Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/59366
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Chemijos inžinerija / Chemical engineering (T005);Farmacija / Pharmacy (M003)
Author(s): Drevinskas, Tomas;Mickienė, Rūta;Maruška, Audrius;Stankevičius, Mantas;Tiso, Nicola;Šalomskas, Algirdas;Lelešius, Raimundas;Karpovaitė, Agneta;Ragažinskienė, Ona
Title: Confirmation of the antiviral properties of medicinal plants via chemical analysis, machine learning methods and antiviral tests: a methodological approach
Is part of: Analytical methods. Cambridge : Royal society of chemistry, 2018, Vol. 10, iss. 16
Extent: p. 1875-1885
Date: 2018
Note: The research was funded by the Research Council of Lithuania, project No. MIP-065/2015. The Milton Roy Spectronic 1201 spectrophotometer was a donation from the Alexander von Humboldt Foundation. The HP3DCE capillary electrophoresis system was kindly donated by Dr Gerard Rozing and Dr HansPeter Schiefer (Agilent Technologies, Germany)
Keywords: Capillary electrophoresis;Antiviral plants;Medicinal plants;Chromatography-mass spectrometry
Abstract: Medicinal plants are reported to possess antiviral activity, but finding the substances that are responsible for antiviral activity in the complex mixture of the plant extract is an extremely difficult task. In this paper a methodology related to the determination of the antiviral properties of medicinal plant extracts and based on phytochemical analysis, antiviral tests and machine learning methods is described. 16 potentially antiviral medicinal plants were selected, and their chemometric characteristics and antiviral properties were investigated. Three different analytical methods were used for chemical analysis: (i) spectrophotometry, (ii) capillary electrophoresis with contactless conductivity detection, and (iii) gas chromatography-mass spectrometry. 14 attributes were obtained describing the composition of the plant extracts. Viral growth inhibition properties were investigated and 8 candidate plant extracts were selected as being active against viruses. Infectious bronchitis virus was used as a model virus. Machine learning techniques including deep neural network classification, classification and regression tree induction and hierarchical clusterization were used for mining the factors that are responsible for antiviral effects. It was determined that (i) phenolic compounds providing high radical scavenging activity and fractions containing high content of phenolic compounds are positively related to antiviral activity in plant extracts, (ii) hydrophilic compounds that are positively charged (pKa > 4.7) in acidic media and possess medium and low electrophoretic mobility properties are negatively related to antiviral activity in medicinal plants, (iii) phenolic acids with pKa lower than 4.7 are not related to antiviral activity in the extracts, and (iv) volatile compounds in the extracts, including diversity, quantity and different volatility properties, do not affect the antiviral activity in medicinal plant extracts
Internet: https://pubs.rsc.org/en/content/articlehtml/2018/ay/c8ay00318a
https://pubs.rsc.org/en/content/articlehtml/2018/ay/c8ay00318a
Affiliation(s): Gamtos mokslų fakultetas
Instrumentinės analizės atviros prieigos centras
Lietuvos sveikatos mokslų universitetas. Veterinarijos akademija
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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