Research Article
BibTex RIS Cite

Diyabet Hastalığının Tedavisinde Kullanılabilecek Alfa-Amilaz İnhibitörü Olarak Kuersetin ve Umbelliferonun Etkisinin In Silico Kanıtı

Year 2022, Volume: 22 Issue: 3, 202 - 216, 23.12.2022
https://doi.org/10.17475/kastorman.1215281

Abstract

Çalışmanın amacı: Bu çalışmanın amacı, diyabet tedavisinde önemli α-amilaz inhibitörü olarak, kuersetin ve umbelliferonun potansiyel kullanımına ilişkin in silico kanıtları ortaya koymaktır.
Materyal ve yöntem: Kuersetin, umbelliferon ve ticari bir α-amilaz inhibitörü olan akarboz'un alfa - amilaza bağlanması sırasındaki olası konformasyonları ve yönelimleri taranmadan önce, enzimin potansiyel aktif bölgeleri CASTp 3.0 sunucusu tarafından tahmin ve analiz edilmiştir. Bu bileşiklerin potansiyel aktif bölgelerle 3D etkileşimleri ve bağlanma enerjilerini elde etmek için moleküler kenetlenme (docking) analizi Auto Dock Tools v.1.5.6 kullanılarak yapılmıştır. Ayrıca etkileşim skorları iGEMDOCK v.2.1 ile hesaplanmıştır. Aktif bölgelerdeki etkileşimleri açıkça gösteren 2D enzim-inhibitör etkileşimleri LigPlot+ v.2.2 ile analiz edilmiştir. Kuersetin ve umbelliferonun ilaç benzerliği özellikleri DruLiTo yazılımı ve SWISSADME sunucusu tarafından akarboz ile karşılaştırılmıştır. Bileşiklerin farmakokinetik özelliklerini gösteren absorpsiyon, dağılım, metabolizma, boşaltım ve toksisite (ADMET) skorları ADMETLab, admetSAR ve PreADMET sunucuları kullanılarak analiz edilmiştir.
Temel sonuçlar: Sonuç olarak, kuersetin ve umbelliferonun α-amilaz inhibitör aktivitesi ve bunların kullanım potansiyelleri in silico kanıtlanmıştır.
Araştırma vurguları: Çalışmanın sonuçları, kuersetin ve umbelliferonun diyabet tedavisinde olası bir tıbbi kullanıma sahip olabileceğini açıkça ortaya koymaktadır

