A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents.

Trupti Chitre, Aniket Bhatambrekar, Purvaj Hirode, Shubhangi Thorat, Sayli G Hajare, Dinesh Garud, Sakshi Jagdale, Kalyani Asgaonkar
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Abstract

Background: A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death.

Material and methods: To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1).

Result: Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening.

Conclusion: The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

结合药效生成、配体虚拟筛选、基于原子的 3D-QSAR 和分子对接研究非布索坦酰胺类化合物作为抗炎药物。
背景:炎症是人体的一种防御机制。它是免疫系统识别、排斥和修复外来破坏性刺激的过程。在世界范围内,慢性炎症性疾病是导致死亡的主要原因:为了获得优化的药效构架,本研究使用 Schrodinger suite 2022-4 对之前报道的非布索坦类抗炎酰胺衍生物系列进行了药效构架假设、配体虚拟筛选和 3D-QSAR 研究。Schrodinger 的 QuikProp 模块用于 ADMET 预测,GLIDE 模块的 HTVS、SP 和 XP 协议用于目标蛋白(PDB ID:3LN1)的分子对接:结果:利用 29 种化合物建立了五点常见药效假说模型,其 pIC50 在 5.34 和 4.871 之间。最重要的药效假说模型 AHHRR_ 1 包含一个氢键受体、两个疏水基团和两个环状取代特征。假设模型 AHHRR_1 使用 Asinex 的分子进行了基于配体的虚拟筛选。此外,还进行了基于单个原子的 3D QSAR 研究,以评估它们对模型开发的贡献。根据 R2 值(0.9531)和 Q2 值(0.9424)选择了最佳 QSAR 模型。最后,在虚拟筛选的基础上通过分子对接获得了四个潜在的命中化合物:虚拟筛选化合物与氨基酸残基的对接作用与标准双氯芬酸钠药物相似。因此,本研究的结果可用于开发强效抗炎药物。
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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