A tailored database combining reference compound-derived metabolite, metabolism platform and chemical characteristic of Chinese herb followed by activity screening: Application to Magnoliae Officinalis Cortex.

IF 8.9
Journal of pharmaceutical analysis Pub Date : 2025-04-01 Epub Date: 2024-08-08 DOI:10.1016/j.jpha.2024.101066
Zhenzhen Xue, Yudong Shang, Lan Yang, Tao Li, Bin Yang
{"title":"A tailored database combining reference compound-derived metabolite, metabolism platform and chemical characteristic of Chinese herb followed by activity screening: Application to Magnoliae Officinalis Cortex.","authors":"Zhenzhen Xue, Yudong Shang, Lan Yang, Tao Li, Bin Yang","doi":"10.1016/j.jpha.2024.101066","DOIUrl":null,"url":null,"abstract":"<p><p>A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex (MOC) was developed and implemented to rapidly profile and discover bioactive metabolites <i>in vivo</i> derived from traditional Chinese medicine (TCM). The strategy possessed four characteristics: 1) The tailored database consisted of metabolites derived from big data-originated reference compound, metabolites predicted <i>in silico</i>, and MOC chemical profile-based pseudomolecular ions. 2) When profiling MOC-derived metabolites <i>in vivo</i>, attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds, as reported by most papers, but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites. 3) Metabolite traceability was performed, especially to distinguish isomeric prototypes-derived metabolites, prototypes of MOC compounds as well as phase I metabolites derived from other MOC compounds. 4) Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification. Using this strategy, 134 metabolites were swiftly characterized after the oral administration of MOC to rats, and several metabolites were reported for the first time. Furthermore, 17 potential active metabolites were discovered by targeting the motilin, dopamine D2, and the serotonin type 4 (5-HT<sub>4</sub>) receptors, and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model. This study extends the application of mass spectrometry (MS) to rapidly profile TCM-derived metabolites <i>in vivo</i>, which will help pharmacologists rapidly discover potent metabolites from a complex matrix.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 4","pages":"101066"},"PeriodicalIF":8.9000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053571/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of pharmaceutical analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jpha.2024.101066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

A strategy combining a tailored database and high-throughput activity screening that discover bioactive metabolites derived from Magnoliae Officinalis Cortex (MOC) was developed and implemented to rapidly profile and discover bioactive metabolites in vivo derived from traditional Chinese medicine (TCM). The strategy possessed four characteristics: 1) The tailored database consisted of metabolites derived from big data-originated reference compound, metabolites predicted in silico, and MOC chemical profile-based pseudomolecular ions. 2) When profiling MOC-derived metabolites in vivo, attentions were paid not only to prototypes of MOC compounds and metabolites directly derived from MOC compounds, as reported by most papers, but also to isomerized metabolites and the degradation products of MOC compounds as well as their derived metabolites. 3) Metabolite traceability was performed, especially to distinguish isomeric prototypes-derived metabolites, prototypes of MOC compounds as well as phase I metabolites derived from other MOC compounds. 4) Molecular docking was utilized for high-throughput activity screening and molecular dynamic simulation as well as zebrafish model were used for verification. Using this strategy, 134 metabolites were swiftly characterized after the oral administration of MOC to rats, and several metabolites were reported for the first time. Furthermore, 17 potential active metabolites were discovered by targeting the motilin, dopamine D2, and the serotonin type 4 (5-HT4) receptors, and part bioactivities were verified using molecular dynamic simulation and a zebrafish constipation model. This study extends the application of mass spectrometry (MS) to rapidly profile TCM-derived metabolites in vivo, which will help pharmacologists rapidly discover potent metabolites from a complex matrix.

结合参比化合物代谢物、代谢平台和中草药化学特性的定制数据库,进行活性筛选:在厚朴皮上的应用。
为了快速分析和发现来自中药(TCM)的体内生物活性代谢物,开发并实施了一种结合定制数据库和高通量活性筛选的策略,以发现来自厚朴皮质(MOC)的生物活性代谢物。该策略具有四个特点:1)定制的数据库由源自大数据的参考化合物衍生的代谢物、计算机预测的代谢物和基于MOC化学谱的伪分子离子组成。2)在分析MOC衍生的体内代谢物时,不仅关注大多数文献报道的MOC化合物原型和MOC化合物直接衍生的代谢物,还关注MOC化合物的异构化代谢物和降解产物及其衍生代谢物。3)进行代谢物溯源,特别是区分异构体原型衍生代谢物、MOC化合物原型以及其他MOC化合物衍生的I期代谢物。4)利用分子对接进行高通量活性筛选,利用分子动力学模拟和斑马鱼模型进行验证。利用这一策略,在大鼠口服MOC后,134种代谢物被迅速表征,其中一些代谢物是首次报道。此外,通过对胃动素、多巴胺D2和血清素4 (5-HT4)受体的靶向研究,发现了17种潜在的活性代谢物,并通过分子动力学模拟和斑马鱼便秘模型验证了部分生物活性。本研究扩展了质谱(MS)在体内快速分析中药衍生代谢物的应用,这将有助于药理学家从复杂的基质中快速发现有效的代谢物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信