Issue 39, 2023

Quality control for a traditional Chinese medicine, Millettia speciosa Champ, using ultra-high-performance liquid chromatography fingerprint, serum pharmacochemistry and network pharmacology

Abstract

Millettia speciosa (M. speciosa) Champ (MSC) is a healthy food type with medicinal and edible homology, which is now considered a clinically significant anti-rheumatoid arthritis medicine. However, there is currently no standardized or generally accepted research strategy by which we can assess M. speciosa. Thus, it is essential to develop novel theories, strategies and evaluation methods for the scientific quality control of M. speciosa. Herein, our use ultra-high-performance liquid chromatography (UPLC)-MS/MS analysis identified 12 common bioactive components absorbed into MSC serum. Next, network pharmacology analysis exhibited that 5 MSC components may be those active components in treating rheumatoid arthritis and may be considered potential quality markers. These 5 components were then quantified using a fast UPLC approach, based on the quality marker of measurability, showing that lenticin can be regarded as the MSC quality marker. The cumulative study findings, based on systematic assessment of chemical composition both in vivo and in vitro, and the potential efficacy of M. speciosa, provide a novel approach for M. speciosa quality control.

Graphical abstract: Quality control for a traditional Chinese medicine, Millettia speciosa Champ, using ultra-high-performance liquid chromatography fingerprint, serum pharmacochemistry and network pharmacology

Supplementary files

Article information

Article type
Paper
Submitted
24 Jun 2023
Accepted
08 Sep 2023
First published
08 Sep 2023

Anal. Methods, 2023,15, 5166-5180

Quality control for a traditional Chinese medicine, Millettia speciosa Champ, using ultra-high-performance liquid chromatography fingerprint, serum pharmacochemistry and network pharmacology

Y. Nong, C. Zhang, Y. Guo, Y. Qin, X. Zhong, L. Feng, Z. Pan, L. Deng, H. Guo and Z. Su, Anal. Methods, 2023, 15, 5166 DOI: 10.1039/D3AY01051A

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