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• W2893375819 startingPage "186" @default.
• W2893375819 abstract "Background: Postprandial hyperglycemia can be reduced by inhibiting major carbohydrate hydrolyzing enzymes, such as α-glucosidase and α-amylase which is an effective approach in both preventing and treating diabetes. Objective: The aim of this study was to synthesize a series of 2,4-dichloro-5-[(N-aryl/alkyl)sulfamoyl] benzoic acid derivatives and evaluate α-glucosidase and α-amylase inhibitory activity along with molecular docking and in silico ADMET property analysis. Method: Chlorosulfonation of 2,4-dichloro benzoic acid followed by reaction with corresponding anilines/amines yielded 2,4-dichloro-5-[(N-aryl/alkyl)sulfamoyl]benzoic acid derivatives. For evaluating their antidiabetic potential α-glucosidase and α-amylase inhibitory assays were carried out. In silico molecular docking studies of these compounds were performed with respect to these enzymes and a computational study was also carried out to predict the drug-likeness and ADMET properties of the title compounds. Results: Compound 3c (2,4-dichloro-5-[(2-nitrophenyl)sulfamoyl]benzoic acid) was found to be highly active having 3 fold inhibitory potential against α-amylase and 5 times inhibitory activity against α-glucosidase in comparison to standard drug acarbose. Conclusion: Most of the synthesized compounds were highly potent or equipotent to standard drug acarbose for inhibitory potential against α-glucosidase and α-amylase enzyme and hence this may indicate their antidiabetic activity. The docking study revealed that these compounds interact with active site of enzyme through hydrogen bonding and different pi interactions." @default.
• W2893375819 created "2018-10-05" @default.
• W2893375819 creator A5046646817 @default.
• W2893375819 creator A5071302238 @default.
• W2893375819 date "2019-02-12" @default.
• W2893375819 modified "2023-09-25" @default.
• W2893375819 title "2,4-Dichloro-5-[(N-aryl/alkyl)sulfamoyl]benzoic Acid Derivatives: In Vitro Antidiabetic Activity, Molecular Modeling and In silico ADMET Screening" @default.
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• W2893375819 doi "https://doi.org/10.2174/1573406414666180924164327" @default.
• W2893375819 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30251608" @default.
• W2893375819 hasPublicationYear "2019" @default.
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