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Year 2025, Volume: 29 Issue: 2, 776 - 789
https://doi.org/10.12991/jrespharm.1666356

Abstract

References

  • [1] Alrefai H, Allababidi H, Levy S, Levy J. The endocrine system in diabetes mellitus. Endocr. 2002; 18(2): 105–119. https://doi.org/10.1385/ENDO:18:2:105
  • [2] Mushtaq A, Azam U, Mehreen S, Naseer MM. Synthetic α-glucosidase inhibitors as promising anti-diabetic agents: recent developments and future challenges. Eur J Med Chem. 2023; 249: 115119. https://doi.org/10.1016/j.ejmech.2023.115119
  • [3] Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, Pavkov ME, Ramachandaran A, Wild SH, James S, Herman WH, Zhang P, Bommer C, Kuo S, Boyko EJ, Magliano DJ. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022; 183: 109119. https://doi.org/10.1016/j.diabres.2021.109119
  • [4] Kashtoh H, Baek KH. Recent updates on phytoconstituent alpha-glucosidase inhibitors: an approach towards the treatment of type two diabetes. Plants. 2022; 11(20): 2722. https://doi.org/10.3390/plants11202722
  • [5] Sakran N, Graham Y, Pintar T, Yang W, Kassir R, Willigendael EM, Singhal R, Kooreman ZE, Ramnarain D, Mahawar K, Parmar C, Madhok B, Pouwels S. The many faces of diabetes. Is there a need for re-classification? A narrative review. BMC Endocr Disord. 2022; 22(1): 229-240. https://doi.org/10.1186/S12902-021-00927-Y
  • [6] Tanabe H, Masuzaki H, Shimabukuro M. Novel strategies for glycaemic control and preventing diabetic complications applying the clustering-based classification of adult-onset diabetes mellitus: a perspective. Diabetes Res Clin Pract. 2021; 180: 109067. https://doi.org/10.1016/j.diabres.2021.109067
  • [7] Dowarah J, Singh VP. Anti-diabetic drugs recent approaches and advancements. Bioorg Med Chem. 2020; 28(5): 115263. https://doi.org/10.1016/J.BMC.2019.115263
  • [8] Ghani U. Re-exploring promising α-glucosidase inhibitors for potential development into oral anti-diabetic drugs: finding needle in the haystack. Eur J Med Chem. 2015; 103:133–162. https://doi.org/10.1016/J.EJMECH.2015.08.043
  • [9] Singh A, Singh K, Sharma A, Kaur K, Kaur K, Chadha R, Bedi PMS. Recent developments in synthetic α-glucosidase inhibitors: a comprehensive review with structural and molecular insight. J Mol Struct. 2023; 1281: 135115. https://doi.org/10.1016/j.molstruc.2023.135115
  • [10] Dirir AM, Daou M, Yousef AF, Yousef LF. A review of alpha-glucosidase inhibitors from plants as potential candidates for the treatment of type-2 diabetes. Phytochem Rev. 2022; 21: 1049-1079. https://doi.org/10.1007/s11101-021-09773-1
  • [11] Patil P, Mandal S, Tomar SK, Anand S. Food protein-derived bioactive peptides in management of type 2 diabetes. Eur J Nutr. 2015; 54(6): 863–880. https://doi.org/10.1007/S00394-015-0974-2/TABLES/5
  • [12] Nasykhova YA, Tonyan ZN, Mikhailova AA, Danilova MM, Glotov AS. Pharmacogenetics of type 2 diabetes—progress and prospects. Int J Mol Sci. 2020; 21(18): 6842. https://doi.org/10.3390/IJMS21186842
  • [13] Fu Y, Sun P, Li G, He R, Shi L, Xing N. Recent advances in the synthetic method and mechanism for the important N-heterocyclic compound of 3-methylindole. J Heterocycl Chem. 2022; 59(7): 1135–1143. https://doi.org/10.1002/JHET.4451
  • [14] Muhammed MT, Aki-Yalcin E. Pharmacophore modeling in drug discovery: methodology and current status. J Turk Chem Soc Sect Chem. 2021; 8(3): 759–772. https://doi.org/10.18596/jotcsa. 927426
  • [15] Fan J, Fu A, Zhang L. Progress in molecular docking. Quant Biol. 2019; 7(2): 83–89. https://doi.org/10.1007/s40484-019-0172-y
  • [16] Muhammed MT, Aki-Yalcin E. Molecular docking: principles, advances, and its applications in drug discovery. Lett Drug Des Discov. 2024; 21(3): 480–495. https://doi.org/10.2174/1570180819666220922103109
