• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

药物性肝损伤数据库综述。

A review of drug-induced liver injury databases.

机构信息

School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, People's Republic of China.

Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR, People's Republic of China.

出版信息

Arch Toxicol. 2017 Sep;91(9):3039-3049. doi: 10.1007/s00204-017-2024-8. Epub 2017 Jul 17.

DOI:10.1007/s00204-017-2024-8
PMID:28717830
Abstract

Drug-induced liver injuries have been a major focus of current research in drug development, and are also one of the major reasons for the failure and withdrawal of drugs in development. Drug-induced liver injuries have been systematically recorded in many public databases, which have become valuable resources in this field. In this study, we provide an overview of these databases, including the liver injury-specific databases LiverTox, LTKB, Open TG-GATEs, LTMap and Hepatox, and the general databases, T3DB, DrugBank, DITOP, DART, CTD and HSDB. The features and limitations of these databases are summarized and discussed in detail. Apart from their powerful functions, we believe that these databases can be improved in several ways: by providing the data about the molecular targets involved in liver toxicity, by incorporating information regarding liver injuries caused by drug interactions, and by regularly updating the data.

摘要

药物性肝损伤一直是药物开发领域当前研究的重点,也是药物开发过程中失败和撤市的主要原因之一。许多公共数据库中系统地记录了药物性肝损伤,这些数据库已成为该领域的宝贵资源。在本研究中,我们概述了这些数据库,包括肝损伤特异性数据库 LiverTox、LTKB、Open TG-GATEs、LTMap 和 Hepatox,以及一般数据库 T3DB、DrugBank、DITOP、DART、CTD 和 HSDB。详细总结和讨论了这些数据库的特点和局限性。除了功能强大之外,我们认为还可以通过以下几种方式改进这些数据库:提供涉及肝毒性的分子靶点的数据,纳入药物相互作用引起的肝损伤信息,并定期更新数据。

相似文献

1
A review of drug-induced liver injury databases.药物性肝损伤数据库综述。
Arch Toxicol. 2017 Sep;91(9):3039-3049. doi: 10.1007/s00204-017-2024-8. Epub 2017 Jul 17.
2
Hepatotoxicity by Drugs: The Most Common Implicated Agents.药物性肝损伤:最常见的致病药物。
Int J Mol Sci. 2016 Feb 6;17(2):224. doi: 10.3390/ijms17020224.
3
LTMap: a web server for assessing the potential liver toxicity by genome-wide transcriptional expression data.LTMap:一个通过全基因组转录表达数据评估潜在肝脏毒性的网络服务器。
J Appl Toxicol. 2014 Jul;34(7):805-9. doi: 10.1002/jat.2923. Epub 2013 Sep 11.
4
The liver toxicity knowledge base: a systems approach to a complex end point.肝脏毒性知识库:一种系统方法应对复杂终点
Clin Pharmacol Ther. 2013 May;93(5):409-12. doi: 10.1038/clpt.2013.16. Epub 2013 Jan 25.
5
Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study.挖掘用于药物安全性评估的隐藏知识:以LiverTox主题建模为例
BMC Bioinformatics. 2014;15 Suppl 17(Suppl 17):S6. doi: 10.1186/1471-2105-15-S17-S6. Epub 2014 Dec 16.
6
Comparative gene and protein expression analyses of a panel of cytokines in acute and chronic drug-induced liver injury in rats.一组细胞因子在大鼠急、慢性药物性肝损伤中的基因和蛋白表达的比较分析。
Toxicology. 2014 Oct 3;324:43-54. doi: 10.1016/j.tox.2014.07.005. Epub 2014 Jul 19.
7
[Drug-induced hepatic diseases].[药物性肝病]
Pathol Biol (Paris). 1999 Nov;47(9):928-37.
8
How to Diagnose and Exclude Drug-Induced Liver Injury.如何诊断和排除药物性肝损伤
Dig Dis. 2015;33(4):472-6. doi: 10.1159/000374091. Epub 2015 Jul 6.
9
[Drug-induced liver injury; fourteenth updated edition of the bibliographic database of liver injuries and related drugs].[药物性肝损伤;肝损伤及相关药物书目数据库第十四版]
Gastroenterol Clin Biol. 2004 Aug-Sep;28(8-9):720-59. doi: 10.1016/s0399-8320(04)95062-2.
10
Drug-induced liver injury: an overview over the most critical compounds.药物性肝损伤:对最关键化合物的概述。
Arch Toxicol. 2015 Mar;89(3):327-34. doi: 10.1007/s00204-015-1456-2. Epub 2015 Jan 25.

