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Small Molecular Accurate Recognition Technology (SMART 2.0) has recently been introduced as a NMR-based machine learning tool for the discovery and characterization of natural products. We attempted targeted isolation of sesquiterpene lactones from Eupatorium fortunei with the aid of structural annotation by SMART 2.0 and chemical profiling. Eight germacrene-type ( 1–7 and 10 ) and two eudesmane-type sesquiterpene lactones ( 8 and 9 ) were isolated from the whole plant of Eupatorium fortunei . With the guidance of the results of the subfractions from E. fortunei obtained by SMART 2.0, their cytotoxic activities were evaluated against five cancer cells (SKOV3, A549, PC3, HEp-2, and MCF-7). Compounds 4 and 8 exhibited IC 50 values of 3.9 ± 1.2 and 3.9 ± 0.6 μM against prostate cancer cells, PC3, respectively. Compound 7 showed good cytotoxicity with IC 50 values of 5.8 ± 0.1 μM against breast cancer cells, MCF-7. In the present study, the rapid annotation of the mixture of compounds in a fraction by the NMR-based machine learning tool helped the targeted isolation of bioactive compounds from natural products.

Many natural product chemists are working to identify a wide variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds. Here, we developed liquid chromatography/mass spectrometry (LC/MS) data-processing protocols for assessing high-throughput spectral data from natural sources and scoring the novelty of unknown metabolites from natural products. This approach automatically produces representative MS spectra (RMSs) corresponding to single secondary metabolites in natural sources. In this study, we used the RMSs of Agrimonia pilosa roots and aerial parts as models to reveal the structural similarities of their secondary metabolites and identify novel compounds, as well as isolation of three types of nine new compounds including three pilosanidin- and four pilosanol-type molecules and two 3-hydroxy-3-methylglutaryl (HMG)-conjugated chromones. Furthermore, we devised a new scoring system, the Fresh Compound Index (FCI), which grades the novelty of single secondary metabolites from a natural material using an in-house database constructed from 466 representative medicinal plants from East Asian countries. We expect that the FCIs of RMSs in a sample will help natural product chemists to discover other compounds of interest with similar chemical scaffolds or novel compounds and will provide insights relevant to the structural diversity and novelty of secondary metabolites in natural products.

− DF formula is comprised of three traditional herbs, Ephedra intermedia, Rheum palmatum and Lithospermum erythrorhizon, and locally used for treating of the metabolic diseases, such as obesity and diabetes in Korea. We tried to optimize the extraction conditions of two major components, (−)-ephedrine and (+)-pseudoephedrine, in DF formula using response surface methodology with Box-Behnken design (BBD). The experimental conditions with 70% for EtOH concentrations, 4.8 hour for extraction hours and 8.7 times for the solvent to material ratio were suggested for the optimized extraction of DF formula with the highest amounts of (−)-ephedrine and (+)-pseudoephedrine in the designed model.