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.
Plants produce numerous secondary metabolites with diverse physicochemical properties. Because different parts of a single plant produce various components, several spectroscopic methods are necessary to inspect their chemical profiles. Mass spectral data are recognized as one of the most useful tools for analyzing components with a wide range of polarities. However, interpreting mass spectral data generated from positive and negative ionization modes is a challenging task because of the diverse chemical profiles of secondary metabolites. Herein, we combine and analyze mass spectral data generated in two ionization modes to detect as many metabolites as possible using the molecular networking approach. We selected different parts of a single plant, Morus alba (Moraceae), which are used in the functional food and medicinal herb industries. The mass spectral data generated from two ionization modes were combined and analyzed using various molecular networking workflows. We confirmed that our approach could be applied to simultaneously analyze the different types of secondary metabolites with different physicochemical properties.
<p>Six new compounds<b> </b>(<b>1−6</b>) were isolated from the leaves of <i>Viburnum erosum</i> along with four known compounds <b>7−10</b>. The structures were determined by NMR and MS spectroscopic analyses, and their absolute configurations were established by chemical and spectroscopic methods. Compounds <b>1–6</b> were <i>α</i>-glucosidic hydroquinone derivatives with different linear monoterpenoid structures. Compounds <b>1−10 </b>were also evaluated for their tyrosinase inhibitory activities, and <b>10</b> showed potent inhibition of tyrosinase enzyme with IC<sub>50</sub> of 37.9 <i>μ</i>M compared to 47.6 <i>μ</i>M of the positive control (<i>β</i>-arbutin).</p>
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