引用本文的文献

1
Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity.间隙-Δ能量,一种键能状态的新指标,有助于预测分子毒性。
ACS Omega. 2024 Apr 12;9(16):17839-17847. doi: 10.1021/acsomega.3c07682. eCollection 2024 Apr 23.
2
Real-World Evidence for COVID-19 Delta Variant's Effects on the Digestive System and Protection of Inactivated Vaccines from a Medical Center in Yangzhou, China: A Retrospective Observational Study.中国扬州一家医疗中心的真实世界证据表明,新冠病毒 Delta 变异株对消化系统的影响,以及灭活疫苗的保护作用:一项回顾性观察研究。
Int J Clin Pract. 2022 Aug 19;2022:7405448. doi: 10.1155/2022/7405448. eCollection 2022.
3
The development and application of models for drug induced liver injury.
药物性肝损伤模型的开发与应用。
RSC Adv. 2018 Feb 20;8(15):8101-8111. doi: 10.1039/c7ra12957b. eCollection 2018 Feb 19.
4
Preliminary Study on Hepatoprotective Effect and Mechanism of (-)-Epigallocatechin-3-gallate against Acetaminophen-induced Liver Injury in Rats.(-)-表没食子儿茶素-3-没食子酸酯对乙酰氨基酚诱导的大鼠肝损伤的保肝作用及其机制的初步研究
Iran J Pharm Res. 2021 Summer;20(3):46-56. doi: 10.22037/ijpr.2020.112727.13918.
5
The LiverTox Paradox-Gaps between Promised Data and Reality Check.肝毒性悖论——承诺的数据与实际情况之间的差距
Diagnostics (Basel). 2021 Sep 24;11(10):1754. doi: 10.3390/diagnostics11101754.
6
The Beneficial Roles of SIRT1 in Drug-Induced Liver Injury.SIRT1 在药物性肝损伤中的有益作用。
Oxid Med Cell Longev. 2019 Jul 1;2019:8506195. doi: 10.1155/2019/8506195. eCollection 2019.
7
Toxicology Data Resources to Support Read-Across and (Q)SAR.支持类推和(定量)构效关系的毒理学数据资源。
Front Pharmacol. 2019 Jun 11;10:561. doi: 10.3389/fphar.2019.00561. eCollection 2019.
8
Carvedilol-Induced Liver Injury, a Rare Cause of Mixed Hepatitis: A Clinical Case.卡维地洛所致肝损伤——混合性肝炎的罕见病因:1例临床病例
GE Port J Gastroenterol. 2019 May;26(3):196-201. doi: 10.1159/000490205. Epub 2018 Jul 10.
9
Induction of endoplasmic reticulum stress by aminosteroid derivative RM-581 leads to tumor regression in PANC-1 xenograft model.氨基甾体衍生物 RM-581 通过诱导内质网应激导致 PANC-1 移植瘤模型中的肿瘤消退。
Invest New Drugs. 2019 Jun;37(3):431-440. doi: 10.1007/s10637-018-0643-4. Epub 2018 Jul 30.
10
Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data.毒理基因组学的发展:从基因表达数据理解和预测化合物诱导的毒性。
Mol Omics. 2018 Aug 6;14(4):218-236. doi: 10.1039/c8mo00042e.