基于异噁唑的血管内皮生长因子受体 2 抑制剂的理性药物发现。

Shital M Patil, Indrani Mahadik, Shashikant V Bhandari, Kalyani D Asgaonkar, Vrushali D Randive, Aishwarya M Edake
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摘要

背景:抑制受体-酪氨酸激酶(RTK)信号通路已成为新型癌症疗法开发的重点。血管内皮生长因子受体(VEGFR)是 RTK 家族的成员,是血管生成和血管生成所必需的。由于血管内皮生长因子受体 2 是负责细胞血管生成和血管生成的亚型,因此阻断它将损害肿瘤细胞的血液供应,减少其发育、增殖和转移:本研究的目的是利用 QSAR 研究获得 VEGFR2 抑制剂的优化药代动力学。这有助于确定新化学实体(NCE)结构与活性之间的联系:材料和方法:使用 QSARINS v.2.2.4 程序,利用多线性回归方法(MLR)生成 QSAR 模型:对于二维 QSAR,所生成的最佳模型的相关系数为 R2= 0.9396。三维 QSAR 模型的 R2= 0.9121 和 Q2= 0.8377。考虑到对接观察、药理行为和毒性分析,大多数衍生物都具有抑制 VEGFR2 的能力:结论:根据 QSAR 研究,与异噁唑相连的苯环上更多的电子供能基团是活性的必要条件。在分子对接研究中,大多数化合物与典型药物中的关键氨基酸 Cys:919、Asp:1046 和 Glu:885 具有更强的亲和力。所有 NCE 都通过了利平斯基筛选。
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Rational Drug Discovery for Isoxazole Based VEGFR2 Inhibition.

Background: Inhibiting receptor-tyrosine-kinase (RTK) signalling pathways has emerged as a key focus of novel cancer therapy development. Vascular endothelial growth factor receptor (VEGFR) is a member of the RTK family and is required for vasculogenesis and angiogenesis. Because VEGFR 2 is the subtype responsible for cellular angiogenesis and vasculogenesis, blocking it will impair tumour cell blood supply, reducing their development, proliferation, and metastasis.

Aim & objective: The aim of this study is to obtain an optimised pharmacophore as a VEGFR2 inhibitor using QSAR investigations. This aids in determining the link between structure and activity in new chemical entities (NCEs).

Materials and methods: The multi-linear regression approach (MLR) method was utilised to generate the QSAR Model using the programme QSARINS v.2.2.4.

Results and discussion: For 2D QSAR, the best models produced has correlation coefficients of R2= 0.9396. The 3D-QSAR model obtained with R2= 0.9121 and Q2 = 0.8377. Taking docking observations, pharmacological behaviour, and toxicity analyses into account, most of the derivatives demonstrated VEGFR2 inhibitory competence.

Conclusion: According to QSAR studies, more electron-donating groups on the benzene ring linked to the isoxazole were shown to be necessary for activity. In molecular docking studies, most compounds have shown stronger affinity for the crucial amino acids Cys:919, Asp:1046, and Glu:885, which are found in typical drugs. All NCEs passed the Lipinski screening.

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