References

  • Balavignesh, V., Srinivasan, E., Ramesh Babu, N. G. & Saravanan, N. (2013). Molecular docking study ON NS5B polymerase of hepatitis c virus by screening of volatile compounds from Acacia concinna and ADMET prediction. International Journal of Pharmaceutical and Life Sciences, 4(4), 2548-2558.
  • Biovia, Dassault Systèmes. (2019). Discovery Studio Visualizer v.20.1.0.19295 [Computer software]. San Diego: Dassault Systèmes.
  • Brandsch, M., Ganapathy, V. & Leibach, F.H. (1995). H (+)-peptide cotransport in Madin-Darby canine kidney cells: expression and calmodulin-dependent regulation. American Journal of Physiology-Renal Physiology, 268(3), F391-F397.
  • Castillo-Garit, J., Cañizares-Carmenate, Y., Marrero-Ponce, Y., Abad, C. & Torrens, F. (2014). Prediction of ADME properties, Part 1: Classification models to predict Caco-2 cell permeability using atom-based bilinear indices. Afinidad, 71(566), 124-138.
  • Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P. W. & Tang, Y. (2012). admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. Journal of Chemical Information and Modeling, 52(11), 3099-3105.
  • Daina, A., Michielin, O. & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 1-13.
  • Dong, J., Wang, N.N., Yao, Z.J., Zhang, L., Cheng, Y., Ouyang, D., Lu, A.P. & Cao, D.S. (2018). ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics, 10(1), 1-11.
  • Drug Likeness Tool. (2018). http://www.niper.gov.in/pi_dev_tools/DruLiToWeb/DruLiTo_index.html
  • Egan, W.J., Merz, K.M. & Baldwin, J.J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867-3877.
  • Elmeliegy, M., Vourvahis, M., Guo, C. & Wang, D. D. (2020). Effect of P-glycoprotein (P-gp) inducers on exposure of P-gp substrates: Review of clinical drug–drug interaction studies. Clinical pharmacokinetics, 59(6), 699-714.
  • Elmiar, F.A., Altuner, E.M., Özgöz, A. & Demir, S., (2018). Diabetic Nephropathy and the Relationship between Diabetic Nephropathy and Genetic Polymorphisms. Journal of Scientific Research and Reports, 19(3), 1-11.
  • Fong, C.W. (2015). Permeability of the blood–brain barrier: molecular mechanism of transport of drugs and physiologically important compounds. The Journal of Membrane Biology, 248(4), 651-669.
  • Fromm, M.F. (2004). Importance of P-glycoprotein at blood–tissue barriers. Trends in Pharmacological Sciences, 25(8), 423-429.
  • Ganapathy, M.E., Brandsch, M., Prasad, P.D., Ganapathy, V. & Leibach, F.H, (1995). Differential recognition of β-lactam antibiotics by intestinal and renal peptide transporters, PEPT 1 and PEPT 2. Journal of Biological Chemistry, 270(43), 25672-25677.
  • Ghose, A.K., Viswanadhan, V.N. & Wendoloski, J.J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55-68.
  • Guengerich, F.P. (2003). Cytochromes P450, drugs, and diseases. Molecular Interventions, 3(4), 194-204.
  • Horio, M., Chin, K.V., Currier, S.J., Goldenberg, S., Williams, C., Pastan, I., Gottesman, M.M. & Handler, J. (1989). Transepithelial transport of drugs by the multidrug transporter in cultured Madin-Darby canine kidney cell epithelia. Journal of Biological Chemistry, 264(25), 14880-14884.
  • Horster, M.F. & Stopp, M. (1986). Transport and metabolic functions in cultured renal tubule cells. Kidney International, 29(1), 46-53.
  • Hsu, K.C., Chen, Y.F., Lin, S.R. & Yang, J.M. (2011). iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics, 12(1), 1-11.
  • Irvine, J.D., Takahashi, L., Lockhart, K., Cheong, J., Tolan, J.W., Selick, H.E. & Grove, J.R. (1999). MDCK (Madin–Darby canine kidney) cells: a tool for membrane permeability screening. Journal of Pharmaceutical Sciences, 88(1), 28-33.
  • Ivanyuk, A., Livio, F., Biollaz, J. & Buclin, T. (2017). Renal drug transporters and drug interactions. Clinical pharmacokinetics, 56(8), 825-892.
  • Jhong, C.H., Riyaphan, J., Lin, S. H., Chia, Y.C. & Weng, C.F. (2015). Screening alpha‐glucosidase and alpha‐amylase inhibitors from natural compounds by molecular docking in silico. Biofactors, 41(4), 242-251.
  • Joshi, T., Joshi, T., Sharma, P., Pundir, H. & Chandra, S. (2020). In silico identification of natural fungicide from Melia azedarach against isocitrate lyase of Fusarium graminearum. Journal of Biomolecular Structure and Dynamics, 1-19.
  • Kell, D.B. & Oliver, S.G. (2014). How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Frontiers in Pharmacology, 5(231), 1-32.
  • Kim, H.M., Kim, J.K., Kang, L. W., Jeong, K.J. & Jung, S.H. (2010). Docking and scoring of quercetin and quercetin glycosides against α-amylase receptor. Bulletin of the Korean Chemical Society, 31(2), 461-463.
  • Kwon, Y.I., Apostolidis, E. & Shetty, K. (2008). Inhibitory potential of wine and tea against α‐amylase and α‐glucosidase for management of hyperglycemia linked to type 2 diabetes. Journal of Food Biochemistry, 32(1), 15-31.
  • Lacombe, O., Guyot, A., Videau, O., Pruvost, A., Bolze, S., Prevost, C. & Mabondzo, A. (2010). Brain penetration predictivity using in-vitro primary rat and human cell-based blood-brain barrier models for drug discovery and development. Fundamental and Clinical Pharmacology, 24(1), 8.
  • Lee, S.K., Chang, G.S., Lee, I.H., Chung, J.E., Sung, K.Y. & No, K.T. (2004). The PreADME: pc-based program for batch prediction of adme properties. EuroQSAR, 9, 5-10.
