Ligand-based drug design of quinazolin-4(3H)-ones as breast cancer inhibitors using QSAR modeling, molecular docking, and pharmacological profiling.

IF 2.1 Q3 ONCOLOGY
Sagiru Hamza Abdullahi, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba, Abdullahi Bello Umar
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引用次数: 1

Abstract

Background: Breast cancer is the most common tumor among females globally. Its prevalence is growing around the world, and it is alleged to be the leading cause of cancer death. Approved anti-breast cancer drugs display several side effects and resistance during the early treatment stage. Hence, there is a need for the development of more effective and safer drugs. This research was aimed at designing more potent quinazolin-4(3H)-one molecules as breast cancer inhibitors using a ligand-based design approach, studying their modes of interaction with the target enzyme using molecular docking simulation, and predicting their pharmacological properties.

Methods: The QSAR model was developed using a series of quinazoline-4(3H)-one derivatives by utilizing Material Studio v8.0 software and validated both internally and externally. Applicability domain virtual screening was utilized in selecting the template molecule, which was structurally modified to design more potent molecules. The inhibitive capacities of the design molecules were predicted using the developed model. Furthermore, molecular docking was performed with the EGFR target active site residues, which were obtained from the protein data bank online server (PDB ID: 2ITO) using Molegro Virtual Docker (MVD) software. SwissADME and pkCSM online sites were utilized in predicting the pharmacological properties of the designed molecules.

Results: Four QSAR models were generated, and the first model was selected due to its excellent internal and external statistical parameters as follows: R2 = 0.919, R2adj = 0.898, Q2cv = 0.819, and R2pred = 0.7907. The robustness of the model was also confirmed by the result of the Y-scrambling test performed with cR2p = 0.7049. The selected model was employed to design seven molecules, with compound 4 (pIC50 = 5.18) adopted as the template. All the designed compounds exhibit better activities ranging from pIC50 = 5.43 to 5.91 compared to the template and Doruxybucin (pIC50 = 5.35). The results of molecular docking revealed better binding with the EGFR target compared with the template and Doruxybucin. The designed compounds exhibit encouraging therapeutic applicability, as evidenced by the findings of pharmacological property prediction.

Conclusions: The designed derivatives could be utilized as novel anti-breast cancer agents.

基于配体的喹唑啉-4(3H)- 1乳腺癌抑制剂的QSAR建模、分子对接和药理学分析
背景:乳腺癌是全球女性中最常见的肿瘤。它在世界各地的流行率正在上升,据称它是癌症死亡的主要原因。经批准的抗乳腺癌药物在早期治疗阶段显示出一些副作用和耐药性。因此,有必要开发更有效和更安全的药物。本研究旨在利用基于配体的设计方法设计更有效的喹唑啉-4(3H)- 1分子作为乳腺癌抑制剂,利用分子对接模拟研究它们与靶酶的相互作用模式,并预测它们的药理学性质。方法:利用Material Studio v8.0软件建立一系列喹唑啉-4(3H)- 1衍生物的QSAR模型,并进行内外验证。利用适用性域虚拟筛选技术筛选模板分子,并对模板分子进行结构修饰,设计出更有效的模板分子。利用所建立的模型预测了设计分子的抑制能力。此外,利用Molegro Virtual Docker (MVD)软件与从蛋白质数据库在线服务器(PDB ID: 2ITO)获取的EGFR靶活性位点残基进行分子对接。利用SwissADME和pkCSM在线网站预测所设计分子的药理学性质。结果:共生成4个QSAR模型,第1个模型因其内外统计参数均较优,R2 = 0.919, R2 = 0.898, Q2cv = 0.819, R2pred = 0.7907。采用cR2p = 0.7049进行y置乱检验,也证实了模型的稳健性。利用选取的模型设计7个分子,以化合物4 (pIC50 = 5.18)为模板。与模板和多鲁西布星(pIC50 = 5.35)相比,所设计的化合物在pIC50 = 5.43 ~ 5.91范围内表现出更好的活性。分子对接结果显示,与模板和多鲁西布星相比,与EGFR靶点的结合更好。所设计的化合物表现出令人鼓舞的治疗适用性,正如药理学性质预测的发现所证明的那样。结论:所设计的衍生物可作为新型抗乳腺癌药物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.50
自引率
0.00%
发文量
46
审稿时长
11 weeks
期刊介绍: As the official publication of the National Cancer Institute, Cairo University, the Journal of the Egyptian National Cancer Institute (JENCI) is an open access peer-reviewed journal that publishes on the latest innovations in oncology and thereby, providing academics and clinicians a leading research platform. JENCI welcomes submissions pertaining to all fields of basic, applied and clinical cancer research. Main topics of interest include: local and systemic anticancer therapy (with specific interest on applied cancer research from developing countries); experimental oncology; early cancer detection; randomized trials (including negatives ones); and key emerging fields of personalized medicine, such as molecular pathology, bioinformatics, and biotechnologies.
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