Docking-based virtual screening, ADMET, and network pharmacology prediction of anthocyanidins against human alpha-amylase and alpha-glucosidase enzymes as potential antidiabetic agents

Abstract

Diabetes mellitus (DM) characterized by high blood sugar concentration is a major global public health problem and untreated DM results in blindness, kidney failure, heart attack, stroke, and lower extremity amputation. In this structure-based drug design (SBDD) study, the potential inhibitory effects of twelve anthocyanidins (aglycon unit of anthocyanins) components on human pancreatic α-amylase and intestinal α-glucosidase enzymes were investigated using the molecular docking method and a novel approach developed by our research group was used to rank the global binding potentials of ligands to a series of different enzymes simultaneously. In addition, drug-likeness, absorption-distribution-metabolism-excretion-toxicity (ADMET) predictions, and intracellular target-component interaction network analyses of twelve anthocyanidin components were performed using the search tool for interactions of chemicals (STITCH). Petunidin, peonidin, and aurantinidin were determined as 'hit' phytochemicals according to the docking binding energy and relative binding capacity index (RBCI) analyses, whereas, based on the RBCI index, petunidin was found to be the most effective ligand in terms of binding capacity to both enzymes that play an important role in DM. The more accessible and large-volume active site of α-amylase compared to α-glucosidase caused petunidin to bind with higher affinity against α-amylase. Promisingly, petunidin did not violate any of the criteria for drug-likeness consisting of a combination of the Lipinski's rule of 5, Ghose and Veber filters, showed no cytochrome (CYP) P450 or hERG I-II inhibitory activity in the ADMET analysis, however, it was found to have a low gastrointestinal absorption profile. In intracellular target-component network analysis using the STITCH online platform, it was determined that petunidin did not show negative functional interactions with any enzyme in the human protein network. Considering these results, it is recommended that petunidin be advanced to further in vitro and in vivo assays as a potential α-amylase and α-glucosidase inhibitory agent in the treatment of DM. However, the intestinal absorption profile of petunidin must be enhanced by molecular optimization without compromising its pharmacological activity.

Keywords

• Structure-based drug design (SBDD)
• Anthocyanidin
• Drug-likeness
• ADMET
• Target-components interaction network

Kaynakça

1. Barabási, A.L., Gulbahce, N., Loscalzo, J., 2011. Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56-68.
2. Belwal, T., Nabavi, S.F., Nabavi, S.M., Habtemariam, S., 2017. Dietary anthocyanins and insulin resistance: When food becomes a medicine. Nutrients, 9(10), 1111.
3. Carrillo‐Larco, R.M., Barengo, N.C., Albitres‐Flores, L., Bernabe‐Ortiz, A., 2019. The risk of mortality among people with type 2 diabetes in Latin America: A systematic review and meta‐analysis of population‐based cohort studies. Diabetes/Metabolism Research and Reviews, 35(4), e3139.
4. Castañeda-Ovando, A., de Lourdes Pacheco-Hernández, M., Páez-Hernández, M.E., Rodríguez, J.A., Galán-Vidal, C.A., 2009. Chemical studies of anthocyanins: A review. Food Chemistry, 113(4), 859-871.
5. Chandran, U., Mehendale, N., Tillu, G., Patwardhan, B., 2015. Network pharmacology of ayurveda formulation Triphala with special reference to anti-cancer property. Combinatorial Chemistry & High Throughput Screening, 18(9), 846-854.
6. Chen, J.G., Wu, S.F., Zhang, Q.F., Yin, Z.P., Zhang, L., 2020. α-Glucosidase inhibitory effect of anthocyanins from Cinnamomum camphora fruit: Inhibition kinetics and mechanistic insights through in vitro and in silico studies. International Journal of Biological Macromolecules, 143, 696-703.
7. Chen, L., Magliano, D.J., Zimmet, P.Z., 2012. The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives. Nature Reviews Endocrinology, 8(4), 228-236.
8. Coman, C., Rugina, O.D., Socaciu, C., 2012. Plants and natural compounds with antidiabetic action. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 40(1), 314-325.

