Das Totan, Bhattacharya Arijit, Jha Tarun, Gayen Shovanlal
Department of Pharmaceutical Technology, Laboratory of Drug Design and Discovery, Jadavpur University, Kolkata, 700032, India.
Department of Pharmaceutical Technology, Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Jadavpur University, Kolkata, 700032, India.
Curr Comput Aided Drug Des. 2025;21(3):270-284. doi: 10.2174/0115734099282303240126061624.
Histone deacetylase 9 (HDAC9) is an important member of the class IIa family of histone deacetylases. It is well established that over-expression of HDAC9 causes various types of cancers including gastric cancer, breast cancer, ovarian cancer, liver cancer, lung cancer, lymphoblastic leukaemia, etc. The important role of HDAC9 is also recognized in the development of bone, cardiac muscles, and innate immunity. Thus, it will be beneficial to find out the important structural attributes of HDAC9 inhibitors for developing selective HDAC9 inhibitors with higher potency.
The classification QSAR-based methods namely Bayesian classification and recursive partitioning method were applied to a dataset consisting of HADC9 inhibitors. The structural features strongly suggested that sulphur-containing compounds can be a good choice for HDAC9 inhibition. For this reason, these models were applied further to screen some natural compounds from Allium sativum. The screened compounds were further accessed for the ADME properties and docked in the homology-modelled structure of HDAC9 in order to find important amino acids for the interaction. The best-docked compound was considered for molecular dynamics (MD) simulation study.
The classification models have identified good and bad fingerprints for HDAC9 inhibition. The screened compounds like ajoene, 1,2 vinyl dithiine, diallyl disulphide and diallyl trisulphide had been identified as compounds having potent HDAC9 inhibitory activity. The results from ADME and molecular docking study of these compounds show the binding interaction inside the active site of the HDAC9. The best-docked compound ajoene shows satisfactory results in terms of different validation parameters of MD simulation study.
This modelling study has identified the natural potential lead (s) from . Specifically, the ajoene with the best features can be considered for further and investigation to establish as potential HDAC9 inhibitors.
组蛋白去乙酰化酶9（HDAC9）是组蛋白去乙酰化酶IIa类家族的重要成员。已明确HDAC9的过表达会导致多种类型的癌症，包括胃癌、乳腺癌、卵巢癌、肝癌、肺癌、淋巴细胞白血病等。HDAC9在骨骼、心肌和先天免疫的发育中也发挥着重要作用。因此，找出HDAC9抑制剂的重要结构特征对于开发更高效的选择性HDAC9抑制剂将是有益的。
基于分类定量构效关系的方法，即贝叶斯分类法和递归划分法，应用于由HADC9抑制剂组成的数据集。结构特征强烈表明含硫化合物可能是抑制HDAC9的良好选择。因此，这些模型被进一步应用于筛选大蒜中的一些天然化合物。对筛选出的化合物进一步评估其药物代谢动力学性质，并将其对接至HDAC9的同源建模结构中，以找出相互作用的重要氨基酸。对对接效果最佳的化合物进行分子动力学（MD）模拟研究。
分类模型已识别出HDAC9抑制的优劣指纹。筛选出的化合物如阿霍烯、1,2-乙烯基二硫醚、二烯丙基二硫化物和二烯丙基三硫化物已被鉴定为具有强大HDAC9抑制活性的化合物。这些化合物的药物代谢动力学和分子对接研究结果显示了它们在HDAC9活性位点内的结合相互作用。对接效果最佳的化合物阿霍烯在MD模拟研究的不同验证参数方面显示出令人满意的结果。
本建模研究已从……中识别出天然潜在先导物。具体而言，具有最佳特征的阿霍烯可考虑进一步进行……和……研究，以确立其作为潜在HDAC9抑制剂的地位。
