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This study is devoted to investigation of 10 iridoids applying quantitative structure-activity relationship analysis (QSAR) to correlate and predict their hepatoprotective activity. Iridoids, the largest class of monoterpenoids, are widespread group of substances occurring in various plant organisms. Quantum-chemical descriptors were calculated by semi-empirical RM1 approach. The obtained model is useful for description of iridoids hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted iridoids. The model obtained in our study shows not only statistical significance, but also an excellent predictive ability. The estimated predictive ability (r 2 test) of the model for the external set is 0.99.

  • Ссылка в интернете
  • DOIhttps://dx.doi.org/10.36522/2181-9637-2020-4-16
  • Дата создание в систему UzSCI07-04-2022
  • Количество прочтений211
  • Дата публикации10-09-2020
  • Язык статьиIngliz
  • Страницы159-165
Ўзбек

Мазкур тадқиқот иши миқдорий нисбат тузилиши фаоллигини (МНТФ) қўллаш орқали 10 та иридоиднинг гепатопротекторлик фаоллигини коррелация ва башорат қилишни ўрганишга бағишланган. Иридоидлар ўсимликнинг турли қисмларида учрайдиган монотерпеноидларнинг энг катта синфи ҳисобланади. Квант-кимёвий дискрипторлар ярим эмперик RM1 усулини қўллаш орқали ҳисобланди. Олинган модель иридоидларнинг гепатоҳимоя фаоллигини ёритиш учун фойдали ва уни янги иридоидларнинг гепатопротекторлик фаоллигига баҳо беришда қўлласа бўлади. Тадқиқотлар натижасида олинган модель нафақат яхши статистик кўрсаткич, балки яхши башорат қилиш қобилиятини ҳам кўрсатди. Моделнинг башорат қилиш кўрсаткичи (r2test) 0,99 ни ташкил қилади.

Русский

Данная статья посвящена исследованию 10 иридоидов с применением метода количественного соотношения структурной активности (КССА) для корреляции и прогнозирования гепатопротекторной активности иридоидов. Они относятся к самому большому классу монотерпеноидов, представляют собой широко распространенную группу веществ, встречающихся в различных растительных организмах. Квантово-химические дескрипторы были рассчитаны с помощью полуэмпирического подхода RM1. Полученная модель полезна для описания гепатозащитной активности иридоидов и может быть использована для оценки гепатозащитной активности новых иридоидов. Модель, полученная в нашем исследовании, продемонстрировала не только статистическую значимость, но и превосходную прогностическую способность. Предполагаемая прогностическая способность модели (r2test) для внешнего набора составляет 0,99.

English

This study is devoted to investigation of 10 iridoids applying quantitative structure-activity relationship analysis (QSAR) to correlate and predict their hepatoprotective activity. Iridoids, the largest class of monoterpenoids, are widespread group of substances occurring in various plant organisms. Quantum-chemical descriptors were calculated by semi-empirical RM1 approach. The obtained model is useful for description of iridoids hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted iridoids. The model obtained in our study shows not only statistical significance, but also an excellent predictive ability. The estimated predictive ability (r 2 test) of the model for the external set is 0.99.

