Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.
H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.
PLoS One. 2018 Dec 5;13(12):e0200502. doi: 10.1371/journal.pone.0200502. eCollection 2018.
Identification of hotspot drug-receptor interactions through in-silico prediction methods (Pharmacophore mapping, virtual screening, 3DQSAR, etc), is considered as a key approach in drug designing and development process. In the current design study, advanced in-silico based computational techniques were used for the identification of lead-like molecules against the targeted receptor β-glucuronidase. The binding pattern of a potent inhibitor in the ligand-receptor X-ray co-crystallize complex was used to identify and extract the structure-base Pharmacophore features. Based on these observations; five structure-based pharmacophore models were derived to conduct the virtual screening of ICCBS in-house data-base. Top-ranked identified Hits (33 compounds) were selected to subject for in-vitro biological activity evaluation against β-glucuronidase enzyme; out of them, twenty compounds (61% of screened compounds) evaluated as actives, however eleven compounds were found to have significantly higher inhibitory activity, including compounds 1, 5-8, 10, 12-13, and 17-19 with IC50 values ranging from 1.2 μM to 34.9 μM. Out of the eleven potent inhibitors, seven compounds 1, 5, 6, 7, 8, 13, and 19 were found new, and evaluated first time for the β-glucuronidase inhibitory activity. Compounds 1, 5 and 19 exhibited a highly potent inhibition in uM of β-glucuronidase enzyme with non-cytotoxic behavior against the mouse fibroblast (3T3) cell line. Our combined in-silico and in-vitro results revealed that the binding pattern analysis of the eleven potent inhibitors, showed almost similar non-covalent interactions, as observed in case of our validated pharmacophore model. The obtained results thus demonstrated that the virtual screening minimizes false positives, and provide a template for the identification and development of new and more potent β-glucuronidase inhibitors with non-toxic effects.
通过基于计算机的预测方法（药效团映射、虚拟筛选、3DQSAR 等）识别热点药物-受体相互作用，被认为是药物设计和开发过程中的关键方法。在当前的设计研究中，使用先进的基于计算机的计算技术来鉴定针对靶向受体β-葡萄糖醛酸酶的类先导分子。从配体-受体 X 射线共晶复合物的强抑制剂的结合模式中，确定并提取基于结构的药效团特征。基于这些观察结果；衍生了五个基于结构的药效团模型，用于对 ICCBS 内部数据库进行虚拟筛选。排名靠前的鉴定命中物（33 种化合物）被选为β-葡萄糖醛酸酶酶体外生物活性评估的候选物；其中，20 种化合物（筛选化合物的 61%）被评估为活性化合物，然而有 11 种化合物被发现具有显著更高的抑制活性，包括化合物 1、5-8、10、12-13 和 17-19，其 IC50 值范围为 1.2 μM 至 34.9 μM。在 11 种强效抑制剂中，有 7 种化合物 1、5、6、7、8、13 和 19 是新的，并且首次评估了它们对β-葡萄糖醛酸酶抑制活性的作用。化合物 1、5 和 19 对β-葡萄糖醛酸酶表现出高度有效的抑制作用，对小鼠成纤维细胞（3T3）细胞系无细胞毒性。我们的计算机和体外综合结果表明，11 种强效抑制剂的结合模式分析显示出几乎相似的非共价相互作用，就像我们验证的药效团模型一样。获得的结果表明，虚拟筛选可以最大限度地减少假阳性，并为识别和开发具有非毒性作用的新型和更有效的β-葡萄糖醛酸酶抑制剂提供模板。
