Studying the hypolypidemic activity of flavonoids and isoflavonoids of the Ononis arvensis L. Methods by in silico

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

Introduction. Today, one of the reliably known causes of mortality in the Russian Federation is diseases of the cardiovascular system, a significant part of which is associated with atherosclerotic disease. Combination therapy for diseases of the cardiovascular system includes, among other things, the use of modern lipid-lowering drugs, the use of which is often limited due to their pronounced side effects. In this regard, it seemed appropriate to search for new compounds of natural origin that potentially have lipid-lowering activity with minimal side effects. According to the scientific literature, natural phenolic compounds, namely substances from the group of flavonoids and isoflavonoids, have a set of such characteristics. In this regard, steelgrass (Ononis arvensis L.), the chemical composition of which is extremely rich and diverse in terms of flavonoids and isoflavonoids, can be a rather promising source for searching and screening compounds with a given activity.

The aim of the study. The purpose of the work was to study and predict the hypolipidemic activity of flavonoids and isoflavonoids of Ononis arvensis L. using in silico methods.

Material and methods. The objects of the study were the structural formulas of flavonoids and isoflavonoids of field steelhead. Calculation of molecular properties was carried out using the Molinspiration chemoinformatic software. Computer prediction of lipid-lowering activity was carried out using the PASS-online service. Molecular docking was performed using the CB-Dock2 services for blind docking and Webina 1.0.5 for active site docking. Hepatotoxicity, mutagenicity and cytotoxicity of the analyzed biologically active substances were studied using the ProTox-II resource.

Results. As a result of in silico studies, it was found that most of the studied flavonoids and isoflavonoids correspond to the Lipinski rule and the drug-likeness concept. In addition, for all studied biologically active substances, activities associated with a decrease in lipid fractions in the body were predicted. The results of molecular docking indicate that all analyzed compounds are capable of potentially inhibiting the enzyme HMG-CoA reductase, which makes it possible to predict the required lipid-lowering effect. Studying the toxicity of the research objects, most of them in silico demonstrated a high level of safety.

Conclusions. The prospects for further research on the development of targeted technology for obtaining herbal preparations from steelhead, enriched with flavonoids and isoflavonoids, as well as subsequent tests to confirm hypolipidemic activity in in vitro and in vivo experiments are shown.

全文:

受限制的访问

作者简介

N. Davitavyan

Kuban State Medical University

编辑信件的主要联系方式.
Email: pharmdep@ksma.ru

Ph.D. (Pharm.), Associate Professor, Associate Professor of the Pharmaceutics Department

俄罗斯联邦, Krasnodar

Е. Nikiforova

Kuban State Medical University

Email: pharmdep@ksma.ru

Ph.D. (Pharm.), Associate Professor, Head of the Pharmaceutics Department

俄罗斯联邦, Krasnodar

Y. Pogulyay

Kuban State Medical University

Email: pharmdep@ksma.ru

Student, the Pharmaceutics Department

俄罗斯联邦, Krasnodar

М. Khochava

Kuban State Medical University

Email: pharmdep@ksma.ru

Ph.D. (Pharm.), Associate Professor, Associate Professor of the Pharmaceutics Department

俄罗斯联邦, Krasnodar

P. Mizina

All-Russian Scientific Research Institute of Medicinal and Aromatic Plants

Email: mizina-pg@yandex.ru

Dr.Sc. (Pharm.), Professor, Adviser

俄罗斯联邦, Moscow

G. Adamov

All-Russian Scientific Research Institute of Medicinal and Aromatic Plants

Email: grig.adamov@mail.ru

Ph.D. (Pharm.), Liding Research Scientist, Department of Chemistry of Natural Compounds

