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Yaghoub Sarrafi

Yaghoub Sarrafi

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Faculty of Chemistry
Address:
Phone: 9121978350

Research

Title
Docking study, molecular dynamic, synthesis, anti-α-glucosidase assessment, and ADMET prediction of new benzimidazole-Schiff base derivatives
Type
JournalPaper
Keywords
Docking study, ADMET prediction, benzimidazole, Schiff base derivatives
Year
2022
Journal Scientific Reports
DOI
Researchers Homa Azizian ، Keyvan Pedrood ، Ali Moazzam ، Yousef Valizadeh ، Kimia Khavaninzadeh ، Ali Zamani ، Mohammad Ali Faramarzi ، Samanesadat Hosseini ، Hossein Adibi ، Bagher Larijani ، Hossein Rastegar ، mohammad mahdavi ، Yaghoub Sarrafi ، Maryam Mohammadi-Khanaposhtani

Abstract

The control of postprandial hyperglycemia is an important target in the treatment of type 2 diabetes mellitus (T2DM). As a result, targeting α-glucosidase as the most important enzyme in the breakdown of carbohydrates to glucose that leads to an increase in postprandial hyperglycemia is one of the treatment processes of T2DM. In the present work, a new class of benzimidazole-Schiff base hybrids 8a–p has been developed based on the potent reported α-glucosidase inhibitors. These compounds were synthesized by sample recantations, characterized by 1H-NMR, 13C-NMR, FT-IR, and CHNS elemental analysis, and evaluated against α-glucosidase. All new compounds, with the exception of inactive compound 8g, showed excellent inhibitory activities (60.1 ± 3.6–287.1 ± 7.4 µM) in comparison to acarbose as the positive control (750.0 ± 10.5). Kinetic study of the most potent compound 8p showed a competitive type of inhibition (Ki value = 60 µM). In silico induced fit docking and molecular dynamics studies were performed to further investigate the interaction, orientation, and conformation of the title new compounds over the active site of α-glucosidase. In silico druglikeness analysis and ADMET prediction of the most potent compounds demonstrated that these compounds were druglikeness and had satisfactory ADMET profile.