School of Pharmacy, Department of Pharmacology, Anurag University, Hyderabad, Telangana, India.
School of Pharmacy, Department of Pharmaceutical Analysis, Anurag University, Hyderabad, Telangana, India.
Adv Exp Med Biol. 2023;1424:233-240. doi: 10.1007/978-3-031-31982-2_26.
In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.
为了开发治疗阿尔茨海默病的治疗药物，我们收集了一系列已经具有乙酰胆碱酯酶（AChE）抑制活性的黄酮类类似物。对于每个分子，我们还收集了生物活性数据（Ki）。然后，开发了 3D-QSAR（定量构效关系模型），其标准统计参数 r2 和 q2 代表了可接受的预测和描述能力。该 SAR 数据可以解释与 AChE 抑制活性相关的关键描述符。使用 QSAR 模型，基于该模型开发了药效团，在此基础上进行了虚拟筛选，并获得了一个数据集，作为预测集加载以拟合开发的 QSAR 模型。符合 QSAR 模型的前 10 种化合物进行了分子对接。发现 CHEMBL1718051 是先导化合物。本研究为优先考虑和发现可能的 AChE 抑制剂提供了一个计算驱动的工具示例。此外，体内和体外测试将显示其治疗潜力。
