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Cheminformatics Application in the Phytochemical and Biological Study of Eucalyptus globulus L. Bark as a Potential Hepatoprotective Drug.

Khaled A NematallahSahar ElmekkawyMaha R A AbdollahMohey M ElmazarAli M El HalawanyMeselhy Ragab Meselhy
Published in: ACS omega (2022)
Natural products are considered as a good source of antifibrotic agents, but identifying and isolating bioactive molecule(s) is still challenging. Fortunately, numerous computational techniques have evolved to save time and efforts in this field. The aim of the current study was to utilize several cheminformatics software to study the chemical and biological features of the bark of Eucalyptus globulus cultivated in Egypt. Sirius software, with the aid of online databases, was used to process liquid chromatography-mass spectrometry (LC-MS) chemical profiling and predict precise molecular formulae, chemical classes, and structures. Accordingly, 37 compounds were tentatively identified, including 15 reported here for the first time from this species. Also, the BioTransformer tool was successfully applied for in silico virtual study of the human metabolism of these compounds, and 1960 different products were obtained through various metabolic pathways. Finally, an electronic library of the identified compounds and their metabolites were developed and docked in silico against eight different protein targets that are involved in the liver fibrosis process. The results revealed that the extract may have a potential hepatoprotective effect through several mechanisms and that the metabolites have the highest binding affinities to the relevant enzymes than their parent compounds. The extract was found to show potent cytotoxic activity against the liver cancer cell lines HEPG2 and HUH-7, and its absorption was enhanced through nanoformulation, as proved using the ex vivo everted gut sac method.
Keyphrases
  • mass spectrometry
  • liquid chromatography
  • liver fibrosis
  • emergency department
  • ms ms
  • healthcare
  • endothelial cells
  • machine learning
  • molecular dynamics simulations
  • big data
  • health information