Tuğcu Gülçin, Önen Bayram Filiz Esra, Sipahi Hande
Yeditepe University Faculty of Pharmacy, Department of Toxicology, İstanbul, Turkey.
Yeditepe University Faculty of Pharmacy, Department of Pharmaceutical Chemistry, İstanbul, Turkey.
Turk J Pharm Sci. 2021 Dec 31;18(6):738-743. doi: 10.4274/tjps.galenos.2021.54815.
4-(2-fluorophenoxy) quinoline derivatives constitute one of the chemical classes of hepatocyte growth factor receptor (c-MET) inhibitors, a promising treatment against various human tumors. There are three aims of the present study: (1) To develop a robust and validated quantitative structure-activity relationship model to predict the c-Met kinase inhibition; (2) to examine the toxicity profiles of these compounds; (3) to design new quinoline derivatives and apply the developed model on these compounds to observe its pertinence.
A multiple linear regression method was used to develop the model with calculated descriptors. State-of-the-art internal and external validation parameters were calculated. The toxicity profiles including structural alerts and the lowest observed adverse effect level (LOAEL) values were evaluated using online tools. New derivatives were designed and tested on the developed model.
A series of 4-(2-fluorophenoxy) quinoline derivatives was linearly modeled and vigorously validated to predict the molecules' c-MET kinase inhibition potential. Statistical metrics of the developed model showed that it was robust and able to make successful predictions for this chemical class. The mass, electronegativity, partial charges, and the structure of the molecules had an effect on the activity. Moreover, the toxicity profiles of the studied compounds were found to be adequate.
Five of the synthesized compounds were observed to be auspicious for the toxicity/activity ratio. The developed model is useful in the virtual screening and in the design of new anti-tumor compounds.
4-（2-氟苯氧基）喹啉衍生物是肝细胞生长因子受体（c-MET）抑制剂的一类化学物质，是一种有前景的针对多种人类肿瘤的治疗药物。本研究有三个目标：（1）建立一个稳健且经过验证的定量构效关系模型，以预测c-Met激酶抑制作用；（2）研究这些化合物的毒性概况；（3）设计新的喹啉衍生物，并将所建立的模型应用于这些化合物，以观察其相关性。
使用多元线性回归方法，利用计算得到的描述符建立模型。计算了最先进的内部和外部验证参数。使用在线工具评估包括结构警示和最低观察到的不良反应水平（LOAEL）值在内的毒性概况。设计了新的衍生物，并在建立的模型上进行测试。
对一系列4-（2-氟苯氧基）喹啉衍生物进行了线性建模并进行了严格验证，以预测分子的c-MET激酶抑制潜力。所建立模型的统计指标表明，它是稳健的，能够对这类化学物质做出成功的预测。分子的质量、电负性、部分电荷和结构对活性有影响。此外，发现所研究化合物的毒性概况是合适的。
观察到五种合成化合物的毒性/活性比很理想。所建立的模型可用于虚拟筛选和新的抗肿瘤化合物的设计。
