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Salvia verticillata (L.)-Biological Activity, Chemical Profile, and Future Perspectives.

Stanislava IvanovaZoya DzhakovaRadiana StaynovaKalin Ivanov
Published in: Pharmaceuticals (Basel, Switzerland) (2024)
Species belonging to the genus Salvia , Lamiaceae, have been deeply involved in the folk medicine of different nations since ancient times. Lilac sage, or Salvia verticillata L. ( S. verticillata ) is a less studied species from the genus. However, it seems to have a prominent potential for the future drug discovery strategies of novel phytopharmaceuticals. This review aims to summarise the data on the biological activity and the phytochemical profile of extracts and essential oils derived from S. verticillata . This review is based on data from 57 in vitro and in vivo studies. The chemical profile of S. verticillata includes different synergic compounds like phenolic acids, flavonoids, terpenes, and salvianolic acids. Although some small amounts of salvianolic acid B were found in S. verticillata extracts, the major compound among the salvianolic acids is salvianolic acid C, a compound associated with the potential for improving liver fibrosis, cardio- and hepatoprotection, and the inhibition of SARS-CoV-2 infection. The cannabinoid type 2 receptor agonist β-caryophyllene is one of the major compounds in S. verticillata essential oils. It is a compound with a prominent potential in regenerative medicine, neurology, immunology, and other medical fields. The in vivo and the in vitro studies, regarding S. verticillata highlighted good antioxidant potential, anti-inflammatory, antibacterial, and antifungal activity. S.verticillata was also reported as a potential source of drug candidates for the treatment of neurodegenerative diseases such as Alzheimer's disease, because of the inhibitory activity on the acetylcholinesterase. However, the number of studies in this direction is limited.
Keyphrases
  • anti inflammatory
  • drug discovery
  • liver fibrosis
  • healthcare
  • human health
  • emergency department
  • electronic health record
  • oxidative stress
  • risk assessment
  • coronavirus disease
  • deep learning
  • drug induced
  • current status