  • [17] Işık A, Çevik UA, Celik I, Erçetin T, Koçak A, Özkay Y, Kaplancıklı ZA. Synthesis, characterization, molecular docking, dynamics simulations, and in silico absorption, distribution, metabolism, and excretion (ADME) studies of new thiazolylhydrazone derivatives as butyrylcholinesterase inhibitors. Z Naturforsch C J Biosci. 2022; 77(11): 447-457. https://doi.org/10.1515/ZNC-2021-0316
  • [18] Maity D, Singh D, Bandhu A. Mce1R of Mycobacterium tuberculosis prefers long chain fatty acids as specific ligands : a computational study. Mol Divers. 2023; 27: 2523-2543. https://doi.org/10.1007/s11030-022-10566-7
  • [19] Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 2018; 46(W1): W363–W367. https://doi.org/10.1093/nar/gky473 [20] Ullah H, Rahim F, Taha M, Hussain R, Tabassum N, Wadood A, Nawaz M, Mosaddik A, Imran S, Wahab Z, Miana GA, Kanwal, Khan KM. Aryl-oxadiazole schiff bases: synthesis, α-glucosidase in vitro inhibitory activity and their in silico studies. Arab J Chem. 2020; 13(4):4904–4915. https://doi.org/10.1016/J.ARABJC.2020.01.005
  • [21] Luthra T, Banothu V, Adepally U, Kumar K, M S, Chakrabarti S, Maddi SR, Sen S. Discovery of novel pyrido-pyrrolidine hybrid compounds as alpha-glucosidase inhibitors and alternative agent for control of type 1 diabetes. Eur J Med Chem. 2020; 188: 112034. https://doi.org/10.1016/J.EJMECH.2020.112034
  • [22] Ullah H, Ahmad N, Rahim F, Uddin I, Hayat S, Zada H, Zaman K, Farooqi K, Bakhtiar M, Khan IU, Rehman AU, Wadood A. Synthesis, molecular docking study of thiazole derivatives and exploring their dual inhibitor potentials against α-amylase and α-glucosidase. Chem Data Collect. 2022; 41: 100932. https://doi.org/10.1016/J.CDC.2022.100932
  • [23] Kazmi M, Zaib S, Amjad ST, Khan I, Ibrar A, Saeed A, Iqbal J. Exploration of aroyl/heteroaroyl iminothiazolines featuring 2,4,5-trichlorophenyl moiety as a new class of potent, selective, and in vitro efficacious glucosidase inhibitors. Bioorganic Chem. 2017; 74: 134–144. https://doi.org/10.1016/J.BIOORG.2017.07.012
  • [24] Kaur J, Singh A, Singh G, Verma RK, Mall R. Novel indolyl linked para-substituted benzylidene-based phenyl containing thiazolidienediones and their analogs as α-glucosidase inhibitors: synthesis, in vitro, and molecular docking studies. Med Chem Res. 2018; 27(3): 903–914. https://doi.org/10.1007/S00044-017-2112-6
  • [25] Mughal EU, Amjid S, Sadiq A, Naeem N, Nazir Y, Alrafai HA, Hassan AA, Al-Nami SY, Abdel Hafez AA, Ali Shah SW, Ghias M. Design and synthesis of 2-amino-4,6-diarylpyrimidine derivatives as potent α-glucosidase and α-amylase inhibitors: structure–activity relationship, in vitro, QSAR, molecular docking, MD simulations and drug-likeness studies. J Biomol Struct Dyn. 2024; 42(1): 244–260. https://doi.org/10.1080/07391102.2023.2198609
  • [26] Ichale R, Kanhed AM, Vora A. Coumarin linked thiazole derivatives as potential α-glucosidase inhibitors to treat diabetes mellitus. Mol Divers. 2024; 28(3):1239-1247. https://doi.org/10.1007/S11030-023-10652-4
  • [27] He M, Li YJ, Shao J, Li YS, Cui ZN. Synthesis and biological evaluation of 2,5-disubstituted furan derivatives containing 1,3-thiazole moiety as potential α‐glucosidase inhibitors. Bioorg Med Chem Lett. 2023; 83: 129173. https://doi.org/10.1016/j.bmcl.2023.129173
  • [28] Wu XZ, Zhu WJ, Lu L, Hu CM, Zheng YY, Zhang X, Lin J, Wu JY, Xiong Z, Zhang K, Xu XT. Synthesis and anti-α-glucosidase activity evaluation of betulinic acid derivatives. Arab J Chem. 2023; 16(5): 104659. https://doi.org/10.1016/j.arabjc.2023.104659
  • [29] Khan S, Iqbal S, Rehman W, Hussain N, Hussain R, Shah M, Ali F, Fouda AM, Khan Y, Dera AA, Issa Alahmdi M, Bahadur A, Al-ghulikah HA, Elkaeed EB. Synthesis, molecular docking and ADMET studies of bis-benzimidazole-based thiadiazole derivatives as potent inhibitors, in vitro α-amylase and α-glucosidase. Arab J Chem. 2023; 16(7): 104847. https://doi.org/10.1016/j.arabjc.2023.104847
