合成吲哚基磺酰胺衍生物,作为α-葡萄糖苷酶和α-淀粉酶的强效抑制剂用于治疗 II 型糖尿病

IF 2.218 Q2 Chemistry
Wasi Ullah , Fazal Rahim , Shawkat Hayat , Hayat Ullah , Muhammad Taha , Shoaib Khan , Amena Khaliq , Saba Bibi , Osama Gohar , Naveed Iqbal , Syed Adnan Ali Shah , Khalid Mohammed Khan
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引用次数: 0

摘要

我们合成了吲哚基磺酰胺类衍生物(1-10),通过核磁共振和 HR-EIMS 对其进行了表征,并针对α-葡萄糖苷酶和α-淀粉酶进行了筛选。与标准阿卡波糖(IC50 = 38.45 ± 0.10 µM 和 1.70 ± 0.10 µM)相比,所有合成的类似物都显示出不同程度的抑制潜力,对α-葡萄糖苷酶的抑制潜力在 1.10 ± 0.10 至 10.90 ± 0.20 µM之间,对α-淀粉酶的抑制潜力在 0.70 ± 0.10 至 11.30 ± 0.20 µM之间。在这两种情况下,类似物 5(IC50 = 1.10 ± 0.10 和 0.40 ± 0.10 μM)和 8(IC50 = 1.20 ± 0.10 和 0.70 ± 0.10 μM)被认为是这一系列中最有效的。已建立的结构-活性关系主要取决于苯环周围的取代模式。通过分子对接研究确定了最有效的类似物与酶活性位点的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthesis of Indole Based Sulfonamide Derivatives as potent inhibitors of α-glucosidase and α-amylase in management of type-II diabetes

We have synthesized indole-based sulfonamides derivatives (1–10), characterized through NMR and HR-EIMS, and screened against α-glucosidase and α-amylase enzymes. All the synthesized analogues showed various degrees of inhibitory potential ranging between 1.10 ± 0.10 to 10.90 ± 0.20 µM (against α-glucosidase) and 0.70 ± 0.10 to 11.30 ± 0.20 µM (against α-amylase) as compared to standard acarbose (IC50 = 38.45 ± 0.10 µM and 1.70 ± 0.10 μM, respectively). In both cases, analogues 5 (IC50 = 1.10 ± 0.10 and 0.40 ± 0.10 μM) and 8 (IC50 = 1.20 ± 0.10 and 0.70 ± 0.10 μM) were identified as the most potent among the series. A structure-activity relationship has been established, which mainly depends upon the substitution pattern around the phenyl ring. The interaction of the most potent analogs with the active site of enzymes was determined through a molecular docking study.

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来源期刊
Chemical Data Collections
Chemical Data Collections Chemistry-Chemistry (all)
CiteScore
6.10
自引率
0.00%
发文量
169
审稿时长
24 days
期刊介绍: Chemical Data Collections (CDC) provides a publication outlet for the increasing need to make research material and data easy to share and re-use. Publication of research data with CDC will allow scientists to: -Make their data easy to find and access -Benefit from the fast publication process -Contribute to proper data citation and attribution -Publish their intermediate and null/negative results -Receive recognition for the work that does not fit traditional article format. The research data will be published as ''data articles'' that support fast and easy submission and quick peer-review processes. Data articles introduced by CDC are short self-contained publications about research materials and data. They must provide the scientific context of the described work and contain the following elements: a title, list of authors (plus affiliations), abstract, keywords, graphical abstract, metadata table, main text and at least three references. The journal welcomes submissions focusing on (but not limited to) the following categories of research output: spectral data, syntheses, crystallographic data, computational simulations, molecular dynamics and models, physicochemical data, etc.
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