Biochemometrics to Identify Synergists and Additives from Botanical Medicines: A Case Study with <i>Hydrastis canadensis</i> (Goldenseal)

A critical challenge in the study of botanical natural products is the difficulty of identifying multiple compounds that may contribute additively, synergistically, or antagonistically to biological activity. Herein, it is demonstrated how combining untargeted metabolomics with synergy-directed fractionation can be effective toward accomplishing this goal. To demonstrate this approach, an extract of the botanical goldenseal (<i>Hydrastis canadensis)</i> was fractionated and tested for its ability to enhance the antimicrobial activity of the alkaloid berberine (<b>4</b>) against the pathogenic bacterium <i>Staphylococcus aureus</i>. Bioassay data were combined with untargeted mass spectrometry-based metabolomics data sets (biochemometrics) to produce selectivity ratio (SR) plots, which visually show which extract components are most strongly associated with the biological effect. Using this approach, the new flavonoid 3,3′-dihydroxy-5,7,4′-trimethoxy-6,8-<i>C</i>-dimethylflavone (<b>29</b>) was identified, as were several flavonoids known to be active. When tested in combination with <b>4</b>, <b>29</b> lowered the IC₅₀ of <b>4</b> from 132.2 ± 1.1 μM to 91.5 ± 1.1 μM. In isolation, <b>29</b> did not demonstrate antimicrobial activity. The current study highlights the importance of fractionation when utilizing metabolomics for identifying bioactive components from botanical extracts and demonstrates the power of SR plots to help merge and interpret complex biological and chemical data sets

Biochemometrics for Natural Products Research: Comparison of Data Analysis Approaches and Application to Identification of Bioactive Compounds

A central challenge of natural products research is assigning bioactive compounds from complex mixtures. The gold standard approach to address this challenge, bioassay-guided fractionation, is often biased toward abundant, rather than bioactive, mixture components. This study evaluated the combination of bioassay-guided fractionation with untargeted metabolite profiling to improve active component identification early in the fractionation process. Key to this methodology was statistical modeling of the integrated biological and chemical data sets (biochemometric analysis). Three data analysis approaches for biochemometric analysis were compared, namely, partial least-squares loading vectors, S-plots, and the selectivity ratio. Extracts from the endophytic fungi <i>Alternaria</i> sp. and <i>Pyrenochaeta</i> sp. with antimicrobial activity against <i>Staphylococcus aureus</i> served as test cases. Biochemometric analysis incorporating the selectivity ratio performed best in identifying bioactive ions from these extracts early in the fractionation process, yielding altersetin (<b>3</b>, MIC 0.23 μg/mL) and macrosphelide A (<b>4</b>, MIC 75 μg/mL) as antibacterial constituents from <i>Alternaria</i> sp. and <i>Pyrenochaeta</i> sp., respectively. This study demonstrates the potential of biochemometrics coupled with bioassay-guided fractionation to identify bioactive mixture components. A benefit of this approach is the ability to integrate multiple stages of fractionation and bioassay data into a single analysis

Comparison of Metabolomics Approaches for Evaluating the Variability of Complex Botanical Preparations: Green Tea (<i>Camellia sinensis</i>) as a Case Study

A challenge that must be addressed when conducting studies with complex natural products is how to evaluate their complexity and variability. Traditional methods of quantifying a single or a small range of metabolites may not capture the full chemical complexity of multiple samples. Different metabolomics approaches were evaluated to discern how they facilitated comparison of the chemical composition of commercial green tea [<i>Camellia sinensis</i> (L.) Kuntze] products, with the goal of capturing the variability of commercially used products and selecting representative products for in vitro or clinical evaluation. Three metabolomic-related methodsuntargeted ultraperformance liquid chromatography–mass spectrometry (UPLC-MS), targeted UPLC-MS, and untargeted, quantitative ¹HNMRwere employed to characterize 34 commercially available green tea samples. Of these methods, untargeted UPLC-MS was most effective at discriminating between green tea, green tea supplement, and non-green-tea products. A method using reproduced correlation coefficients calculated from principal component analysis models was developed to quantitatively compare differences among samples. The obtained results demonstrated the utility of metabolomics employing UPLC-MS data for evaluating similarities and differences between complex botanical products