Phytochemical Characterization and Pharmacological Properties of Lichen Extracts from Cetrarioid Clade by Multivariate Analysis and Molecular Docking.

Evid Based Complement Alternat Med

Department of Pharmacology, Pharmacognosy and Botany, Faculty of Pharmacy, Universidad Complutense de Madrid, Plaza Ramon y Cajal S/n, Ciudad Universitaria, Madrid 28040, Spain.

Published: June 2022

Introduction: Lichens, due to the presence of own secondary metabolites such as depsidones and depsides, became a promising source of health-promoting organisms with pharmacological activities. However, lichens and their active compounds have been much less studied. Therefore, the present study aims to evaluate for the first time the antioxidant capacity and enzyme inhibitory activities of 14 lichen extracts belonging to cetrarioid clade in order to identify new natural products with potential pharmacological activity.

Materials And Methods: In this study, an integrated strategy was applied combining multivariate statistical analysis (principal component analysis and hierarchical cluster analysis), phytochemical identification, activity evaluation ( battery of antioxidant assays FRAP, DPPH, and ORAC), and enzyme inhibitory activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) and molecular profiling with docking studies of the most promising secondary metabolites. Among fourteen lichen samples, stands out for its higher antioxidant capacities, followed by , , and . Moreover, and extracts were the best inhibitors of AChE and BuChE. The major secondary metabolites identified by HPLC were alectoronic acid and -collatolic acid for and usnic acid and protolichesterinic acid for . Molecular docking studies revealed that alectoronic acid exhibited the strongest binding affinity with both AChE and BuChE with and without water molecules.

Conclusions: Our results concluded that these species could be effective in the treatment of neurodegenerative diseases, being mandatory further investigation in cell culture and models.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187481PMC
http://dx.doi.org/10.1155/2022/5218248DOI Listing

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