№ Название ссылки
1 Tietze L.F., 1983, Secologanin, a Biogenetic Key Compound – Synthesis and Biogenesis of the Iridoid and Secoiridoid Glycosides // Angew Chem Int Ed, 22:828.
2 Ghisalberti, E.L., 1998, Biological and pharmacological activity of naturally occurring iridoids and secoiridoids // Phytomedicine, 5:147
3 Hegnauer, R., 1986, Chemotaxonomie der Pflanzen Hyperchem 8.0.10. HyperCube Inc. Retrieved from http://www.hyper.com
4 El-Naggar, L.J.; Beal J.L. Iridoids. A Review. // J. Nat. Prod., 1980, 43, 649.
5 Andrade, K.Q., Moura, F.A., Dos Santos, J.M., Oxidative stress and inflammation in hepatic diseases: Therapeutic possibilities of N-acetylcysteine.// Int J Mol Sci., 2015 16:30269.
6 Czaja, A.J., World, J. Hepatic inflammation and progressive liver fibrosis in chronic liver disease.// Gastroenterol. – 2014, 20:2515
7 Tilg, H., Moschen, A.R. Evolution of inflammation in nonalcoholic fatty liver disease: The multiple parallel hits hypothesis. // Hepatology. 2010 52:1836
8 Wei Peng. Hepatoprotective activity of total iridoid glycosides isolated from Paederia scandens (lour.) Merr. var. tomentosa. / Wei Peng, Xiao-Qian Qiu, Zhi-Heng Shu, Qing-Chun Liu, Mei-Bian Hub, Ting Han, Khalid Rahman, Lu-Ping Qin, Cheng-Jian Zheng // Journal of Ethnopharmacology. – 2015. – N 174. – Рр. 317-321.
9 Kang Dai. Hepatoprotective activity of iridoids, seco-iridoids and analog glycosides from gentianaceae on HepG2 cells via CYP3A4 induction and mitochondrial pathway. / Kang Dai, Xue-Jia Yi, Xian-Ju Huang, Azhar Muhammad, Mei Li, Jun Li, Guang-Zhong Yanga, Yue Gaoc // Food Funct. – 2018. – N 9. – Р. 2673.
10 Saraswat, B.; Visen, P.K.; Patnaik, G.K.; Dhawan, B.N. Protective effect of picroliv, active constituent of Picrorhiza kurrooa, against oxytetracycline induced hepatic damage.// Indian J. Expt. Biol., 1997, 35, 1302.
11 Rastogi, R.; Srivastava, A.K.; Srivastava,M.; Rastogi, A.K. Hepatocurative Effect of Picroliv and Silymarin Against Aflatoxin B1 Induced Hepatotoxicity in Rats. //Planta Med., 2000, 66, 709.
12 Rastogi, R.; Srivastava, A.K.; Rastogi, A.K. Biochemical changes induced in liver and serum of aflatoxin B1-treated male wistar rats: Preventive effect of picroliv. // Pharmacol. Toxicol., 2001, 88, 53.
13 Singh, M.; Tiwari, V.; Jain, A.; Ghoshal, S. Antioxidant potential of plumieride against CCl4-induced peroxidative damage in rats. //Indian J. Med. Res., 2005, 121, 676
14 Syrov V.N. Hepatoprotector activity of iridoid glycosides with respect to heliotrineinduced acute toxic liver damage in mice // Pharmaceutical Chemistry Journal. – 1999. – N 33. – Р. 410-412.
15 Corporation website HyperCube Inc., Gainesville, USA [Electronic resource]. – Аccess mode: http://www.hyper.com/.
16 Rocha G.B. RM1: A reparameterization of AM1 for H, C, N, O, P, S, F, Cl, Br, and I. / G.B. Rocha; R.O. Freire; A.M. Simas; J.J.P. Stewart // Comput Chem. – 2006, – N 27 (10). – Рр. 1101-1111.
17 Davis, L. Handbook of Genetic Algorithms // Van Nostrand Reinhold. – New York, 1991.
18 Devillers J. Genetic Algorithms in Molecular Modeling // Academic Press. – London, 1996.
19 Gramatica P. QSARINS: A new software for the development, analysis, and validation of QSAR MLR models. / P. Gramatica, N. Chirico, E. Papa, S. Kovarich, S. Cassani // Journal of Computational Chemistry, Software news and updates. – 2013. – N 34. – Рр. 2121-2132.
20 Gramatica P. QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. / P. Gramatica, S. Cassani, N. Chirico // Journal of Computational Chemistry, Software news and updates. – 2014. – N 35.
21 Todeschini R. DRAGON software for the Calculation of Molecular Descriptors / R. Todeschini, V. Consonni, A. Mauri, M. Pavan. – 2004. – V. 5.1 for Windows.
22 Todeschini R., Consonni V. Handbook of Molecular Descriptors // Wiley-VCH. – Weinheim and New York. – 2000.
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