2D-QSAR study and design of novel pyrazole derivatives as an anticancer lead compound against A-549, MCF-7, HeLa, HepG-2, PaCa-2, DLD-1

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-05-01 DOI:10.1016/j.comtox.2023.100265
Fatima Ezzahra Bennani , Latifa Doudach , Khalid Karrouchi , Youssef El rhayam , Christopher E. Rudd , M'hammed Ansar , My El Abbes Faouzi
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引用次数: 1

Abstract

In this study, a local quantitative structure–activity relationship (QSAR) models were developed for set of compounds tested for their inhibitory activity against six different cancer cell lines viz. A-549, MCF-7, HeLa, HepG-2, PaCa-2 and DLD-1. Two different statistical approaches Principal Component Analysis (PCA) and Partial Least Square (PLS) analyses were employed to developed QSAR models. Further, activity predictions were carried out for in-house synthesized 63 pyrazole derivatives. Prediction of pIC50 value of all 63 synthesized pyrazole derivatives were estimated based on the most significant QSAR model developed for each cancer cell line. Several statistical parameters such as correlation coefficient R2, RMSE, Cross validated R2, Cross validated RMSE, internal validation Q2 and the external validation R2 revealed that developed models showed a significant value for explaining an acceptable QSAR model. The results derived highlighted some important compounds for being the most promise lead candidate against the six-cancer cell line with a significant pIC50 value. Considering the contribution of most important descriptors, we have designed new molecules which found to have greater inhibitory potentiality than the reference compounds. Overall, the results suggest that the developed QSAR models might be useful as a theoretical reference for experimental studies and designing more potent anti-cancer therapeutic pyrazoles based compounds.

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新型吡唑衍生物抗癌先导化合物A-549、MCF-7、HeLa、HepG-2、PaCa-2、DLD-1的2D-QSAR研究与设计
本研究建立了一组化合物的局部定量构效关系(QSAR)模型,测试了它们对6种不同癌细胞系(a- 549、MCF-7、HeLa、HepG-2、PaCa-2和DLD-1)的抑制活性。采用主成分分析(PCA)和偏最小二乘法(PLS)两种不同的统计方法来建立QSAR模型。此外,对内部合成的63种吡唑衍生物进行了活性预测。所有63个合成的吡唑衍生物的pIC50预测值是基于为每个癌细胞系建立的最显著的QSAR模型估计的。相关系数R2、RMSE、交叉验证R2、交叉验证RMSE、内部验证Q2和外部验证R2等统计参数显示,所开发的模型对解释可接受的QSAR模型具有显着价值。结果突出了一些具有显著pIC50值的重要化合物,它们是最有希望的抗六种癌细胞系的主要候选化合物。考虑到大多数重要描述符的贡献,我们设计了比参比化合物具有更大抑制潜力的新分子。综上所述,所建立的QSAR模型可为实验研究和设计更有效的抗癌治疗性吡唑类化合物提供理论参考。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
期刊最新文献
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