  • Lee, S.K., Lee, I.H., Kim, H.J., Chang, G.S., Chung, J.E. & No, K.T. (2003). The PreADME Approach: Web-based program for rapid prediction of physico-chemical, drug absorption and drug-like properties. EuroQSAR designing drugs and crop protectants: processes, problems and solutions. Blackwell Publishing.
  • Leeson, P. (2012). Chemical beauty contest. Nature, 481(7382), 455-456.
  • Lin, J.H. & Yamazaki, M. (2003). Role of P-glycoprotein in pharmacokinetics. Clinical Pharmacokinetics, 42(1), 59-98.
  • Lund, M., Petersen, T.S. & Dalhoff, K.P. (2017). Clinical implications of P-glycoprotein modulation in drug–drug interactions. Drugs, 77(8), 859-883.
  • Mazimba, O. (2017). Umbelliferone: Sources, chemistry and bioactivities review. Bulletin of Faculty of Pharmacy, Cairo University, 55(2), 223-232.
  • Menshaz, A. & Altuner, E.M. (2020). The Potential of Some Plant-Derived Compounds in Inhibition of α-Amylase, Important for Diabetic Patients. Fresenius Environmental Bulletin, 29(9A), 8642-8646.
  • Muegge, I., Heald, S.L. & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841-1846.
  • Muehlbacher, M., Spitzer, G.M., Liedl, K.R. & Kornhuber, J. (2011). Qualitative prediction of blood–brain barrier permeability on a large and refined dataset. Journal of Computer-aided Molecular Design, 25(12), 1095-1106.
  • Murakami, A., Ashida, H. & Terao, J. (2008). Multitargeted cancer prevention by quercetin. Cancer Letters, 269(2), 315-325.
  • Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L. & Pähler, A. (2008). Computational toxicology in drug development. Drug Discovery Today, 13(7-8), 303-310.
  • Nielsen, P.A., Andersson, O., Hansen, S.H., Simonsen, K.B. & Andersson, G. (2011). Models for predicting blood–brain barrier permeation. Drug Discovery Today, 16(11-12), 472-475.
  • Nyenwe, E.A., Jerkins, T.W., Umpierrez, G.E. & Kitabchi, A.E. (2011). Management of type 2 diabetes: evolving strategies for the treatment of patients with type 2 diabetes. Metabolism, 60(1), 1-23.
  • O'Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T. & Hutchison, G.R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3(1), 1-14.
  • Pardridge, W.M. (2007). Blood–brain barrier delivery. Drug Discovery Today, 12(1-2), 54-61.
  • Patil, S.M., Martiz, R.M., Ramu, R., Shirahatti, P. S., Prakash, A., Kumar, B.P. & Kumar, N. (2021). Evaluation of flavonoids from banana pseudostem and flower (quercetin and catechin) as potent inhibitors of α-glucosidase: an in silico perspective. Journal of Biomolecular Structure and Dynamics, 1-15.
  • Petrus, K., Schwartz, H. & Sontag, G. (2011). Analysis of flavonoids in honey by HPLC coupled with coulometric electrode array detection and electrospray ionization mass spectrometry. Analytical and Bioanalytical Chemistry, 400(8), 2555-2563.
  • Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C. & Ferrin, T.E. (2004). UCSF Chimera-a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605-1612.
  • Poirier, D. (2003). Inhibitors of 17β-hydroxysteroid dehydrogenases. Current Medicinal Chemistry, 10(6), 453-477.
  • Qian, M., Haser, R., Buisson, G., Duee, E. & Payan, F. (1994). The Active Center of a Mammalian. alpha.-Amylase. Structure of the Complex of a Pancreatic. alpha.-Amylase with a Carbohydrate Inhibitor Refined to 2.2-. ANG. Resolution. Biochemistry, 33(20), 6284-6294.
  • Ramasubbu, N., Paloth, V., Luo, Y., Brayer, G.D. & Levine, M.J. (1996). Structure of human salivary α-amylase at 1.6 Å resolution: implications for its role in the oral cavity. Acta Crystallographica Section D: Biological Crystallography, 52(3), 435-446.
  • Russo, G.L., Russo, M., Spagnuolo, C., Tedesco, I., Bilotto, S., Iannitti, R. & Palumbo, R. (2014). Quercetin: a pleiotropic kinase inhibitor against cancer. In V. Zappia, S. Panico, G. Russo, A. Budillon. & F. Della Ragione (Eds.), Advances in Nutrition and Cancer. Cancer Treatment and Research, 159, 185-205. Springer.
  • Tian, W., Chen, C., Lei, X., Zhao, J. & Liang, J., (2018). CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research, 46(W1), W363-W367.
  • Trott, O. & Olson, A.J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461.
  • Tundis, R., Loizzo, M.R., & Menichini, F. (2010). Natural products as α-amylase and α-glucosidase inhibitors and their hypoglycaemic potential in the treatment of diabetes: an update. Mini Reviews in Medicinal Chemistry, 10(4), 315-331.
  • URL-1. (1996). 1SMD Human salivary amylase; https://www.rcsb.org/structure/1SMD. (accessed 14.11.2021).
  • Veber, D.F., Johnson, S.R., Cheng, H.Y., Smith, B.R., Ward, K.W. & Kopple, K.D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623.
  • Wallace, A.C., Laskowski, R.A. & Thornton, J.M. (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Engineering, Design and Selection, 8(2), 127-134.
  • Wang, S., Li, Y., Wang, J., Chen, L., Zhang, L., Yu, H. & Hou, T. (2012). ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Molecular Pharmaceutics, 9(4), 996-1010.
  • White, N.H. (2015). Long-term outcomes in youths with diabetes mellitus. Pediatric Clinics, 62(4), 889-909.
  • Yang, H., Lou, C., Sun, L., Li, J., Cai, Y., Wang, Z., Li, W., Liu, G. & Tang, Y. (2019). admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 35(6), 1067-1069.
  • Zanger, U.M. & Schwab, M. (2013). Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacology & Therapeutics, 138(1), 103-141.

