Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors
Abstract α-Glucosidase is a key enzyme responsible for controlling the blood glucose, making a pivotal target in the treatment of type 2 diabetes mellitus. Present work introduces1,2,4triazolo[1,5-a]pyridine as a novel, potent scaffold for α-glucosidase inhibition. A diverse scope of targeted compou...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01819-0 |
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| author | Fariba Peytam Parham Foroumadi Hayrettin Ozan Gulcan Maryam Norouzbahari Somayeh Mojtabavi Mohammad Ali Faramarzi Fahimeh Ghasemi Mohammadreza Torabi Behnaz Bameri Maliheh Barazandeh Tehrani Loghman Firoozpour Alireza Foroumadi |
| author_facet | Fariba Peytam Parham Foroumadi Hayrettin Ozan Gulcan Maryam Norouzbahari Somayeh Mojtabavi Mohammad Ali Faramarzi Fahimeh Ghasemi Mohammadreza Torabi Behnaz Bameri Maliheh Barazandeh Tehrani Loghman Firoozpour Alireza Foroumadi |
| author_sort | Fariba Peytam |
| collection | DOAJ |
| description | Abstract α-Glucosidase is a key enzyme responsible for controlling the blood glucose, making a pivotal target in the treatment of type 2 diabetes mellitus. Present work introduces1,2,4triazolo[1,5-a]pyridine as a novel, potent scaffold for α-glucosidase inhibition. A diverse scope of targeted compounds was prepared through an efficient, straightforward synthetic protocol. A series of compounds (15a–15v) were synthesized using a simple and efficient protocol, all showing notable inhibitory activity. Among them, compound 15j exhibited the best inhibition potency (IC₅₀ = 6.60 ± 0.09 µM), acting as a competitive and selective α-glucosidase inhibitor with no effect on α-amylase. Moreover, comprehensive computational studies were performed to validate the in vitro results and provide insight into compounds’ binding interactions within the α-glucosidase’s active site. The machine learning model, trained with the Estate fingerprint, achieved an AUC score of 0.65, demonstrating its utility in predicting α-glucosidase inhibition. Random Forest was identified as the most suitable model, and the dataset with the highest R² value was selected for further feature selection and model improvement. Molecular docking studies demonstrated that compound 15j had a strong binding affinity toward α-glucosidase, with a docking score of − 10.04 kcal/mol, and formed several remarkable interactions, particularly three key hydrogen bonds with TYR158, GLN353, and GLU411, contributing to its high inhibitory efficacy. The results of the molecular dynamics simulation demonstrated that the 15j–α-glucosidase complex exhibits high stability and effectively maintains its binding without causing significant structural changes in the enzyme, confirming the stable interaction and selective inhibition of this compound at the enzyme’s active site. |
| format | Article |
| id | doaj-art-e596be479fc64a5f809ffaf1741bd7e2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e596be479fc64a5f809ffaf1741bd7e22025-08-20T03:48:18ZengNature PortfolioScientific Reports2045-23222025-05-0115112010.1038/s41598-025-01819-0Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitorsFariba Peytam0Parham Foroumadi1Hayrettin Ozan Gulcan2Maryam Norouzbahari3Somayeh Mojtabavi4Mohammad Ali Faramarzi5Fahimeh Ghasemi6Mohammadreza Torabi7Behnaz Bameri8Maliheh Barazandeh Tehrani9Loghman Firoozpour10Alireza Foroumadi11Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical SciencesInternational Campus-School of Pharmacy, Tehran University of Medical SciencesFaculty of Pharmacy, Eastern Mediterranean UniversityFaculty of Pharmacy, Final International UniversityDepartment of Pharmaceutical Biotechnology, Faculty of Pharmacy, Tehran University of Medical SciencesDepartment of Pharmaceutical Biotechnology, Faculty of Pharmacy, Tehran University of Medical SciencesDepartment of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical SciencesDepartment of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical SciencesDepartment of Toxicology and Pharmacology, Faculty of Pharmacy and Toxicology and Diseases Group, Pharmaceutical Sciences Research Center (PSRC), The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical SciencesDepartment of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical SciencesDepartment of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical SciencesDepartment of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical SciencesAbstract α-Glucosidase is a key enzyme responsible for controlling the blood glucose, making a pivotal target in the treatment of type 2 diabetes mellitus. Present work introduces1,2,4triazolo[1,5-a]pyridine as a novel, potent scaffold for α-glucosidase inhibition. A diverse scope of targeted compounds was prepared through an efficient, straightforward synthetic protocol. A series of compounds (15a–15v) were synthesized using a simple and efficient protocol, all showing notable inhibitory activity. Among them, compound 15j exhibited the best inhibition potency (IC₅₀ = 6.60 ± 0.09 µM), acting as a competitive and selective α-glucosidase inhibitor with no effect on α-amylase. Moreover, comprehensive computational studies were performed to validate the in vitro results and provide insight into compounds’ binding interactions within the α-glucosidase’s active site. The machine learning model, trained with the Estate fingerprint, achieved an AUC score of 0.65, demonstrating its utility in predicting α-glucosidase inhibition. Random Forest was identified as the most suitable model, and the dataset with the highest R² value was selected for further feature selection and model improvement. Molecular docking studies demonstrated that compound 15j had a strong binding affinity toward α-glucosidase, with a docking score of − 10.04 kcal/mol, and formed several remarkable interactions, particularly three key hydrogen bonds with TYR158, GLN353, and GLU411, contributing to its high inhibitory efficacy. The results of the molecular dynamics simulation demonstrated that the 15j–α-glucosidase complex exhibits high stability and effectively maintains its binding without causing significant structural changes in the enzyme, confirming the stable interaction and selective inhibition of this compound at the enzyme’s active site.https://doi.org/10.1038/s41598-025-01819-0[1,2,4]Triazolo[1,5-a]pyridineα-Glucosidaseα-AmylaseMachine learning model |
| spellingShingle | Fariba Peytam Parham Foroumadi Hayrettin Ozan Gulcan Maryam Norouzbahari Somayeh Mojtabavi Mohammad Ali Faramarzi Fahimeh Ghasemi Mohammadreza Torabi Behnaz Bameri Maliheh Barazandeh Tehrani Loghman Firoozpour Alireza Foroumadi Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors Scientific Reports [1,2,4]Triazolo[1,5-a]pyridine α-Glucosidase α-Amylase Machine learning model |
| title | Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors |
| title_full | Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors |
| title_fullStr | Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors |
| title_full_unstemmed | Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors |
| title_short | Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors |
| title_sort | design synthesis and evaluation of triazolo 1 5 a pyridines as novel and potent α glucosidase inhibitors |
| topic | [1,2,4]Triazolo[1,5-a]pyridine α-Glucosidase α-Amylase Machine learning model |
| url | https://doi.org/10.1038/s41598-025-01819-0 |
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