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Biological Activities of Lichen-Derived Monoaromatic Compounds.

Thanh-Hung DoThuc-Huy DuongHuy Truong NguyenThi-Hien NguyenJirapast SichaemChuong Hoang NguyenHuu-Hung NguyenNguyen Phuoc Long
Published in: Molecules (Basel, Switzerland) (2022)
Lichen-derived monoaromatic compounds are bioactive compounds, associated with various pharmacological properties: antioxidant, antifungal, antiviral, cytotoxicity, and enzyme inhibition. However, little is known about data regarding alpha-glucosidase inhibition and antimicrobial activity. Very few compounds were reported to have these activities. In this paper, a series of monoaromatic compounds from a lichen source were isolated and structurally elucidated. They are 3,5-dihydroxybenzoic acid ( 1 ), 3,5-dihydroxybenzoate methyl ( 2 ), 3,5-dihydroxy-4-methylbenzoic acid ( 3 ), 3,5-dihydroxy-4-methoxylbenzoic acid ( 4 ), 3-hydroxyorcinol ( 5 ), atranol ( 6 ), and methyl hematommate ( 7 ). To obtain more derivatives, available compounds from the previous reports such as methyl β-orsellinate ( 8 ), methyl orsellinate ( 9 ), and D-montagnetol ( 10 ) were selected for bromination. Electrophilic bromination was applied to 8 - 10 using NaBr/H 2 O 2 reagents to yield products methyl 5-bromo-β-orsellinate ( 8a ), methyl 3,5-dibromo-orsellinate ( 9a ), 3-bromo-D-montagnetol ( 10a ), and 3,5-dibromo-D-montagnetol ( 10b ). Compounds were evaluated for alpha-glucosidase inhibition and antimicrobial activity against antibiotic-resistant, pathogenic bacteria Enterococcus faecium , Staphylococcus aureus , and Acinetobacter baumannii . Compound 4 showed stronger alpha-glucosidase inhibition than others with an IC 50 value of 24.0 µg/mL. Synthetic compound 9a exhibited remarkable activity against Staphylococcus aureus with a MIC value of 4 µg/mL. Molecular docking studies were performed to confirm the consistency between in vitro and in silico studies.
Keyphrases
  • molecular docking
  • staphylococcus aureus
  • acinetobacter baumannii
  • molecular dynamics simulations
  • drug resistant
  • multidrug resistant
  • pseudomonas aeruginosa
  • oxidative stress
  • machine learning
  • big data
  • drug induced