  • [30] Muhammed MT, Kokbudak Z, Akkoc S. Cytotoxic activities of the pyrimidine-based acetamide and isophthalimide derivatives: an in vitro and in silico studies. Mol Simul. 2023; 49(10): 982–992. https://doi.org/10.1080/08927022.2023.2202766
  • [31] Akman S, Akkoc S, Zeyrek CT, Muhammed MT, Ilhan IO. Density functional modeling , and molecular docking with SARS-CoV-2 spike protein ( Wuhan ) and omicron S protein ( variant ) studies of new heterocyclic compounds including a pyrazoline nucleus. J Biomol Struct Dyn. 2023; 41(22): 12951–12965. https://doi.org/10.1080/07391102.2023.2169765
  • [32] Qidwai T. QSAR modeling, docking and ADMET studies for exploration of potential anti-malarial compounds against Plasmodium falciparum. Silico Pharmacol. 2017; 5(6): 1–13. https://doi.org/10.1007/s40203-017-0026-0
  • [33] Fonteh P, Elkhadir A, Omondi B, Guzei I, Darkwa J, Meyer D. Impedance technology reveals correlations between cytotoxicity and lipophilicity of mono and bimetallic phosphine complexes. BioMetals. 2015; 28(4): 653–667. https://doi.org/10.1007/s10534-015-9851-y
  • [34] Dahlgren D, Lennernäs H. Intestinal permeability and drug absorption: predictive experimental, computational and in vivo approaches. Pharmaceutics. 2019; 11(8): 411. https://doi.org/10.3390/pharmaceutics11080411
  • [35] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001; 46(1–3): 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0
  • [36] Bateman A, Martin M-J, Orchard S, Magrane M, Ahmad S, Alpi E, Bowler-Barnett EH, Britto R, Bye-A-Jee H, Cukura A, Denny P, Dogan T, Ebenezer T, Fan J, Garmiri P, da Costa Gonzales LJ, Hatton-Ellis E, Hussein A, Ignatchenko A, Insana G, Ishtiaq R, Joshi V, Jyothi D, Kandasaamy S, Lock A, Luciani A, Lugaric M, Ledaschi N, Rivoire C, Sigrist CJA, Sonesson K, Sundaram S, Wu CH, Arighi CN, Arminski L, Chen C, Chen Y, Huang H, Laiho K, McGarvey P, Natale DA, Ross K, Vinayaka CR, Wang Q, Wang Y, Zhang J. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 2023; 51(D1): D523–D531. https://doi.org/10.1093/NAR/GKAC1052
  • [37] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nat. 2021; 596(7873): 583–589. https://doi.org/10.1038/s41586-021-03819-2
  • [38] Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER suite: Protein structure and function prediction. Nat Methods. 2014; 12(1): 7–8. https://doi.org/10.1038/nmeth.3213
  • [39] Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, De Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018; 46(W1): W296–W303. https://doi.org/10.1093/nar/gky427
  • [40] Colovos C, Yeates T. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993; 2: 1511–1519
  • [41] Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010; 31(2): 455-461. https://doi.org/10.1002/JCC.21334
  • [42] Arslan G, Gökçe B, Muhammed MT, Albayrak Ö. Synthesis, DFT calculations, and molecular docking study of acetohydrazide-based sulfonamide derivatives as paraoxonase 1 inhibitors. ChemistrySelect. 2023; 8(10): e202204630. https://doi.org/10.1002/slct.202204630
  • [43] Celik I, Erol M, Duzgun Z. In silico evaluation of potential inhibitory activity of remdesivir, favipiravir, ribavirin and galidesivir active forms on SARS-CoV-2 RNA polymerase. Mol Divers. 2022; 26(1):279–292. https://doi.org/10.1007/s11030-021-10215-5
  • [44] Accelrys Software, Disovery Studio, 2012
  • [45] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7: 42717. https://doi.org/10.1038/srep42717
  • [46] Han Y, Zhang J, Hu CQ, Zhang X, Ma B, Zhang P. In silico ADME and toxicity prediction of ceftazidime and its impurities. Front Pharmacol. 2019; 10: 434–445. https://doi.org/10.3389/fphar.2019.00434

Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation

Year 2025, Volume: 29 Issue: 2, 776 - 789
https://doi.org/10.12991/jrespharm.1666356

Abstract

Diabetes mellitus is a metabolic disorder with high prevalence. As hyperglycemia is the main manifestation of diabetes, controlling postprandial hyperglycemia by inhibiting carbohydrate digestion is important to treat the disease. α-glucosidase is one of the carbohydrate hydrolyzing enzymes that breaks carbohydrates into monosaccharides and thus causes hyperglycemia. Therefore, α-glucosidase is an attractive target to decrease blood glucose level by suppressing carbohydrate digestion. There are clinically available α-glucosidase inhibitor drugs. However, these drugs are associated with adverse effects. Therefore, novel drugs with high efficacy and low adverse effects are needed. Heterocyclic compounds are under investigation to this end. In this study, active heterocyclic inhibitors were selected. The probable mode of action for these compounds was investigated through molecular docking and molecular dynamics (MD) simulation after the human α-glucosidase structure was built via homology modeling. The pharmacokinetic properties of the compounds were also assessed. The docking study showed that some of them have high binding potential to the α-glucosidase. However, the compounds with high binding potential gave enzyme-compound complexes with moderate stability. Compound 5 gave a complex with relatively higher stability. The computational pharmacokinetic study revealed that the compounds except compounds 12 and 13 would have good absorption or permeability for oral administration. Understanding the mechanism of action for the existing active compounds will be helpful to pursue the research for further applications and to design novel compounds with similar scaffolds. The findings of this study need further investigation through in vitro and in vivo methods.

