Traditional East Asian medicine not only serves as a potential source of drug discovery, but also plays an important role in the healthcare systems of Korea, China, and Japan. Tandem mass spectrometry (MS/MS)-based untargeted metabolomics is a key methodology for high-throughput analysis of the complex chemical compositions of medicinal plants used in traditional East Asian medicine. This Data Descriptor documents the deposition to a public repository of a re-analyzable raw LC-MS/MS dataset of 337 medicinal plants listed in the Korean Pharmacopeia, in addition to a reference spectral library of 223 phytochemicals isolated from medicinal plants. Enhanced by recently developed repository-level data analysis pipelines, this information can serve as a reference dataset for MS/MS-based untargeted metabolomic analysis of plant specialized metabolites.
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.
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