Randomized trial uncovers quality markers in Paeonia ostii, indicating potential for medicinal use.
Paeonia ostii has been widely used in traditional medicine; however, the phytochemical basis underlying its multi-target bioactivities across different anatomical parts remains insufficiently characterized, limiting the establishment of scientifically grounded quality markers. In this study, an integrated activity-labeled molecular networking (ALMN) strategy was applied to systematically uncover core bioactive constituents from five distinct parts of P. ostii , including cortex Moutan, seed coat, leaf, fruit capsule, and stem. Three representative bioactivities relevant to metabolic and inflammatory disorders were evaluated, namely α -glucosidase inhibition, antioxidant capacity, and inhibition of nitric oxide production. By correlating LC-MS-based metabolomic profiles with quantitative bioassay data, ALMN enabled the visualization and prioritization of activity-associated molecular clusters within a complex chemical space. Several key compounds were subsequently identified and experimentally validated. Stilbene derivatives such as suffruticosols A-C and epsilon-viniferins exhibited pronounced antioxidant activity alongside moderate α-glucosidase inhibition, whereas methyl gallate and ethyl gallate displayed exceptionally potent α-glucosidase inhibitory effects, with IC₅₀ values of 0.39 and 0.28 μM, respectively, outperforming other identified phenolics. Enzyme kinetic analyses indicated a competitive inhibition mode for both compounds. In addition, flavonoids such as isorhamnetin showed balanced activity across multiple bioassays. Intersection analysis further highlighted ethyl gallate as a representative multi-target candidate, leading to the construction of a putative quality marker network. Collectively, this work elucidates the multi-target phytochemical basis of different P. ostii parts and demonstrates the utility of an ALMN driven framework for the rapid identification of quality markers in complex botanical systems.
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Wang et al. (2026) studied this question.
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