References

  • [1] Alrefai H, Allababidi H, Levy S, Levy J. The endocrine system in diabetes mellitus. Endocr. 2002; 18(2): 105–119. https://doi.org/10.1385/ENDO:18:2:105
  • [2] Mushtaq A, Azam U, Mehreen S, Naseer MM. Synthetic α-glucosidase inhibitors as promising anti-diabetic agents: recent developments and future challenges. Eur J Med Chem. 2023; 249: 115119. https://doi.org/10.1016/j.ejmech.2023.115119
  • [3] Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, Pavkov ME, Ramachandaran A, Wild SH, James S, Herman WH, Zhang P, Bommer C, Kuo S, Boyko EJ, Magliano DJ. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022; 183: 109119. https://doi.org/10.1016/j.diabres.2021.109119
  • [4] Kashtoh H, Baek KH. Recent updates on phytoconstituent alpha-glucosidase inhibitors: an approach towards the treatment of type two diabetes. Plants. 2022; 11(20): 2722. https://doi.org/10.3390/plants11202722
  • [5] Sakran N, Graham Y, Pintar T, Yang W, Kassir R, Willigendael EM, Singhal R, Kooreman ZE, Ramnarain D, Mahawar K, Parmar C, Madhok B, Pouwels S. The many faces of diabetes. Is there a need for re-classification? A narrative review. BMC Endocr Disord. 2022; 22(1): 229-240. https://doi.org/10.1186/S12902-021-00927-Y
  • [6] Tanabe H, Masuzaki H, Shimabukuro M. Novel strategies for glycaemic control and preventing diabetic complications applying the clustering-based classification of adult-onset diabetes mellitus: a perspective. Diabetes Res Clin Pract. 2021; 180: 109067. https://doi.org/10.1016/j.diabres.2021.109067
  • [7] Dowarah J, Singh VP. Anti-diabetic drugs recent approaches and advancements. Bioorg Med Chem. 2020; 28(5): 115263. https://doi.org/10.1016/J.BMC.2019.115263
  • [8] Ghani U. Re-exploring promising α-glucosidase inhibitors for potential development into oral anti-diabetic drugs: finding needle in the haystack. Eur J Med Chem. 2015; 103:133–162. https://doi.org/10.1016/J.EJMECH.2015.08.043
  • [9] Singh A, Singh K, Sharma A, Kaur K, Kaur K, Chadha R, Bedi PMS. Recent developments in synthetic α-glucosidase inhibitors: a comprehensive review with structural and molecular insight. J Mol Struct. 2023; 1281: 135115. https://doi.org/10.1016/j.molstruc.2023.135115
  • [10] Dirir AM, Daou M, Yousef AF, Yousef LF. A review of alpha-glucosidase inhibitors from plants as potential candidates for the treatment of type-2 diabetes. Phytochem Rev. 2022; 21: 1049-1079. https://doi.org/10.1007/s11101-021-09773-1
  • [11] Patil P, Mandal S, Tomar SK, Anand S. Food protein-derived bioactive peptides in management of type 2 diabetes. Eur J Nutr. 2015; 54(6): 863–880. https://doi.org/10.1007/S00394-015-0974-2/TABLES/5
  • [12] Nasykhova YA, Tonyan ZN, Mikhailova AA, Danilova MM, Glotov AS. Pharmacogenetics of type 2 diabetes—progress and prospects. Int J Mol Sci. 2020; 21(18): 6842. https://doi.org/10.3390/IJMS21186842
  • [13] Fu Y, Sun P, Li G, He R, Shi L, Xing N. Recent advances in the synthetic method and mechanism for the important N-heterocyclic compound of 3-methylindole. J Heterocycl Chem. 2022; 59(7): 1135–1143. https://doi.org/10.1002/JHET.4451
  • [14] Muhammed MT, Aki-Yalcin E. Pharmacophore modeling in drug discovery: methodology and current status. J Turk Chem Soc Sect Chem. 2021; 8(3): 759–772. https://doi.org/10.18596/jotcsa. 927426
  • [15] Fan J, Fu A, Zhang L. Progress in molecular docking. Quant Biol. 2019; 7(2): 83–89. https://doi.org/10.1007/s40484-019-0172-y
  • [16] Muhammed MT, Aki-Yalcin E. Molecular docking: principles, advances, and its applications in drug discovery. Lett Drug Des Discov. 2024; 21(3): 480–495. https://doi.org/10.2174/1570180819666220922103109
