Prediction of Antiosteoporosis Activity of Thirty-Nine Phytoestrogen Compounds in Estrogen Receptor-Dependent Manner Through In Silico Approach doi.org/10.26538/tjnpr/v5i10.6
Main Article Content
Abstract
Osteoporosis is one of the health problems in postmenopausal women due to estrogen deficiency. Phytoestrogen compounds can be used as an alternative osteoporosis treatment because of their similarity in structure and activity to estrogen. This research was conducted to predict the antiosteoporosis activity of thirty-nine phytoestrogen compounds and raloxifene, a modern antiosteoporosis drug in silico. The first step of the study involved the analysis of physicochemical properties of thirty-nine compounds and raloxifene using the SwissADME web tool. Compounds that met the criteria of the physicochemical properties were then subjected to molecular docking using PyRx 0.8 software with the AutoDock Vina method. The results were analyzed using Biovia Discovery Studio Visualizer 2016 software to find one or more compounds that predicted ERβ agonists. Finally, a toxicity test using the pkCSM web tool on the predicted agonist compounds was conducted to determine the values of hepatoxicity, skin sensitization, and Ames toxicity. AdmetSAR2 web tool was also used to predict the LD50 class of toxicity. The results of this in silico study revealed that raloxifene and 23 compounds displayed agonist interaction toward ERβ, and two of these compounds, namely catechin and epicatechin, were predicted agonist to ERβ with binding values of -5.6 and -5.9 kcal/mol, respectively. These two compounds also showed the lowest toxicity. The finding from this research indicated that catechin and epicatechin are the most potent and non-toxic antiosteoporosis compounds among the 39 phytoestrogens.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Aditama APR, Ma’arif B, Mirza DM, Laswati H, Agil M. In vitro and in silico analysis on the bone formation activity of N-Hexane fraction of Semanggi (Marsilea crenata Presl.). Sys Rev Pharm. 2020; 11(11):837-849.
Ramadani M. Osteoporosis Risk Factors and Efforts for Prevention. Kemas. 2010; 4(2):111-115
Villa A, Vegeto E, Poletti A, Maggi A. Estrogens, Neuroinflammation and Neurodegeneration. Endocrine Rev. 2016; 37(4):371-402.
Pratama NR, Yunita E, Tyas DRA. Ekstrak Kulit Pisang Kepok (Mlisa paradisiilca L.) as Phytoestrogens on Breast Gland Development of Ovariectomized Rats through Enhanced C-Myc Expression. Saintifika. 2011; 3(1):19-26
Zhou LP, Wong KY, Yeung HT, Dong XL, Xiao HH, Gong AGW, Tsim KWK and Wong MS. Bone Protective Effects of Danggui Buxue Tang Alone and in Combination With Tamoxifen or Raloxifene in vivo and in vitro. FrontPharmacol. 2018; 9(779):1-15.
Rietjens IM, Sotoca AM, Vervoort J, Louisse J. Mechanism Underlying the Dualistic Mode of Action of Major Soy Isoflavones in Relation to Cell Proliferation and Cancer Risk. Mol. Nutr. Food Res. 2013; 57(1):100-113.
Ma’arif B, Fitri H, Saisah NL, Najib LA, Yuwafi AH, Atmaja RRD, Inayatillah FR, Dianti MR, Laswati H, Agil M. Prediction of compounds with antiosteoporosis activity in Chrysophyllum cainito L. leaves through in silicoapproach. J Basic Clin Physiol Pharmacol. 2021; 32(4):803-808.
Ososki AL and Kennelly EJ. Phytoestrogens: a review of the present state of research. Phytother Res. 2003;17(8):845-869.
Yang TS, Wang SY, Yang YC, Su CH, Lee FK, Chen SC, Tseng CY, Jou HJ, Huang JP, Huang KE. Effects of
standardized phytoestrogen on Taiwanese menopausal women. Taiwan J Obstet Gynecol. 2012; 51(2):229-235.
Sirotkin AV and Harrath AH. Phytoestrogen and Their Effects. Eur J Pharmacol. 2014; 741(1):230-236.
Moutsatsou P. The spectrum of phytoestrogens in nature: our knowledge is expanding. Hormones. 2007; 6(3):173-193.
Wocławek-Potocka I, Mannelli C, Boruszewska D, Zleba IK, Wasniewski T, Skarzynski DJ. Diverse Effects of Phytoestrogens on the Reproductive Performance: Cow as a Model. Int J Endocrinol. 2013; 2013:1-15.
Mekinic IG, Skroza D, Ljubenkov I, Katalinic V. Insight into the Presence of Stilbenes in Medicinal Plants Traditionally Used in Croatian Folk Medicine. Nat Prod Comm. 2016; 11(6):833-835.
Panche AN, Diwan AD, Chandra SR. Flavonoids: an overview. J Nutr Sci. 2016; 5(47):1-15.
Sharma V and Ramawat KG. Isoflavonoids. In: Ramawat K, Mérillon JM. (eds) Natural Products. Springer, Berlin, Heidelberg; 2013. 1849-1865p.
Brodowska KM. Natural flavonoids: classification, potential role, and application of flavonoid analogues. Eur J Biol Res. 2017; 7(2):108-123.
