounds under investigation. Furthermore, these five (5) hit compounds were further subjected to drug-likeness and pharmacokinetic properties prediction to determine which among them have the best pharmacokinetic profile. Compounds 16 and 27 among the hit compounds were observed to have high chance of passive absorption by the gastrointestinal tract while the other three have low tendency of passive absorption. More so, only compounds 16 and 27 have higher bioavailability scores, and none of the two have more than one violation of the RO5 criteria. The cause of efficiency of compounds 16 and 27 might be as a result of good pharmacokinetic profiles and drug-likeness properties possessed by the molecules when compared to other hit compounds.

中文翻译：

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一些非小细胞肺癌治疗药物的定量构效关系、分子对接、药物相似性和药代动力学研究

据报道，肺癌是世界上主要的癌症病例之一。据报道，它每年造成大量死亡，约占全球癌症死亡总数的三分之一。占上述肺癌问题约 85% 的主要肺癌亚型是非小细胞肺癌 (NSCLC)。主要影响女性和吸烟者的非小细胞肺癌的最常见原因被认为是表皮生长因子受体酪氨酸激酶 (EGFR TK) 的过度表达。通过定量构效关系 (QSAR) 技术开发了关于三十五 (35) 种 NSCLC 治疗剂的五个模型。选择并报告其中最好的模型，因为它具有以下验证参数的统计拟合度：R2 为 0.8764，R2adj 为 0.8370，Qcv2 为 0.7655，R2test 为 0.7024，LOF 为 0.3312。在本研究中，分子对接用于阐明三十五 (35) 种 NSCLC 治疗剂与 EGFR 酪氨酸激酶受体 (3IKA) 的结合姿势之间的结合相互作用模式。其中，化合物 29 被认为具有最优异的结合亲和力，为 - 8.8 kcal/mol。使用 SWISSADME 预测了所有 NSCLC 治疗剂的药物相似性和药代动力学特性，所研究的分子中没有一个违反超过 Lipinski 的 RO5 过滤器规定的条件允许限度。使用分子对接虚拟筛选鉴定了五种命中化合物。进一步筛选了五 (5) 个命中化合物，并使用它们的药代动力学特征和药物相似特性鉴定了其中优异的化合物 16 和 27。QSAR 技术用于在三十五 (35) 种 NSCLC 治疗剂上建立五个模型。报告了其中最好的模型，因为它具有良好的验证参数具有统计意义。分子对接结果已鉴定出五 (5) 个命中化合物。所研究的所有命中化合物最常见的氨基酸残基是 Glu762、Leu718、Lys745 和 Val726，这可能是所研究化合物具有更高抑制活性/结合亲和力的原因。此外，这五 (5) 个命中化合物进一步进行药物相似性和药代动力学特性预测，以确定其中哪些具有最佳的药代动力学特征。观察到命中化合物中的化合物16和27被胃肠道被动吸收的可能性高，而其他三种被动吸收的可能性低。更重要的是，只有化合物 16 和 27 具有更高的生物利用度评分，并且这两种化合物都没有超过一处违反 RO5 标准。化合物 16 和 27 有效的原因可能是与其他命中化合物相比，这些分子具有良好的药代动力学特征和药物相似性。