In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes

Year 2022, Volume: 22 Issue: 3, 202 - 216, 23.12.2022
https://doi.org/10.17475/kastorman.1215281

Abstract

Aim of study: The aim of this study is to show the in silico evidences about the potential use of quercetin and umbelliferone as α-amylase inhibitors, which is important for the treatment of diabetes.
Material and methods: The possible conformations and orientations of quercetin, umbelliferone, and acarbose, in binding to the active sites of alpha-amylase, were analysed by CASTp server. The molecular dockings of these compounds to the potential active site were performed by AutoDock Tools to obtain 3D interactions and binding energies. In addition, the interaction scores were calculated by iGEMDOCK. The 2D enzyme-inhibitor interactions, which clearly show the interactions at the active sites, were analysed by LigPlot+. The drug-likeness properties of quercetin and umbelliferone were compared to acarbose by DruLiTo software and SWISSADME server. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) scores, which present the pharmacokinetic properties of the compounds were analysed by ADMETLab, admetSAR, and PreADMET servers
Main results: As a result, the α-amylase inhibitor activity and the potential use of quercetin and umbelliferone were proved in silico.
Highlights: The results of the study clearly put forward that quercetin and umbelliferone could have possible medicinal use in the treatment of diabetes

References

  • Balavignesh, V., Srinivasan, E., Ramesh Babu, N. G. & Saravanan, N. (2013). Molecular docking study ON NS5B polymerase of hepatitis c virus by screening of volatile compounds from Acacia concinna and ADMET prediction. International Journal of Pharmaceutical and Life Sciences, 4(4), 2548-2558.
  • Biovia, Dassault Systèmes. (2019). Discovery Studio Visualizer v.20.1.0.19295 [Computer software]. San Diego: Dassault Systèmes.
  • Brandsch, M., Ganapathy, V. & Leibach, F.H. (1995). H (+)-peptide cotransport in Madin-Darby canine kidney cells: expression and calmodulin-dependent regulation. American Journal of Physiology-Renal Physiology, 268(3), F391-F397.
  • Castillo-Garit, J., Cañizares-Carmenate, Y., Marrero-Ponce, Y., Abad, C. & Torrens, F. (2014). Prediction of ADME properties, Part 1: Classification models to predict Caco-2 cell permeability using atom-based bilinear indices. Afinidad, 71(566), 124-138.
  • Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P. W. & Tang, Y. (2012). admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. Journal of Chemical Information and Modeling, 52(11), 3099-3105.
  • Daina, A., Michielin, O. & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 1-13.
  • Dong, J., Wang, N.N., Yao, Z.J., Zhang, L., Cheng, Y., Ouyang, D., Lu, A.P. & Cao, D.S. (2018). ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. Journal of Cheminformatics, 10(1), 1-11.
  • Drug Likeness Tool. (2018). http://www.niper.gov.in/pi_dev_tools/DruLiToWeb/DruLiTo_index.html
  • Egan, W.J., Merz, K.M. & Baldwin, J.J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867-3877.
  • Elmeliegy, M., Vourvahis, M., Guo, C. & Wang, D. D. (2020). Effect of P-glycoprotein (P-gp) inducers on exposure of P-gp substrates: Review of clinical drug–drug interaction studies. Clinical pharmacokinetics, 59(6), 699-714.
  • Elmiar, F.A., Altuner, E.M., Özgöz, A. & Demir, S., (2018). Diabetic Nephropathy and the Relationship between Diabetic Nephropathy and Genetic Polymorphisms. Journal of Scientific Research and Reports, 19(3), 1-11.
  • Fong, C.W. (2015). Permeability of the blood–brain barrier: molecular mechanism of transport of drugs and physiologically important compounds. The Journal of Membrane Biology, 248(4), 651-669.
  • Fromm, M.F. (2004). Importance of P-glycoprotein at blood–tissue barriers. Trends in Pharmacological Sciences, 25(8), 423-429.
  • Ganapathy, M.E., Brandsch, M., Prasad, P.D., Ganapathy, V. & Leibach, F.H, (1995). Differential recognition of β-lactam antibiotics by intestinal and renal peptide transporters, PEPT 1 and PEPT 2. Journal of Biological Chemistry, 270(43), 25672-25677.