  • [17] Işık A, Çevik UA, Celik I, Erçetin T, Koçak A, Özkay Y, Kaplancıklı ZA. Synthesis, characterization, molecular docking, dynamics simulations, and in silico absorption, distribution, metabolism, and excretion (ADME) studies of new thiazolylhydrazone derivatives as butyrylcholinesterase inhibitors. Z Naturforsch C J Biosci. 2022; 77(11): 447-457. https://doi.org/10.1515/ZNC-2021-0316
  • [18] Maity D, Singh D, Bandhu A. Mce1R of Mycobacterium tuberculosis prefers long chain fatty acids as specific ligands : a computational study. Mol Divers. 2023; 27: 2523-2543. https://doi.org/10.1007/s11030-022-10566-7
  • [19] Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 2018; 46(W1): W363–W367. https://doi.org/10.1093/nar/gky473 [20] Ullah H, Rahim F, Taha M, Hussain R, Tabassum N, Wadood A, Nawaz M, Mosaddik A, Imran S, Wahab Z, Miana GA, Kanwal, Khan KM. Aryl-oxadiazole schiff bases: synthesis, α-glucosidase in vitro inhibitory activity and their in silico studies. Arab J Chem. 2020; 13(4):4904–4915. https://doi.org/10.1016/J.ARABJC.2020.01.005
  • [21] Luthra T, Banothu V, Adepally U, Kumar K, M S, Chakrabarti S, Maddi SR, Sen S. Discovery of novel pyrido-pyrrolidine hybrid compounds as alpha-glucosidase inhibitors and alternative agent for control of type 1 diabetes. Eur J Med Chem. 2020; 188: 112034. https://doi.org/10.1016/J.EJMECH.2020.112034
  • [22] Ullah H, Ahmad N, Rahim F, Uddin I, Hayat S, Zada H, Zaman K, Farooqi K, Bakhtiar M, Khan IU, Rehman AU, Wadood A. Synthesis, molecular docking study of thiazole derivatives and exploring their dual inhibitor potentials against α-amylase and α-glucosidase. Chem Data Collect. 2022; 41: 100932. https://doi.org/10.1016/J.CDC.2022.100932
  • [23] Kazmi M, Zaib S, Amjad ST, Khan I, Ibrar A, Saeed A, Iqbal J. Exploration of aroyl/heteroaroyl iminothiazolines featuring 2,4,5-trichlorophenyl moiety as a new class of potent, selective, and in vitro efficacious glucosidase inhibitors. Bioorganic Chem. 2017; 74: 134–144. https://doi.org/10.1016/J.BIOORG.2017.07.012
  • [24] Kaur J, Singh A, Singh G, Verma RK, Mall R. Novel indolyl linked para-substituted benzylidene-based phenyl containing thiazolidienediones and their analogs as α-glucosidase inhibitors: synthesis, in vitro, and molecular docking studies. Med Chem Res. 2018; 27(3): 903–914. https://doi.org/10.1007/S00044-017-2112-6
  • [25] Mughal EU, Amjid S, Sadiq A, Naeem N, Nazir Y, Alrafai HA, Hassan AA, Al-Nami SY, Abdel Hafez AA, Ali Shah SW, Ghias M. Design and synthesis of 2-amino-4,6-diarylpyrimidine derivatives as potent α-glucosidase and α-amylase inhibitors: structure–activity relationship, in vitro, QSAR, molecular docking, MD simulations and drug-likeness studies. J Biomol Struct Dyn. 2024; 42(1): 244–260. https://doi.org/10.1080/07391102.2023.2198609
  • [26] Ichale R, Kanhed AM, Vora A. Coumarin linked thiazole derivatives as potential α-glucosidase inhibitors to treat diabetes mellitus. Mol Divers. 2024; 28(3):1239-1247. https://doi.org/10.1007/S11030-023-10652-4
  • [27] He M, Li YJ, Shao J, Li YS, Cui ZN. Synthesis and biological evaluation of 2,5-disubstituted furan derivatives containing 1,3-thiazole moiety as potential α‐glucosidase inhibitors. Bioorg Med Chem Lett. 2023; 83: 129173. https://doi.org/10.1016/j.bmcl.2023.129173
  • [28] Wu XZ, Zhu WJ, Lu L, Hu CM, Zheng YY, Zhang X, Lin J, Wu JY, Xiong Z, Zhang K, Xu XT. Synthesis and anti-α-glucosidase activity evaluation of betulinic acid derivatives. Arab J Chem. 2023; 16(5): 104659. https://doi.org/10.1016/j.arabjc.2023.104659
  • [29] Khan S, Iqbal S, Rehman W, Hussain N, Hussain R, Shah M, Ali F, Fouda AM, Khan Y, Dera AA, Issa Alahmdi M, Bahadur A, Al-ghulikah HA, Elkaeed EB. Synthesis, molecular docking and ADMET studies of bis-benzimidazole-based thiadiazole derivatives as potent inhibitors, in vitro α-amylase and α-glucosidase. Arab J Chem. 2023; 16(7): 104847. https://doi.org/10.1016/j.arabjc.2023.104847
