An Integrated Strategy for Comprehensive Characterization of Traditional Chinese Medicine (TCM) Formulas: A Case Study of Gegen-Qinlian Decoction.

IF 3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Phytochemical Analysis Pub Date : 2025-06-02 DOI:10.1002/pca.3547

Zhitian Peng, Chongsheng Peng, Ying Peng, Xiaobo Li

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引用次数: 0

Abstract

Objective: An integrated strategy was utilized in this study to comprehensively characterize the chemical composition of Gegen-Qinlian Decoction (GQD).

Methods: The major components of GQD (total flavonoids, total saponins, total alkaloids, carbohydrates, and proteins) were determined firstly using colorimetric methods, while free amino acids and trace elements were analyzed by automatic amino acid analyzer and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. After that, the small-molecule components of GQD were characterized using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS). Furthermore, a quantitative method was established for 26 representative compounds determination simultaneously using ultra-high performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (UHPLC-QTRAP-MS/MS) in multiple reaction monitoring (MRM) mode.

Results: The accurate content and proportion of the major constituents in GQD were determined for the first time. A total of 230 compounds were characterized from GQD, of which four undescribed alkaloids were identified initially by their tandem mass data analyses based on mass spectral fragmentation pathways of isoquinoline alkaloids. A quantitative method was established to determine 26 representative compounds, and validated through linearity, precision, repeatability, stability, and recovery. The total content of the 26 compounds accounted for 27.81%, of which baicalin being the most abundant was up to 107.26 ± 1 mg/g.

Conclusions: The present study achieved a comprehensive characterization of constituents in GQD, laying the foundation for elucidating the active components, mechanism, and the quality control of GQD, offering a valuable analytical framework for research on other traditional Chinese medicine (TCM) formulas.

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中药方剂综合表征的整合策略——以葛根芩连汤为例

目的：采用综合策略对葛根芩连汤的化学成分进行综合表征。方法：首先采用比色法测定GQD的主要成分（总黄酮、总皂苷、总生物碱、碳水化合物和蛋白质），然后分别采用自动氨基酸分析仪和电感耦合等离子体质谱法（ICP-MS）测定游离氨基酸和微量元素。然后，利用超高效液相色谱-四极杆飞行时间串联质谱（UHPLC-QTOF-MS/MS）对GQD的小分子成分进行表征。在多反应监测（MRM）模式下，建立了超高效液相色谱-三重四极杆线性离子阱串联质谱（UHPLC-QTRAP-MS/MS）同时测定26种代表性化合物的定量方法。结果：首次准确测定了药材中主要成分的含量和比例。从GQD中共鉴定了230个化合物，其中4个未描述的生物碱是基于异喹啉生物碱的质谱碎片化途径通过串联质谱数据分析初步鉴定的。建立了测定26个代表性化合物的定量方法，并通过线性、精密度、重复性、稳定性和回收率进行了验证。26种化合物总含量占总含量的27.81%，其中黄芩苷含量最高，达107.26±1 mg/g。结论：本研究实现了GQD成分的全面表征，为阐明GQD的有效成分、作用机制和质量控制奠定了基础，为其他中药方剂的研究提供了有价值的分析框架。

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来源期刊

Phytochemical Analysis 生物-分析化学

CiteScore

6.00

自引率

6.10%

发文量

审稿时长

1.7 months

期刊介绍： Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.