A major goal in the discovery of bioactive natural products is to rapidly identify active compound(s) and dereplicate known molecules from complex biological extracts. The conventional bioassay-guided fractionation process can be time consuming and often requires multi-step procedures. Herein, we apply a metabolomic strategy merging multivariate data analysis and multi-informative molecular maps to rapidly prioritize bioactive molecules directly from crude plant extracts. The strategy was applied to 59 extracts of three Bacopa species (B. monnieri, B. caroliniana and B. floribunda), which were profiled by UHPLC-HRMS2 and screened for anti-lipid peroxidation activity. Using this approach, six lipid peroxidation inhibitors 1‒6 of three Bacopa spp. were discovered, three of them being new compounds: monnieraside IV (4), monnieraside V (5) and monnieraside VI (6). The results demonstrate that this combined approach could efficiently guide the discovery of new bioactive natural products. Furthermore, the approach allowed to evidence that main semi-quantitative changes in composition linked to the anti-lipid peroxidation activity were also correlated to seasonal effects notably for B. monnieri.
Keywords: Bacopa monnieri; LC-MS; metabolomics; molecular network; multivariate data analysis.