Anti‐inflammatory activity of Lauraceae plant species and prediction models based on their metabolomics profiling data

de Alcântara, Bianca Gonçalves Vasconcelos and Neto, Albert Katchborian and Garcia, Daniela Aparecida and Casoti, Rosana and Oliveira, Tiago Branquinho and de Paula Ladvocat, Ana Claudia Chagas and Edrada-Ebel, RuAngelie and Soares, Marisi Gomes and Dias, Danielle Ferreira and Chagas de Paula, Daniela Aparecida (2023) Anti‐inflammatory activity of Lauraceae plant species and prediction models based on their metabolomics profiling data. Chemistry and Biodiversity, 20 (9). e202300650. ISSN 1612-1872 (https://doi.org/10.1002/cbdv.202300650)

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Abstract

The Lauraceae is a botanical family known for its anti‐inflammatory potential. However, several species have not yet been studied. Thus, this work aimed to screen the anti‐inflammatory activity of this plant family and to build statistical prediction models. The methodology was based on the statistical analysis of high‐resolution liquid chromatography coupled with mass spectrometry data and the ex vivo anti‐inflammatory activity of plant extracts. The ex vivo results demonstrated significant anti‐inflammatory activity for several of these plants for the first time. The sample data were applied to build anti‐inflammatory activity prediction models, including the partial least square acquired, artificial neural network, and stochastic gradient descent, which showed adequate fitting and predictive performance. Key anti‐inflammatory markers, such as aporphine and benzylisoquinoline alkaloids were annotated with confidence level 2. Additionally, the validated prediction models proved to be useful for predicting active extracts using metabolomics data and studying their most bioactive metabolites.

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