基于网络药理学和化学计量学对中成药质量控制方法研究: 以活络散为例

doi:10.5246/jcps.2024.09.061

中国药学(英文版) ›› 2024, Vol. 33 ›› Issue (9): 819-836.DOI: 10.5246/jcps.2024.09.061

基于网络药理学和化学计量学对中成药质量控制方法研究: 以活络散为例

朱美玲¹, 张基荣¹, 张秋荣², 林羽¹, 李小艳¹^,^*(), 许文¹^,³^,^*(), 徐伟¹

^1. 福建中医药大学药学院, 福建福州 350122
^2. 中国科学院分子细胞科学卓越创新中心, 上海 200031
^3. 福建中医药大学科技创新与转化中心, 福建福州 350122

收稿日期:2024-02-16 修回日期:2024-03-23 接受日期:2024-04-24 出版日期:2024-10-03 发布日期:2024-10-03
通讯作者: 李小艳, 许文

Integrated approach of network pharmacology and chemometrics for quality control of Chinese patent medicine: A case study on Huo-Luo-San

Meiling Zhu¹, Jirong Zhang¹, Qiurong Zhang², Yu Lin¹, Xiaoyan Li¹^,^*(), Wen Xu¹^,³^,^*(), Wei Xu¹

¹ College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China
² Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
³ Innovation and Transformation Center of Science and Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China

Received:2024-02-16 Revised:2024-03-23 Accepted:2024-04-24 Online:2024-10-03 Published:2024-10-03
Contact: Xiaoyan Li, Wen Xu
Supported by:
The Subject of Fujian Province Science and Technology Hall of China (Grant No. 2022J01867); National Key R&D Program of China (Grant No. 2019YFC1710505) and School Management Project of Fujian University of Traditional Chinese Medicine University (Grant No. X2021001 and No. XJC202301).

摘要/Abstract

摘要：

本文以活络散为例构建了一种网络药理学和化学计量学相结合的方法以找寻中成药的化学质量标志物。首先我们利用网络药理学对活络散复方中各药材的主要化学成分的作用靶点进行筛选, 结合《中国药典》下各药材指标性成分筛选出13个成分(绿原酸、香蒲新苷、异鼠李素-3-O-新橙皮苷、木犀草苷、三七皂苷R₁、人参皂苷Rg₁、黄芩苷、盐酸小檗碱、人参皂苷Rb₁、去氢木香内酯、薯蓣皂苷、欧前胡素、木香烃内酯), 建立活络散的“草药-化合物-靶点”, 其次基于网络药理学, 采用超高效液相色谱串联三重四极杆质谱法(UPLC-MS/MS)同时测定活络散复方中的13个成分的含量, Waters Cortecs C₁₈ (2.1 mm × 100 mm, 1.6 μm), 以乙腈(A)和0.1%甲酸水(B)为流动相, 梯度洗脱。最后将含量测定结果进行化学计量学分析, 包括聚类分析、主成分分析、正交偏最小二乘投影判别分析和箱型图分析来筛选质量标志物。13个成分在考察的浓度范围内呈良好的线性关系。经OPLS-DA和箱型图分析, 三七皂苷R₁、黄芩苷、薯蓣皂苷、绿原酸、欧前胡素、盐酸小檗碱是影响活络散复方的主要化学质量标志物。本研究构建的方法快速、灵敏、高效, 可为活络散的质量控制提供一种新的技术手段。

关键词: 中药, 质量标准, 活络散, 网络药理学, 化学计量学

Abstract:

