天然产物药物发现:二氧化硅还是硅基?

Q1 Pharmacology, Toxicology and Pharmaceutics Handbook of experimental pharmacology Pub Date : 2023-01-01 DOI:10.1007/164_2022_611
Hye Kyong Kim, Young Hae Choi, Robert Verpoorte
{"title":"天然产物药物发现:二氧化硅还是硅基?","authors":"Hye Kyong Kim,&nbsp;Young Hae Choi,&nbsp;Robert Verpoorte","doi":"10.1007/164_2022_611","DOIUrl":null,"url":null,"abstract":"<p><p>Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.</p>","PeriodicalId":12859,"journal":{"name":"Handbook of experimental pharmacology","volume":"277 ","pages":"117-141"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Natural Products Drug Discovery: On Silica or In-Silico?\",\"authors\":\"Hye Kyong Kim,&nbsp;Young Hae Choi,&nbsp;Robert Verpoorte\",\"doi\":\"10.1007/164_2022_611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.</p>\",\"PeriodicalId\":12859,\"journal\":{\"name\":\"Handbook of experimental pharmacology\",\"volume\":\"277 \",\"pages\":\"117-141\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Handbook of experimental pharmacology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/164_2022_611\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Pharmacology, Toxicology and Pharmaceutics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Handbook of experimental pharmacology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/164_2022_611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
引用次数: 1

摘要

在整个人类历史上,天然产物一直是药物开发的最重要来源。随着时间的推移,药物的配方已经从原始药物发展到精炼化学品。在现代药物发现中,传统的天然产物寻铅通常采用自上而下的方法,即生物引导分馏。在这种方法中,用色谱法分离粗提取物,并对所得馏分进行活性测试。随后,活性组分进一步精制,直至获得单一活性化合物。然而,这是一个极其缓慢和昂贵的过程。在为改善这种情况而开发的替代方案中,代谢组学已被证明产生了有趣的结果,并在过去二十年中成功地应用于药物发现。基于代谢组学的铅发现方法包括两个步骤:(1)对目标样品(如植物提取物)进行深入的化学分析和生物活性评估;(2)通过化学计量学将化学和生物学数据进行关联。在该方法的第一步中,目标样品以非靶向方式进行化学分析,以检测尽可能多的化合物。到目前为止,核磁共振光谱,LC-MS, GC-MS和MS/MS谱是最常见的分析工具。利用多元数据分析等多种化学计量学方法,将剖面数据与生物活性进行关联。这种硅分析有很大的潜力取代或补充传统的硅上生物测定指导分离,因为它将大大减少生物测定的数量,从而减少时间和成本。此外,当协同机制存在时,它可能会揭示出经典的自上而下方法显然不适合的协同机制。本章旨在概述基于化学分析与化学计量学在天然产物药物发现中的应用的成功方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Natural Products Drug Discovery: On Silica or In-Silico?

Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Handbook of experimental pharmacology
Handbook of experimental pharmacology Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)
CiteScore
5.20
自引率
0.00%
发文量
54
期刊介绍: The Handbook of Experimental Pharmacology is one of the most authoritative and influential book series in pharmacology. It provides critical and comprehensive discussions of the most significant areas of pharmacological research, written by leading international authorities. Each volume in the series represents the most informative and contemporary account of its subject available, making it an unrivalled reference source.
期刊最新文献
What Is QSP and Why Does It Exist?: A Brief History. Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction. Application of Quantitative Systems Pharmacology Approaches to Support Pediatric Labeling in Rare Diseases. The Use of Natural Products for Preventing Cognitive Decline/Providing Neuroprotection. Natural Products to Promote Vascular Health.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1