  • Ghose, A.K., Viswanadhan, V.N. & Wendoloski, J.J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55-68.
  • Guengerich, F.P. (2003). Cytochromes P450, drugs, and diseases. Molecular Interventions, 3(4), 194-204.
  • Horio, M., Chin, K.V., Currier, S.J., Goldenberg, S., Williams, C., Pastan, I., Gottesman, M.M. & Handler, J. (1989). Transepithelial transport of drugs by the multidrug transporter in cultured Madin-Darby canine kidney cell epithelia. Journal of Biological Chemistry, 264(25), 14880-14884.
  • Horster, M.F. & Stopp, M. (1986). Transport and metabolic functions in cultured renal tubule cells. Kidney International, 29(1), 46-53.
  • Hsu, K.C., Chen, Y.F., Lin, S.R. & Yang, J.M. (2011). iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics, 12(1), 1-11.
  • Irvine, J.D., Takahashi, L., Lockhart, K., Cheong, J., Tolan, J.W., Selick, H.E. & Grove, J.R. (1999). MDCK (Madin–Darby canine kidney) cells: a tool for membrane permeability screening. Journal of Pharmaceutical Sciences, 88(1), 28-33.
  • Ivanyuk, A., Livio, F., Biollaz, J. & Buclin, T. (2017). Renal drug transporters and drug interactions. Clinical pharmacokinetics, 56(8), 825-892.
  • Jhong, C.H., Riyaphan, J., Lin, S. H., Chia, Y.C. & Weng, C.F. (2015). Screening alpha‐glucosidase and alpha‐amylase inhibitors from natural compounds by molecular docking in silico. Biofactors, 41(4), 242-251.
  • Joshi, T., Joshi, T., Sharma, P., Pundir, H. & Chandra, S. (2020). In silico identification of natural fungicide from Melia azedarach against isocitrate lyase of Fusarium graminearum. Journal of Biomolecular Structure and Dynamics, 1-19.
  • Kell, D.B. & Oliver, S.G. (2014). How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Frontiers in Pharmacology, 5(231), 1-32.
  • Kim, H.M., Kim, J.K., Kang, L. W., Jeong, K.J. & Jung, S.H. (2010). Docking and scoring of quercetin and quercetin glycosides against α-amylase receptor. Bulletin of the Korean Chemical Society, 31(2), 461-463.
  • Kwon, Y.I., Apostolidis, E. & Shetty, K. (2008). Inhibitory potential of wine and tea against α‐amylase and α‐glucosidase for management of hyperglycemia linked to type 2 diabetes. Journal of Food Biochemistry, 32(1), 15-31.
  • Lacombe, O., Guyot, A., Videau, O., Pruvost, A., Bolze, S., Prevost, C. & Mabondzo, A. (2010). Brain penetration predictivity using in-vitro primary rat and human cell-based blood-brain barrier models for drug discovery and development. Fundamental and Clinical Pharmacology, 24(1), 8.
  • Lee, S.K., Chang, G.S., Lee, I.H., Chung, J.E., Sung, K.Y. & No, K.T. (2004). The PreADME: pc-based program for batch prediction of adme properties. EuroQSAR, 9, 5-10.
  • Lee, S.K., Lee, I.H., Kim, H.J., Chang, G.S., Chung, J.E. & No, K.T. (2003). The PreADME Approach: Web-based program for rapid prediction of physico-chemical, drug absorption and drug-like properties. EuroQSAR designing drugs and crop protectants: processes, problems and solutions. Blackwell Publishing.
  • Leeson, P. (2012). Chemical beauty contest. Nature, 481(7382), 455-456.
  • Lin, J.H. & Yamazaki, M. (2003). Role of P-glycoprotein in pharmacokinetics. Clinical Pharmacokinetics, 42(1), 59-98.
  • Lund, M., Petersen, T.S. & Dalhoff, K.P. (2017). Clinical implications of P-glycoprotein modulation in drug–drug interactions. Drugs, 77(8), 859-883.
  • Mazimba, O. (2017). Umbelliferone: Sources, chemistry and bioactivities review. Bulletin of Faculty of Pharmacy, Cairo University, 55(2), 223-232.
  • Menshaz, A. & Altuner, E.M. (2020). The Potential of Some Plant-Derived Compounds in Inhibition of α-Amylase, Important for Diabetic Patients. Fresenius Environmental Bulletin, 29(9A), 8642-8646.
  • Muegge, I., Heald, S.L. & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841-1846.
  • Muehlbacher, M., Spitzer, G.M., Liedl, K.R. & Kornhuber, J. (2011). Qualitative prediction of blood–brain barrier permeability on a large and refined dataset. Journal of Computer-aided Molecular Design, 25(12), 1095-1106.