Traditional Chinese medicine (TCM) has garnered significant global interest owing to its multi-component and multi-target theoretical framework and extensive therapeutic efficacy. However, the identification of quality markers (Q-markers) remains a formidable challenge in TCM. Hence, this study aimed to integrate network pharmacology and chemometrics to identify Q-markers in Chinese patent medicine, with a focus on Huo-Luo-San (HLS) as a case study. HLS, a widely used powdered Chinese patent medicine in China, comprises a complex formula of 10 herbs, initially formulated during the Qing dynasty for treating fractures. Initially, 13 components, chlorogenic acid, typhaneoside, isorhamnetin-3-O-neohesperidoside, cynaroside, notoginsenoside R₁, ginsenoside Rg₁, baicalin, berberine hydrochloride, ginsenoside Rb₁, dehydrocostus lactone, dioscin, imperatorin, and costunolide, were selected as phytochemical markers for each herb based on the Chinese Pharmacopoeia (2020 version), forming the “Herbs-Compounds-targets” network of HLS using network pharmacology. Subsequently, employing network pharmacology, the 13 HLS components were quantified using UPLC-QqQ-MS. Chromatographic conditions were optimized on a Waters Cortecs C₁₈ column (2.1 mm × 100 mm, 1.6 μm) with a gradient elution comprising 0.1% formic acid in water and acetonitrile. Analyte detection was performed in the multiple-reaction monitoring mode, and the method underwent validation for linearity, detection limit, precision, repeatability, stability, and accuracy. The validated method was then utilized to analyze the 13 components in 15 batches of HLS samples. Chemometric techniques, including hierarchical cluster analysis, principal component analysis, orthogonal partial least squares projection discriminant analysis, and box map analyses, were subsequently employed to identify the Q-markers. Ultimately, six components, baicalin, notoginsenoside R₁, berberine hydrochloride, dioscin, imperatorin, and chlorogenic acid, were selected as Q-markers for HLS. The integration of network pharmacology with chemometrics represented a novel approach for selecting Q-markers in Chinese patent medicine.

Key words: Traditional Chinese medicine, Quality markers, Huo-Luo-San, Network pharmacology, Chemometrics

Supporting: /attached/file/20241006/20241006175340_895.pdf

朱美玲, 张基荣, 张秋荣, 林羽, 李小艳, 许文, 徐伟. 基于网络药理学和化学计量学对中成药质量控制方法研究: 以活络散为例[J]. 中国药学(英文版), 2024, 33(9): 819-836.

Meiling Zhu, Jirong Zhang, Qiurong Zhang, Yu Lin, Xiaoyan Li, Wen Xu, Wei Xu. Integrated approach of network pharmacology and chemometrics for quality control of Chinese patent medicine: A case study on Huo-Luo-San[J]. Journal of Chinese Pharmaceutical Sciences, 2024, 33(9): 819-836.

图/表 17

Figure 1. Method for screening Q-marker in complex systems of TCM.

Figure 2. Chemical structures of 13 analytes and two internal standards. (1) CA, (2) T, (3) IN, (4) CS, (5) R1, (6) RG1, (7) B, (8) BH, (9) RB1, (10) DL, (11) D, (12) I, (13) CL, IS1, IS2.

Figure 3. Venn diagram and PPI network for the common targets of HLS and traumatic injury.

Figure 4. Network of “Herbs-compounds-targets”. The orange arrow represents HLS, the purple square represents herbs, the blue diamond represents components, and the pink round represents the target.

Figure 5. R1 is used as an example to depict the detailed identification processes of saponin.

Figure 6. B is used as an example to depict the detailed identification processes of flavone.

Table 1. Precursor/product ions and parameters for the MRM of 13 components.

Figure 7. MRM ion channel chromatogram of blank solution (A), standards (B) and HLS sample (C).

Figure 8. ESI– (A), ESI+ (B) ion chromatograms of the substances and ESI– (C), ESI+ (D) ion chromatogram of the sample.

Table 2. Regression equations, correlation coefficients, and linearity ranges of 13 components in HLS.

Table 3. Precision, repeatability, and stability of 13 components in HLS.

Table 4. Results of recovery tests (n = 6).

Table 5. The content of HLS in the 15 batches (mg/g, n = 3).

Figure 9. HCA for the 15 batches of HLS.

Figure 10. Scores plots of PCA of samples in 15 batches of HLS.

Figure 11. Plot of OPLS-DA and VIP plots of samples in 15 batches of HLS.

Figure 12. Results of the box plots of 13 components in HLS. Note: *P < 0.01; **P < 0.001.

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基于网络药理学和化学计量学对中成药质量控制方法研究: 以活络散为例

Integrated approach of network pharmacology and chemometrics for quality control of Chinese patent medicine: A case study on Huo-Luo-San

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