Structure-Activity Relationship Studies on VEGFR2 Tyrosine Kinase Inhibitors for Identification of Potential Natural Anticancer Compounds.

IF 1.9 4区 医学 Q3 CHEMISTRY, MEDICINAL
Meenakshi Verma, Aqib Sarfraz, Inamul Hasan, Prema Gauri Vasudev, Feroz Khan
{"title":"Structure-Activity Relationship Studies on VEGFR2 Tyrosine Kinase Inhibitors for Identification of Potential Natural Anticancer Compounds.","authors":"Meenakshi Verma, Aqib Sarfraz, Inamul Hasan, Prema Gauri Vasudev, Feroz Khan","doi":"10.2174/0115734064247526231129080415","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Over-expression of Vascular Endothelial Growth Factor Receptors (VEGFRs) leads to the hyperactivation of oncogenes. For inhibition of this hyperactivation, the USA Food Drug Administration (FDA) has approved many drugs that show adverse effects, such as hypertension, hypothyroidism, etc. There is a need to discover potent natural compounds that show minimal side effects. In the present study, we have taken structurally diverse known VEGFR2 inhibitors to develop a Quantitative Structure-Activity Relationship (QSAR) model and used this model to predict the inhibitory activity of natural compounds for VEGFR2.</p><p><strong>Methods: </strong>The QSAR model was developed through the forward stepwise Multiple Linear Regression (MLR) method. A developed QSAR model was used to predict the inhibitory activity of natural compounds. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) assessment and molecular docking studies were performed. The binding stability of the natural compounds with VEGFR2 was elucidated through Molecular Dynamics (MD) simulation.</p><p><strong>Results: </strong>The developed QSAR model against VEGFR2 showed the regression coefficient of the training dataset (r<sup>2</sup>) as 0.81 and the external regression coefficient of the test dataset (r2 test) 0.71. Descriptors, viz., electro-topological state of potential hydrogen bonds (maxHBint2, nHBint6), atom types (minssNH), maximum topological distance matrix (SpMAD_Dt), and 2D autocorrelation (ATSC7v), have been identified. Using this model, 14 natural compounds have been selected that have shown inhibitory activity for VEGFR2, of which six natural compounds have been found to possess a strong binding affinity with VEGFR2. In MD simulation, four complexes have shown binding stability up to 50ns.</p><p><strong>Conclusion: </strong>The developed QSAR model has identified 5 conserved activity-inducing physiochemical properties, which have been found to be correlated with the anticancer activity of the nonidentical ligand molecules bound with the VEGFR2 kinase. Lavendustin_A, 3'-O-acetylhamaudol, and arctigenin have been obtained as possible lead natural compounds against the VEGFR2 kinase.</p>","PeriodicalId":18382,"journal":{"name":"Medicinal Chemistry","volume":" ","pages":"646-661"},"PeriodicalIF":1.9000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medicinal Chemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0115734064247526231129080415","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Over-expression of Vascular Endothelial Growth Factor Receptors (VEGFRs) leads to the hyperactivation of oncogenes. For inhibition of this hyperactivation, the USA Food Drug Administration (FDA) has approved many drugs that show adverse effects, such as hypertension, hypothyroidism, etc. There is a need to discover potent natural compounds that show minimal side effects. In the present study, we have taken structurally diverse known VEGFR2 inhibitors to develop a Quantitative Structure-Activity Relationship (QSAR) model and used this model to predict the inhibitory activity of natural compounds for VEGFR2.

Methods: The QSAR model was developed through the forward stepwise Multiple Linear Regression (MLR) method. A developed QSAR model was used to predict the inhibitory activity of natural compounds. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) assessment and molecular docking studies were performed. The binding stability of the natural compounds with VEGFR2 was elucidated through Molecular Dynamics (MD) simulation.

Results: The developed QSAR model against VEGFR2 showed the regression coefficient of the training dataset (r2) as 0.81 and the external regression coefficient of the test dataset (r2 test) 0.71. Descriptors, viz., electro-topological state of potential hydrogen bonds (maxHBint2, nHBint6), atom types (minssNH), maximum topological distance matrix (SpMAD_Dt), and 2D autocorrelation (ATSC7v), have been identified. Using this model, 14 natural compounds have been selected that have shown inhibitory activity for VEGFR2, of which six natural compounds have been found to possess a strong binding affinity with VEGFR2. In MD simulation, four complexes have shown binding stability up to 50ns.

Conclusion: The developed QSAR model has identified 5 conserved activity-inducing physiochemical properties, which have been found to be correlated with the anticancer activity of the nonidentical ligand molecules bound with the VEGFR2 kinase. Lavendustin_A, 3'-O-acetylhamaudol, and arctigenin have been obtained as possible lead natural compounds against the VEGFR2 kinase.

VEGFR2 酪氨酸激酶抑制剂的结构-活性关系研究,以确定潜在的天然抗癌化合物。
背景:血管内皮生长因子受体(VEGFR)的过度表达会导致癌基因的过度激活。为抑制这种过度激活,美国食品药品管理局(FDA)批准了许多药物,但这些药物都会产生不良反应,如高血压、甲状腺机能减退等。我们需要发现副作用最小的强效天然化合物。在本研究中,我们利用结构多样的已知 VEGFR2 抑制剂建立了一个定量结构-活性关系(QSAR)模型,并利用该模型预测天然化合物对 VEGFR2 的抑制活性:方法:通过正向逐步多元线性回归(MLR)方法建立 QSAR 模型。利用建立的 QSAR 模型预测天然化合物的抑制活性。进行了吸收、分布、代谢、排泄和毒性(ADMET)评估和分子对接研究。通过分子动力学(MD)模拟阐明了天然化合物与 VEGFR2 的结合稳定性:结果:针对 VEGFR2 建立的 QSAR 模型显示,训练数据集的回归系数(r2)为 0.81,测试数据集的外部回归系数(r2 pred)为 0.71。已确定的描述符包括潜在氢键的电拓扑状态(maxHBint2, nHBint6)、原子类型(minssNH)、最大拓扑距离矩阵(SpMAD_Dt)和二维自相关性(ATSC7v)。利用该模型,筛选出了 14 种对 VEGFR2 具有抑制活性的天然化合物,并发现其中 6 种天然化合物与 VEGFR2 具有很强的结合亲和力。在 MD 模拟中,四个复合物的结合稳定性高达 10ns:结论:所建立的 QSAR 模型发现了 5 种保守的活性诱导理化性质,这些性质与结合 VEGFR2 激酶的非相同配体分子的抗癌活性相关。结果发现,Lavendustin_A、3'-O-acetylhamaudol 和 arctigenin 可能是抗血管内皮生长因子受体 2 激酶的先导天然化合物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Medicinal Chemistry
Medicinal Chemistry 医学-医药化学
CiteScore
4.30
自引率
4.30%
发文量
109
审稿时长
12 months
期刊介绍: Aims & Scope Medicinal Chemistry a peer-reviewed journal, aims to cover all the latest outstanding developments in medicinal chemistry and rational drug design. The journal publishes original research, mini-review articles and guest edited thematic issues covering recent research and developments in the field. Articles are published rapidly by taking full advantage of Internet technology for both the submission and peer review of manuscripts. Medicinal Chemistry is an essential journal for all involved in drug design and discovery.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信