Evaluation of the anti-inflammatory activity of Acacia polyphylla and identification of a new apigenin-3-C- glycosylated type flavonoid.

Owing to the potentially harmful adverse effects of current anti-inflammatory drugs, there is a need to identify new alternative substances. Thus, this study aimed to perform a phytochemical analysis of A. polyphylla to identify compounds responsible for its anti-inflammatory activity. Several fractions of the A. polyphylla extract were obtained and evaluated in an ex vivo anti-inflammatory assay using fresh human blood. Among the evaluated fractions, the BH fraction displayed the highest percentage of PGE2 inhibition (74.8%) compared to the reference drugs dexamethasone and indomethacin, demonstrating its excellent potential for anti-inflammatory activity. Astragalin (P1), a known 3-O-glucoside of kaempferol, was isolated from the A. polyphylla extract for the first time. In addition, a new compound (P2) was isolated and identified as the apigenin-3-C-glycosylated flavonoid. Astragalin showed moderate PGE2 activity (48.3%), whereas P2 was not anti-inflammatory. This study contributes to the phytochemical studies of A. polyphylla and confirms its anti-inflammatory potential.

Anti-Inflammatory Activity of Lauraceae Plant Species and Prediction Models Based on Their Metabolomics Profiling Data.

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.