  • [30] Muhammed MT, Kokbudak Z, Akkoc S. Cytotoxic activities of the pyrimidine-based acetamide and isophthalimide derivatives: an in vitro and in silico studies. Mol Simul. 2023; 49(10): 982–992. https://doi.org/10.1080/08927022.2023.2202766
  • [31] Akman S, Akkoc S, Zeyrek CT, Muhammed MT, Ilhan IO. Density functional modeling , and molecular docking with SARS-CoV-2 spike protein ( Wuhan ) and omicron S protein ( variant ) studies of new heterocyclic compounds including a pyrazoline nucleus. J Biomol Struct Dyn. 2023; 41(22): 12951–12965. https://doi.org/10.1080/07391102.2023.2169765
  • [32] Qidwai T. QSAR modeling, docking and ADMET studies for exploration of potential anti-malarial compounds against Plasmodium falciparum. Silico Pharmacol. 2017; 5(6): 1–13. https://doi.org/10.1007/s40203-017-0026-0
  • [33] Fonteh P, Elkhadir A, Omondi B, Guzei I, Darkwa J, Meyer D. Impedance technology reveals correlations between cytotoxicity and lipophilicity of mono and bimetallic phosphine complexes. BioMetals. 2015; 28(4): 653–667. https://doi.org/10.1007/s10534-015-9851-y
  • [34] Dahlgren D, Lennernäs H. Intestinal permeability and drug absorption: predictive experimental, computational and in vivo approaches. Pharmaceutics. 2019; 11(8): 411. https://doi.org/10.3390/pharmaceutics11080411
  • [35] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001; 46(1–3): 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0
  • [36] Bateman A, Martin M-J, Orchard S, Magrane M, Ahmad S, Alpi E, Bowler-Barnett EH, Britto R, Bye-A-Jee H, Cukura A, Denny P, Dogan T, Ebenezer T, Fan J, Garmiri P, da Costa Gonzales LJ, Hatton-Ellis E, Hussein A, Ignatchenko A, Insana G, Ishtiaq R, Joshi V, Jyothi D, Kandasaamy S, Lock A, Luciani A, Lugaric M, Ledaschi N, Rivoire C, Sigrist CJA, Sonesson K, Sundaram S, Wu CH, Arighi CN, Arminski L, Chen C, Chen Y, Huang H, Laiho K, McGarvey P, Natale DA, Ross K, Vinayaka CR, Wang Q, Wang Y, Zhang J. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 2023; 51(D1): D523–D531. https://doi.org/10.1093/NAR/GKAC1052
  • [37] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nat. 2021; 596(7873): 583–589. https://doi.org/10.1038/s41586-021-03819-2
  • [38] Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER suite: Protein structure and function prediction. Nat Methods. 2014; 12(1): 7–8. https://doi.org/10.1038/nmeth.3213
  • [39] Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, Heer FT, De Beer TAP, Rempfer C, Bordoli L, Lepore R, Schwede T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018; 46(W1): W296–W303. https://doi.org/10.1093/nar/gky427
  • [40] Colovos C, Yeates T. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993; 2: 1511–1519
  • [41] Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010; 31(2): 455-461. https://doi.org/10.1002/JCC.21334
  • [42] Arslan G, Gökçe B, Muhammed MT, Albayrak Ö. Synthesis, DFT calculations, and molecular docking study of acetohydrazide-based sulfonamide derivatives as paraoxonase 1 inhibitors. ChemistrySelect. 2023; 8(10): e202204630. https://doi.org/10.1002/slct.202204630
  • [43] Celik I, Erol M, Duzgun Z. In silico evaluation of potential inhibitory activity of remdesivir, favipiravir, ribavirin and galidesivir active forms on SARS-CoV-2 RNA polymerase. Mol Divers. 2022; 26(1):279–292. https://doi.org/10.1007/s11030-021-10215-5
  • [44] Accelrys Software, Disovery Studio, 2012
  • [45] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017; 7: 42717. https://doi.org/10.1038/srep42717
  • [46] Han Y, Zhang J, Hu CQ, Zhang X, Ma B, Zhang P. In silico ADME and toxicity prediction of ceftazidime and its impurities. Front Pharmacol. 2019; 10: 434–445. https://doi.org/10.3389/fphar.2019.00434
There are 45 citations in total.