9. Congreve, M., Carr, R., Murray, C., Jhoti, H., 2003. A'rule of three'for fragment-based lead discovery?. Drug Discovery Today, 8(19), 876-877.
10. 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.
11. Daina, A., Michielin, O., Zoete, V., 2019. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357-W364.
12. Delaney, J.S., 2004. ESOL: estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer Sciences, 44(3), 1000-1005.
13. Finch, A., Pillans, P., 2014. P-glycoprotein and its role in drug-drug interactions. Australian Prescriber, 37(4), 137-139.
14. Forouhi, N.G., Wareham, N.J., 2014. Epidemiology of diabetes. Medicine (Abingdon), 42 (12): 698–702.
15. Gadaleta, D., Vuković, K., Toma, C., Lavado, G.J., Karmaus, A.L., Mansouri, K., Roncaglioni, A., 2019. SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. Journal of Cheminformatics, 11(1), 1-16.
16. 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.
17. Gleeson, M.P., 2008. Generation of a set of simple, interpretable ADMET rules of thumb. Journal of Medicinal Chemistry, 51(4), 817-834.
18. Hann, M.M., Keserü, G.M., 2012. Finding the sweet spot: the role of nature and nurture in medicinal chemistry. Nature Reviews Drug Discovery, 11(5), 355-365.
19. Hopkins, A.L., 2008. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682-690.
20. Horbowicz, M., Kosson, R., Grzesiuk, A., Debski, H., 2008. Anthocyanins of fruits and vegetables-their occurrence, analysis and role in human nutrition. Vegetable Crops Research Bulletin, 68, 5-22.
21. Iacobucci, G.A., Sweeny, J.G., 1983. The chemistry of anthocyanins, anthocyanidins and related flavylium salts. Tetrahedron, 39(19), 3005-3038.
22. Istifli, E.S., Netz, P.A., Sihoglu Tepe, A., Husunet, M.T., Sarikurkcu, C., Tepe, B., 2022. In silico analysis of the interactions of certain flavonoids with the receptor-binding domain of 2019 novel coronavirus and cellular proteases and their pharmacokinetic properties. Journal of Biomolecular Structure and Dynamics, 40(6), 2460-2474.
23. Jayawardena, R., Ranasinghe, P., Galappatthy, P., Malkanthi, R.L.D.K., Constantine, G.R., Katulanda, P., 2012. Effects of zinc supplementation on diabetes mellitus: a systematic review and meta-analysis. Diabetology & Metabolic Syndrome, 4(1), 1-12.
24. Kell, D.B., Dobson, P.D., Oliver, S.G., 2011. Pharmaceutical drug transport: the issues and the implications that it is essentially carrier-mediated only. Drug Discovery Today, 16(15-16), 704-714.
25. Khoo, H.E., Azlan, A., Tang, S.T., Lim, S.M., 2017. Anthocyanidins and anthocyanins: Colored pigments as food, pharmaceutical ingredients, and the potential health benefits. Food & Nutrition Research, 61(1), 1361779.
26. Krasiński, A., Radić, Z., Manetsch, R., Raushel, J., Taylor, P., Sharpless, K.B., Kolb, H.C., 2005. In situ selection of lead compounds by click chemistry: target-guided optimization of acetylcholinesterase inhibitors. Journal of the American Chemical Society, 127(18), 6686-6692.
27. Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64, 4-17.
28. Liu, Y., Wang, Q., Wu, K., Sun, Z., Tang, Z., Li, X., Zhang, B., 2022. Anthocyanins’ effects on diabetes mellitus and islet transplantation. Critical Reviews in Food Science and Nutrition, 1-24.
29. Lobo, S., 2020. Is there enough focus on lipophilicity in drug discovery?. Expert Opinion on Drug Discovery, 15(3), 261-263.
30. Malde, A.K., Zuo, L., Breeze, M., Stroet, M., Poger, D., Nair, P.C., Mark, A.E., 2011. An automated force field topology builder (ATB) and repository: version 1.0. Journal of Chemical Theory and Computation, 7(12), 4026-4037.
31. Meng, X.Y., Zhang, H.X., Mezei, M., Cui, M., 2011. Molecular docking: a powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 7(2), 146-157.
32. Oliveira, H., Fernandes, A., Brás, N., Mateus, N., de Freitas, V., Fernandes, I., 2020. Anthocyanins as antidiabetic agents—in vitro and in silico approaches of preventive and therapeutic effects. Molecules, 25(17), 3813.
33. Oti, M., Snel, B., Huynen, M.A., Brunner, H.G., 2006. Predicting disease genes using protein–protein interactions. Journal of Medical Genetics, 43(8), 691-698.
34. Pedretti, A., Villa, L., Vistoli, G., 2004. VEGA–an open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. Journal of Computer-Aided Molecular Design, 18(3), 167-173.
35. Pellecchia, M., Becattini, B., Crowell, K.J., Fattorusso, R., Forino, M., Fragai, M., Tautz, L., 2004. NMR-based techniques in the hit identification and optimisation processes. Expert Opinion on Therapeutic Targets, 8(6), 597-611.
