Abstract

Type II Diabetes Mellitus (DM) is caused by insulin resistance in peripheral tissue and impaired insulin secretion through a dysfunction of the pancreatic β-cell. Acarbose is an anti-DM drug, it is effective but its continuous use may lead to undesirable side effects. Hence, the development of novel drugs from natural source that have both anti-diabetic and anti-oxidant activities, with little or no side effect during long-term use is of great importance. To investigate the anti-DM and anti-oxidant phyto-constituents of Chromoleana odorata, e-pharmacophore model was generated using human pancreatic α–amylase (HPA) standard inhibitor, Acarbose to map important pharmacophoric features of HPA, and used to screen several phyto-constituents of C. odorata to match at least 4 sites of the generated hypothesis. Glide and Induced Fit Docking followed by Prime MM-GBSA calculation, drug-likeness and ADME studies were employed for high fitness (>1.0) compounds retrieved from e-pharmacophore screening process. The drug-likeness properties of the lead compounds, Quercetin and Ombuin were analyzed taking into account the Lipinski’s and Veber’s rules. Further, machine-learning approach was used to generate QSAR model. The computed model, kpls_desc_19 was used to predict the bioactivity (pIC50) of Quercetin and Ombuin. Phyto-constituents of C. odorata; Quercetin and Ombuin have shown better and promising results when compared to that of the standard, acarbose. Based on the present study, orally delivered Quercetin and Ombuin from C. odorata are relatively better inhibitor of HPA, thus they can be a useful therapeutic candidate in the management/treatment of DM when compared to acarbose.

Communicated by Ramaswamy H. Sarma

摘要

II 型糖尿病 (DM) 是由外周组织中的胰岛素抵抗和胰腺 β 细胞功能障碍导致的胰岛素分泌受损引起的。阿卡波糖是一种抗 DM 药物，它是有效的，但其持续使用可能会导致不良副作用。因此，从天然来源中开发具有抗糖尿病和抗氧化活性、长期使用副作用很小或没有副作用的新型药物具有重要意义。为了研究Chromoleana odorata的抗 DM 和抗氧化植物成分，使用人胰腺 α-淀粉酶 (HPA) 标准抑制剂 Acarbose 生成了电子药效团模型，以绘制 HPA 的重要药效团特征，并用于筛选几种植物-C . odorata的成分匹配生成假设的至少 4 个位点。Glide 和 Induced Fit Docking 随后进行 Prime MM-GBSA 计算、药物相似性和 ADME 研究用于从电子药效团筛选过程中检索到的高适应性 (>1.0) 化合物。考虑到 Lipinski 和 Veber 规则，分析了先导化合物、槲皮素和 Ombuin 的药物相似性。此外，机器学习方法用于生成 QSAR 模型。计算模型 kpls_desc_19 用于预测槲皮素和 Ombuin 的生物活性 (pIC50)。C. odorata的植物成分；与标准品阿卡波糖相比，槲皮素和 Ombuin 显示出更好和有希望的结果。在本研究的基础上，口服槲皮素和 Ombuin 来自C. odorata是相对较好的 HPA 抑制剂，因此与阿卡波糖相比，它们可以成为治疗/治疗 DM 的有用候选药物。

由 Ramaswamy H. Sarma 传达