Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Kennedy Road, Pune, Maharashtra, India.
Department of Chemistry, Sir Parashurambhau College, Tilak Road, Pune, Maharashtra, India.
Curr Drug Discov Technol. 2025;22(1):e110324227883. doi: 10.2174/0115701638281229240226101906.
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.
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).
Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC 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 R (0.9531) and Q (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening.
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.
身体的防御机制包括炎症。这是一个免疫系统识别、排斥和开始修复外来和损伤性刺激的过程。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团，本研究中使用 Schrodinger 套件 2022-4 对先前报道的基于非布司他的抗炎酰胺衍生物系列进行了药效团假设、基于配体的虚拟筛选和 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。
利用 29 个化合物，建立了一个具有 5 个点的常见药效团假设模型，pIC 值在 5.34 到 4.871 之间。顶级药效团假设 AHHRR_1 模型由一个氢键受体、两个疏水区和两个环取代特征组成。假设模型 AHHRR_1 经过基于配体的虚拟筛选，使用来自 Asinex 的分子。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 R（0.9531）和 Q（0.9424）的值，选择了最佳 QSAR 模型。最后，通过基于虚拟筛选的分子对接获得了四个潜在的命中物。
虚拟筛选化合物与标准双氯芬酸钠药物显示出类似的与氨基酸残基的对接相互作用。因此，本研究的结果可以在开发有效的抗炎药物中进行探索。