  • Murakami, A., Ashida, H. & Terao, J. (2008). Multitargeted cancer prevention by quercetin. Cancer Letters, 269(2), 315-325.
  • Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L. & Pähler, A. (2008). Computational toxicology in drug development. Drug Discovery Today, 13(7-8), 303-310.
  • Nielsen, P.A., Andersson, O., Hansen, S.H., Simonsen, K.B. & Andersson, G. (2011). Models for predicting blood–brain barrier permeation. Drug Discovery Today, 16(11-12), 472-475.
  • Nyenwe, E.A., Jerkins, T.W., Umpierrez, G.E. & Kitabchi, A.E. (2011). Management of type 2 diabetes: evolving strategies for the treatment of patients with type 2 diabetes. Metabolism, 60(1), 1-23.
  • O'Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T. & Hutchison, G.R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3(1), 1-14.
  • Pardridge, W.M. (2007). Blood–brain barrier delivery. Drug Discovery Today, 12(1-2), 54-61.
  • Patil, S.M., Martiz, R.M., Ramu, R., Shirahatti, P. S., Prakash, A., Kumar, B.P. & Kumar, N. (2021). Evaluation of flavonoids from banana pseudostem and flower (quercetin and catechin) as potent inhibitors of α-glucosidase: an in silico perspective. Journal of Biomolecular Structure and Dynamics, 1-15.
  • Petrus, K., Schwartz, H. & Sontag, G. (2011). Analysis of flavonoids in honey by HPLC coupled with coulometric electrode array detection and electrospray ionization mass spectrometry. Analytical and Bioanalytical Chemistry, 400(8), 2555-2563.
  • Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C. & Ferrin, T.E. (2004). UCSF Chimera-a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605-1612.
  • Poirier, D. (2003). Inhibitors of 17β-hydroxysteroid dehydrogenases. Current Medicinal Chemistry, 10(6), 453-477.
  • Qian, M., Haser, R., Buisson, G., Duee, E. & Payan, F. (1994). The Active Center of a Mammalian. alpha.-Amylase. Structure of the Complex of a Pancreatic. alpha.-Amylase with a Carbohydrate Inhibitor Refined to 2.2-. ANG. Resolution. Biochemistry, 33(20), 6284-6294.
  • Ramasubbu, N., Paloth, V., Luo, Y., Brayer, G.D. & Levine, M.J. (1996). Structure of human salivary α-amylase at 1.6 Å resolution: implications for its role in the oral cavity. Acta Crystallographica Section D: Biological Crystallography, 52(3), 435-446.
  • Russo, G.L., Russo, M., Spagnuolo, C., Tedesco, I., Bilotto, S., Iannitti, R. & Palumbo, R. (2014). Quercetin: a pleiotropic kinase inhibitor against cancer. In V. Zappia, S. Panico, G. Russo, A. Budillon. & F. Della Ragione (Eds.), Advances in Nutrition and Cancer. Cancer Treatment and Research, 159, 185-205. Springer.
  • Tian, W., Chen, C., Lei, X., Zhao, J. & Liang, J., (2018). CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research, 46(W1), W363-W367.
  • Trott, O. & Olson, A.J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461.
  • Tundis, R., Loizzo, M.R., & Menichini, F. (2010). Natural products as α-amylase and α-glucosidase inhibitors and their hypoglycaemic potential in the treatment of diabetes: an update. Mini Reviews in Medicinal Chemistry, 10(4), 315-331.
  • URL-1. (1996). 1SMD Human salivary amylase; https://www.rcsb.org/structure/1SMD. (accessed 14.11.2021).
  • Veber, D.F., Johnson, S.R., Cheng, H.Y., Smith, B.R., Ward, K.W. & Kopple, K.D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623.
  • Wallace, A.C., Laskowski, R.A. & Thornton, J.M. (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Engineering, Design and Selection, 8(2), 127-134.
  • Wang, S., Li, Y., Wang, J., Chen, L., Zhang, L., Yu, H. & Hou, T. (2012). ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage. Molecular Pharmaceutics, 9(4), 996-1010.
  • White, N.H. (2015). Long-term outcomes in youths with diabetes mellitus. Pediatric Clinics, 62(4), 889-909.
  • Yang, H., Lou, C., Sun, L., Li, J., Cai, Y., Wang, Z., Li, W., Liu, G. & Tang, Y. (2019). admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 35(6), 1067-1069.
  • Zanger, U.M. & Schwab, M. (2013). Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacology & Therapeutics, 138(1), 103-141.
There are 59 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ergin Murat Altuner This is me

