Discovery of quality markers of Phyllanthus emblica by integrating chromatographic fingerprint, serum pharmacochemistry and network pharmacology

化学 余甘子 指纹(计算) 色谱法 传统医学 药理学 人工智能 医学 计算机科学
作者
Yihan Xu,Juan Chen
出处
期刊:Journal of Pharmaceutical and Biomedical Analysis [Elsevier]
卷期号:249: 116346-116346 被引量:2
标识
DOI:10.1016/j.jpba.2024.116346
摘要

Phyllanthus emblica (P. emblica) is a vital medicinal plant with both medical and edible values. In the quality standard of P. emblica listed by the Chinese Pharmacopoeia, gallic acid is used as the index component for the content determination. However, a large number of tannin components can be decomposed into gallic acid during its refluxing extraction process, thus affecting the accuracy and specificity of the content determination. Thus, the index component used for the quality control needs to be further determined. In this study, the quality markers of P. emblica was specified by integrating chromatographic fingerprint, serum pharmacochemistry and network pharmacology. The chromatographic fingerprint of 18 batches of P. emblica samples were established by ultra-high-performance liquid chromatography (UPLC), and 8 differential components causing quality fluctuation were identified by chemometric analysis and UPLC-Q-TOF/MS analysis. Afterwards, 14 prototype migration components absorbed into the blood after gavage administration to rats were identified by UPLC-Q-TOF/MS analysis. Subsequently, a network pharmacology approach was used to construct the component-target-disease-pathway network, resulting in the identification of 22 components responsible for efficacy of P. emblica. Finally, by integrating the above results, ellagic acid was screened out as one of the Q-markers and could be employed as a quantitative component of P. emblica to improve the quality standard. The strategy is also informative for discovering Q-markers of other TCMs.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
liao应助zwc采纳,获得10
1秒前
汉堡包应助无昵称采纳,获得10
1秒前
1秒前
sqcpk完成签到,获得积分10
1秒前
量子星尘发布了新的文献求助10
1秒前
小菜一碟完成签到,获得积分10
1秒前
ori完成签到,获得积分10
2秒前
SibetHu发布了新的文献求助10
3秒前
CodeCraft应助小华采纳,获得10
3秒前
3秒前
3秒前
bkagyin应助豆儿嘚小豆儿采纳,获得10
3秒前
典雅夏之完成签到,获得积分10
3秒前
hy发布了新的文献求助10
3秒前
3秒前
bkagyin应助啧啧啧采纳,获得10
4秒前
4秒前
曾经富发布了新的文献求助10
4秒前
4秒前
听雨应助桃子e采纳,获得10
4秒前
潇洒紫真发布了新的文献求助10
5秒前
科研通AI2S应助Catherine采纳,获得10
5秒前
sss发布了新的文献求助10
5秒前
大萌完成签到,获得积分10
5秒前
bkagyin应助QQQ采纳,获得10
5秒前
5秒前
5秒前
6秒前
逍遥猪皮完成签到,获得积分10
6秒前
布丁大师完成签到,获得积分10
6秒前
qwq完成签到,获得积分10
6秒前
可乐加冰发布了新的文献求助10
6秒前
6秒前
6秒前
6秒前
典雅夏之发布了新的文献求助10
7秒前
积极的含芙完成签到,获得积分20
7秒前
7秒前
科研小小白完成签到,获得积分10
8秒前
淡淡友瑶发布了新的文献求助10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Exploring Nostalgia 500
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
Advanced Memory Technology: Functional Materials and Devices 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5667927
求助须知:如何正确求助?哪些是违规求助? 4888141
关于积分的说明 15122164
捐赠科研通 4826686
什么是DOI,文献DOI怎么找? 2584281
邀请新用户注册赠送积分活动 1538179
关于科研通互助平台的介绍 1496440