俄罗斯联邦, Moscow

参考

  1. Number of deaths by main classes of causes of death. Federal State Statistics Service: website. URL: https://rosstat.gov.ru/ (access date: 12/15/2023).
  2. Yao Y.S., Li T.D., Zeng Z.H. Mechanisms underlying direct actions of hyperlipidemia on myocardium: an updated review. Lipids in Health and Disease. 2020; 19(1): 1–6.
  3. Reiner Z., Catapano A.L., Backer G.D. et al. The Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS). ESC/EAS Guidelines for the management of dyslipidaemias. Eur Heart J. 2011; 32(14): 1769–1818.
  4. Balakumar P., Babbar L. Preconditioning the hyperlipidemic myocardium: fact or fantasy? Cellular Signaling. 2012; 24(3): 589–595.
  5. State register of medicines: website. URL: https://grls.minzdrav.gov.ru (access date: 12/15/2023).
  6. Taechalertpaisarn J., Zhao B., Liang X. et al. Small molecule inhibitors of the PCSK9·LDLR interaction. Journal of the American Chemical Society. 2018; 140(9): 3242–3249.
  7. Attardo S., Musumeci O., Velardo D. et al. Statins neuromuscular adverse effects. International journal of molecular sciences. 2022; 23(15): 8364.
  8. Sgro C., Escousse A. Side effects of fibrates (except liver and muscle). Therapie. 1991; 46(5): 351–354.
  9. Steiner A., Weisser B., Vetter W. A comparative review of the adverse effects of treatments for hyperlipidaemia. Drug Safety. 1991; 6:118–113.
  10. Gampe N., Darcsi A., Nedves A.N. et al. Phytochemical analysis of Ononis arvensis L. by liquid chromatography coupled with mass spectrometry. Journal of Mass Spectrometry. 2018; 54 (2): 1–30.
  11. Denes T., Papp N., Marton K. et al. Polyphenol content of Ononis arvensis L. and Rhinanthus serotinus oborny used in the Transylvanian ethnomedicine. International journal of pharmacognosy and phytochemistry. 2015; 30(1): 1301–1307.
  12. Tulaikin A.I., Yakovlev G.P. Study of the chemical composition of the field steel grass. Pharmacy. 2007; 2: 10–11.
  13. Davitavyan N. A., Sampiev A. M. Current state and prospects for further research of the field steelhead Ononis arvensis L. (review). Kuban Scientific Medical Bulletin. 2005; 3-4: 38.
  14. Ong S.K.L., Shanmugam M.K., Fan L. et al. Focus on formononetin: anticancer potential and molecular targets. Cancers. 2019; 11(5): 611.
  15. Hernández-Aquino E., Muriel P. Beneficial effects of naringenin in liver diseases: Molecular mechanisms. World journal of gastroenterology. 2018; 24(16): 1679.
  16. Islam A., Islam M. S., Rahman M.K. et al. Thelogical pharmaco and biological roles of eriodictyol. Archives of pharmacal research. 2020; 43: 582–592.
  17. Sun M.Y., Ye Y., Xiao L. et al. Daidzein: A review of pharmacological effects. African journal of traditional, complementary and alternative medicines. 2016; 13(3): 117–132.
  18. Kim S., Thiessen P. A., Cheng T. et al. PUG-View: programmatic access to chemical annotations integrated in PubChem. J. Cheminform. 2019; 11(1): 56.
  19. Hadda T.B., Rastija V., AlMalki F. et al. Petra/Osiris/Molinspiration and molecular docking analyzes of 3-hydroxy-indolin-2-one derivatives as potential antiviral agents. Current Computer-Aided Drug Design. 2021; 17(1): 123–133.
  20. Filimonov D.A., Druzhilovsky D.S., Lagunin A.A. et al. Computer prediction of the spectra of biological activity of chemical compounds: possibilities and limitations. Biomedical Chemistry: Research and Methods. 2018; 1(1): e00004.
  21. Filz O.A., Lagunin A.A., Filimonov D.A. et al. In silico fragment-based drug design using PASS approach. SAR & QSAR in Environmental Research. 2012; 23(3-4): 279–296.
  22. Berman H.M., Westbrook J., Feng Z. et al. The protein data bank. Nucleic acids research. 2000; 28(1): 235–242.
  23. Liu Y., Yang X., Gan J. et al. CB-Dock2: Improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic acids research. 2022; 50(W1): W159–W164.
  24. Trott O., Olson A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry. 2010; 31(2): 455–461.
  25. Kochnev Y., Hellemann E., Cassidy K. et al. Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser. Bioinformatics. 2020; 36(16): 4513–4515.
  26. Sanner M.F. Python: a programming language for software integration and development. J Mol Graph Model. 1999; 17(1): 57–61.
  27. Wei G. A.I., Zhang Y., Ai L. et al. Screening of HMG-CoA reductase inhibitors from composite Salvia miltiorrhiza using AutoDock. Chinese Journal of Natural Medicines. 2010; 8(1): 51–56.
  28. Banerjee P., Eckert A. O., Schrey A. K. et al. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic acids research. 2018; 46(W1): W257–W263.
  29. Lipinski S.A. Lead-and drug-like compounds: the rule-of-five revolution. Drug discovery today: Technologies. 2004; 1(4): 337–341.
  30. Istvan E. S., Deisenhofer J. Structural mechanism for statin inhibition of HMG-CoA reductase. Science. 2001; 292(5519): 1160–1164.
  31. Istvan E.S., Palnitkar M., Buchanan S.K. et al. Crystal structure of the catalytic portion of human HMG-CoA reductase: insights into regulation of activity and catalysis. The EMBO journal. 2000; 19(5): 819–830.
  32. da Costa R.F., Freire V.N., Bezerra E.M. et al. Explaining statin inhibition effectiveness of HMG-CoA reductase by quantum biochemistry computations. Physical Chemistry Chemical Physics. 2012; 14(4): 1389–1398.

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Schematic representation of the proposed interaction of trifolirizine with the active center of HMG-CoA reductase: A – The docking position; B – Interaction with amino acid residues

下载 (212KB)
索引源数据
3. Fig. 2. Schematic representation of the proposed interaction of ononin with the active center of HMG-CoA reductase: A - Docking position; B – Interaction with amino acid residues

下载 (520KB)
索引源数据
4. Fig. 3. Schematic representation of the proposed interaction of rosuvastatin with the active center of HMG-CoA reductase: A – Docking position; B - Interaction with amino acid residues

下载 (521KB)
索引源数据

版权所有 © Russkiy Vrach Publishing House, 2024
##common.cookie##