Nikolic IL, Savic-Gajic IM, Tacic AD, Savic IM. Classification and Biological Activity of Phytoestrogens: A Review. Adv Technol. 2017; 6(2):96-106.
Martínez FDP, Arciniega M, Medina-Franco JL. Molecular Docking: Current Advances and Challenges. TIP Rev EspCienc Quím Biol. 2018; 21(1):1-23.
Noori HR and Spanagel R. In silico pharmacology: drug design and discovery’s gate to the future. In silicoPharmacol. 2013; 1(1):1-2.
Wadood A, Ahmed N, Shah L, Ahmad A, Hassan H, Shams S. In silico Drug Design: An Approach Whish Revolutionarised the Drug Discovery Process. OA Drug Design Deliv. 2013; 1(1):3.
Mçcklinghoff S, Rolf R, Maelle C, Arie V, Christian O, Luc B. Synthesis and Crystal Structure of a Phosphorylated Estrogen Receptor Ligand Binding Domain. ChemBioChem. 2010; 11(16):2251-2254.
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; 7:1-13.
Chagas CM, Moss S, Alisaraiea L. Drug metabolites and their effects on the development of adverse reactions: Revisiting Lipinski’s Rule of Five. Int J Pharm. 2018;549(1-2):133-149.
Hanwell MD, Donald EC, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: An advanced semanticchemical editor, visualization, and analysis platform. JCheminform. 2012; 4(1):17.
Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev. 2014; 66: 334-395.
Jejurikar BL and Rohane SH. Drug Designing in Discovery Studio. Asian J Res Chem. 2021; 14(2):135-138.
Sandeep G, Nagasree KP, Hanisha M, Kumar K. Audocker LE: a GUI for virtual screnning with autodock vina. BMC Res Notes. 2011; 4(1):445.
Trott O and Olson AJ. Software News and Update Autodock Vina: Improving the Speed and Accurary of Docking with a New Scoring Function, Efficient Optimazation and Multithreading. J Comput Chem. 2009;31(2):455-461.
Rachmania RA, Hariyanti, Zikriah R, Soultan A. In silicostudy of alkaloids of Crimum asiaticum in the inhibition of COX enzyme. J Kim Val. 2018; 4(2):124-136.
Xue DAI and Jie WU. Selective estrogen receptor modulator: Raloxifene. J Reprod Contracep. 2011;
(1):51-60.
Muchtaridi M, Dermawan D, Yusuf M. Molecular Docking, 3D Structure-Based Pharmacophore Modeling, and ADME Prediction of Alpha Mangostin and Its Derivatives against Estrogen Receptor Alpha. J Young Pharm. 2018; 10(3):252-259.
Siswandono and Soekardjo B. Medicinal Chemistry. Surabaya : Airlangga University Press; 1995; 381p.
Ruswanto, Mardhiah, Mardianingrum R, Novitriani K. Synthesis and in silico study of 3-Nitro-N'-[(Pyridin-4-Yl) Carbonyl] Benzohydrazide as antituberculosis. Chimica etNatura Acta. 2015; 3(2):54-61.
Vasavi CS, Goyal A, Divya G, Munusami P. In silico analysis of interactions in heme binding proteins. Int J Pharm Pharm Sci. 2015; 7(1):354-359.
Giordanetto F, Tyrchan C, Ulander J. Intramolecular Hydrogen Bond Expectations in Medicinal Chemistry. ACS Med Chem Lett. 2017; 8(2):139-142.
Riaz N, Shahbaz A, Kalssom S. Ligand Based Pharmacophore Model Development for the Identification of Novel Anti-Psychotic Drugs. Appl Sci Res Rev. 2018;5(2):1-10.
Cheng A. In silico Prediction of Hepatotoxicity. Curr Comput-Aid Drug. 2009; 5(2):122-127.
Verheyen GR, Braeken E, Deun KV, Miert SV. Evaluation of in silico tools to predict the skin sensitization potential of chemicals. SAR QSAR Environ Res. 2012; 28(1):59-73.
Alba MA, Montoya RM, Aguirre JJE. The Ames Test in Twenty-first Century. Res Rev J Toxicol. 2012; 2(1):23-37.
Malik A, Manan A, Mirza MU. Molecular docking and in silico ADMET studies of silibinin and glycyrrhetic acid anti-inflammatory activity. Trop J Pharm Res. 2017;16(1):67-74.
Gadaleta D, Vukovic K, Toma C, Lavado GJ, Karmaus AL. SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data. J Cheminform. 2019; 11(1):58-73.
Kini U and Nandeesh BN. Physiology of bone formation, remodeling, and metabolism. In: Radionuclide and hybrid bone imaging. Springer, Berlin, Heidelberg; 2012; 29-57p.
Huang H, Cheng T, Lin S, Ho C, Chyu JY, Yang R, Chen C, Shen C. Osteoprotective Roles of Green Tea Catechins. Antioxidants. 2020; 9(11):1136.
Zanwar AA, Badole SL, Shende PS, Hegde MV, Bodhankar SL. Antioxidant Role of Catechin in Health and Disease.Polyphenols in Human Health and Disease. Acad Press.2014; 1:267-271.