Publication Date December 23, 2022
Published in Issue Year 2022 Volume: 22 Issue: 3

Cite

APA Altuner, E. M. (2022). In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes. Kastamonu University Journal of Forestry Faculty, 22(3), 202-216. https://doi.org/10.17475/kastorman.1215281
AMA Altuner EM. In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes. Kastamonu University Journal of Forestry Faculty. December 2022;22(3):202-216. doi:10.17475/kastorman.1215281
Chicago Altuner, Ergin Murat. “In Silico Proof of the Effect of Quercetin and Umbelliferone As Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes”. Kastamonu University Journal of Forestry Faculty 22, no. 3 (December 2022): 202-16. https://doi.org/10.17475/kastorman.1215281.
EndNote Altuner EM (December 1, 2022) In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes. Kastamonu University Journal of Forestry Faculty 22 3 202–216.
IEEE E. M. Altuner, “In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes”, Kastamonu University Journal of Forestry Faculty, vol. 22, no. 3, pp. 202–216, 2022, doi: 10.17475/kastorman.1215281.
ISNAD Altuner, Ergin Murat. “In Silico Proof of the Effect of Quercetin and Umbelliferone As Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes”. Kastamonu University Journal of Forestry Faculty 22/3 (December 2022), 202-216. https://doi.org/10.17475/kastorman.1215281.
JAMA Altuner EM. In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes. Kastamonu University Journal of Forestry Faculty. 2022;22:202–216.
MLA Altuner, Ergin Murat. “In Silico Proof of the Effect of Quercetin and Umbelliferone As Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes”. Kastamonu University Journal of Forestry Faculty, vol. 22, no. 3, 2022, pp. 202-16, doi:10.17475/kastorman.1215281.
Vancouver Altuner EM. In Silico Proof of the Effect of Quercetin and Umbelliferone as Alpha-Amylase Inhibitors, Which Can Be Used in the Treatment of Diabetes. Kastamonu University Journal of Forestry Faculty. 2022;22(3):202-16.

14178  14179       14165           14166           14167            14168