Details

Primary Language English
Subjects Pharmaceutical Chemistry
Journal Section Articles
Authors

Muhammed Tilahun Muhammed

Ecemsu Sari This is me

Şükran İncikuşu This is me

Tuğba Baştürk This is me

Publication Date
Submission Date January 17, 2024
Acceptance Date May 13, 2024
Published in Issue Year 2025 Volume: 29 Issue: 2

Cite

APA Muhammed, M. T., Sari, E., İncikuşu, Ş., Baştürk, T. (n.d.). Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation. Journal of Research in Pharmacy, 29(2), 776-789. https://doi.org/10.12991/jrespharm.1666356
AMA Muhammed MT, Sari E, İncikuşu Ş, Baştürk T. Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation. J. Res. Pharm. 29(2):776-789. doi:10.12991/jrespharm.1666356
Chicago Muhammed, Muhammed Tilahun, Ecemsu Sari, Şükran İncikuşu, and Tuğba Baştürk. “Computational Insight into Synthetic Alpha-Glucosidase Inhibitors: Homology Modeling, Docking, and Molecular Dynamics Simulation”. Journal of Research in Pharmacy 29, no. 2 n.d.: 776-89. https://doi.org/10.12991/jrespharm.1666356.
EndNote Muhammed MT, Sari E, İncikuşu Ş, Baştürk T Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation. Journal of Research in Pharmacy 29 2 776–789.
IEEE M. T. Muhammed, E. Sari, Ş. İncikuşu, and T. Baştürk, “Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation”, J. Res. Pharm., vol. 29, no. 2, pp. 776–789, doi: 10.12991/jrespharm.1666356.
ISNAD Muhammed, Muhammed Tilahun et al. “Computational Insight into Synthetic Alpha-Glucosidase Inhibitors: Homology Modeling, Docking, and Molecular Dynamics Simulation”. Journal of Research in Pharmacy 29/2 (n.d.), 776-789. https://doi.org/10.12991/jrespharm.1666356.
JAMA Muhammed MT, Sari E, İncikuşu Ş, Baştürk T. Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation. J. Res. Pharm.;29:776–789.
MLA Muhammed, Muhammed Tilahun et al. “Computational Insight into Synthetic Alpha-Glucosidase Inhibitors: Homology Modeling, Docking, and Molecular Dynamics Simulation”. Journal of Research in Pharmacy, vol. 29, no. 2, pp. 776-89, doi:10.12991/jrespharm.1666356.
Vancouver Muhammed MT, Sari E, İncikuşu Ş, Baştürk T. Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation. J. Res. Pharm. 29(2):776-89.