36. Pérez Gutierrez, R., Hernández Luna, H., Hernández Garrido, S., 2006. Antioxidant activity of Tagetes erecta essential oil. Journal of the Chilean Chemical Society, 51(2), 883-886.
37. Peterson, J.J., Dwyer, J.T., Jacques, P.F., McCullough, M.L., 2015. Improving the estimation of flavonoid intake for study of health outcomes. Nutrition Reviews, 73(8), 553-576.
38. Pires, D.E., Blundell, T.L., Ascher, D.B., 2015. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066-4072.
39. Promyos, N., Temviriyanukul, P., Suttisansanee, U., 2020. Investigation of anthocyanidins and anthocyanins for targeting α-glucosidase in diabetes mellitus. Preventive Nutrition and Food Science, 25(3), 263-271.
40. Sanner, M.F., 1999. Python: a programming language for software integration and development. Journal of Molecular Graphics and Modelling, 17(1), 57-61.
41. Santos, R.N.D., Ferreira, L.G., Andricopulo, A.D., 2018. Practices in molecular docking and structure-based virtual screening. In Computational drug discovery and design (pp. 31-50). Humana Press, New York, NY.
42. Sharma, S. (1996). Applied multivariate techniques. Wiley, ISBN: 978-0-471-31064-8.
43. Shultz, M.D., 2018. Two decades under the influence of the rule of five and the changing properties of approved oral drugs: miniperspective. Journal of Medicinal Chemistry, 62(4), 1701-1714.
44. Sivamaruthi, B.S., Kesika, P., Subasankari, K., Chaiyasut, C., 2018. Beneficial effects of anthocyanins against diabetes mellitus associated consequences-A mini review. Asian Pacific Journal of Tropical Biomedicine, 8(10), 471-477.
45. Sky-Peck, H.H., Thuvasethakul, P., 1977. Human pancreatic alpha-amylase. II. Effects of pH, substrate and ions on the activity of the enzyme. Annals of Clinical & Laboratory Science, 7(4), 310-317.
46. Szklarczyk, D., Santos, A., Von Mering, C., Jensen, L.J., Bork, P., Kuhn, M., 2016. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Research, 44(D1), D380-D384.
47. Testa, B., Vistoli, G., Pedretti, A., van de Waterbeemd, H., Avdeef, A., Ivanciuc, O., Muresan, S., 2007. Molecular drug properties: measurement and prediction. Wiley, ISBN:9783527621286.
48. Tomasik, P., Horton, D., 2012. Enzymatic conversions of starch. Advances in Carbohydrate Chemistry And Biochemistry, 68, 59-436.
49. 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.
50. Tsuda, T., Ueno, Y., Aoki, H., Koda, T., Horio, F., Takahashi, N., Osawa, T., 2004. Anthocyanin enhances adipocytokine secretion and adipocyte-specific gene expression in isolated rat adipocytes. Biochemical and Biophysical Research Communications, 316(1), 149-157.
51. Tsuda, T., Ueno, Y., Kojo, H., Yoshikawa, T., Osawa, T., 2005. Gene expression profile of isolated rat adipocytes treated with anthocyanins. Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids, 1733(2-3), 137-147.
52. Tsuda, T., Ueno, Y., Yoshikawa, T., Kojo, H., Osawa, T., 2006. Microarray profiling of gene expression in human adipocytes in response to anthocyanins. Biochemical Pharmacology, 71(8), 1184-1197.
53. 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.
54. Vistoli, G., Pedretti, A., Testa, B., 2008. Assessing drug-likeness–what are we missing?. Drug Discovery Today, 13(7-8), 285-294.
55. Wallace, T.C., Giusti, M.M., 2015. Anthocyanins. Advances in Nutrition, 6(5), 620-622.
56. Wiernsperger, N., Rapin, J., 2010. Trace elements in glucometabolic disorders: an update. Diabetology & Metabolic Syndrome, 2(1), 1-9.
57. Yang, K., Bai, H., Ouyang, Q., Lai, L., Tang, C., 2008. Finding multiple target optimal intervention in disease‐related molecular network. Molecular Systems Biology, 4(1), 228.
58. Zamora-Ros, R., Knaze, V., Luján-Barroso, L., Slimani, N., Romieu, I., Fedirko, V., González, C.A., 2011. Estimated dietary intakes of flavonols, flavanones and flavones in the European Prospective Investigation into Cancer and Nutrition (EPIC) 24 hour dietary recall cohort. British Journal of Nutrition, 106(12), 1915-1925.
59. Zhang, J., Sun, L., Dong, Y., Fang, Z., Nisar, T., Zhao, T., Guo, Y., 2019. Chemical compositions and α-glucosidase inhibitory effects of anthocyanidins from blueberry, blackcurrant and blue honeysuckle fruits. Food Chemistry, 299, 125102.
60. Zheng, Y., Ley, S.H., Hu, F.B., 2018. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature Reviews Endocrinology, 14(2), 88-98.