支持在监管决策中应用经合组织 (OECD) 关于 (Q)SAR 模型验证和预测评估的指导文件的框架

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2024-03-16 DOI:10.1016/j.comtox.2024.100305
Christopher Barber, Crina Heghes, Laura Johnston
{"title":"支持在监管决策中应用经合组织 (OECD) 关于 (Q)SAR 模型验证和预测评估的指导文件的框架","authors":"Christopher Barber,&nbsp;Crina Heghes,&nbsp;Laura Johnston","doi":"10.1016/j.comtox.2024.100305","DOIUrl":null,"url":null,"abstract":"<div><p>Advances in the development and application of in silico models in toxicology has been recognised by two OECD guidance documents (69: Guidance Document On The Validation Of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models and 386: (Q)SAR Assessment Framework: Guidance for the regulatory assessment of (Q)SAR models, predictions, and results based on multiple predictions) published in 2007 and 2023 respectively. The former outlines criteria for appropriate model validation, whilst the latter provides guidance around assessing predictions derived from them. The concepts and criteria described within these guidelines have been used to establish a framework to support both model builders and those applying them to support regulatory decisions. Herein we demonstrate how to meet those criteria and propose where further guidance is essential for ensuring the consistent, confident, and safe application of in silico models in support of regulatory decisions.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"30 ","pages":"Article 100305"},"PeriodicalIF":3.1000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A framework to support the application of the OECD guidance documents on (Q)SAR model validation and prediction assessment for regulatory decisions\",\"authors\":\"Christopher Barber,&nbsp;Crina Heghes,&nbsp;Laura Johnston\",\"doi\":\"10.1016/j.comtox.2024.100305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Advances in the development and application of in silico models in toxicology has been recognised by two OECD guidance documents (69: Guidance Document On The Validation Of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models and 386: (Q)SAR Assessment Framework: Guidance for the regulatory assessment of (Q)SAR models, predictions, and results based on multiple predictions) published in 2007 and 2023 respectively. The former outlines criteria for appropriate model validation, whilst the latter provides guidance around assessing predictions derived from them. The concepts and criteria described within these guidelines have been used to establish a framework to support both model builders and those applying them to support regulatory decisions. Herein we demonstrate how to meet those criteria and propose where further guidance is essential for ensuring the consistent, confident, and safe application of in silico models in support of regulatory decisions.</p></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":\"30 \",\"pages\":\"Article 100305\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468111324000070\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TOXICOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111324000070","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

经合组织(OECD)的两份指导文件(69:定量)结构-活性关系[(Q)SAR]模型验证指导文件》和《386:(Q)SAR 评估框架:分别于 2007 年和 2023 年发布。前者概述了适当的模型验证标准,后者则为评估由模型得出的预测结果提供了指导。这些指南中描述的概念和标准已被用于建立一个框架,为模型构建者和应用模型支持监管决策的人员提供支持。在此,我们将展示如何达到这些标准,并提出进一步的指导对于确保一致、自信和安全地应用硅学模型支持监管决策至关重要。
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
A framework to support the application of the OECD guidance documents on (Q)SAR model validation and prediction assessment for regulatory decisions

Advances in the development and application of in silico models in toxicology has been recognised by two OECD guidance documents (69: Guidance Document On The Validation Of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models and 386: (Q)SAR Assessment Framework: Guidance for the regulatory assessment of (Q)SAR models, predictions, and results based on multiple predictions) published in 2007 and 2023 respectively. The former outlines criteria for appropriate model validation, whilst the latter provides guidance around assessing predictions derived from them. The concepts and criteria described within these guidelines have been used to establish a framework to support both model builders and those applying them to support regulatory decisions. Herein we demonstrate how to meet those criteria and propose where further guidance is essential for ensuring the consistent, confident, and safe application of in silico models in support of regulatory decisions.

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
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
期刊最新文献
Reconstruction of exposure to volatile organic compounds from venous blood concentration and an uncertain physiologically-based pharmacokinetic model Developing quantitative Adverse Outcome Pathways: An ordinary differential equation-based computational framework Quantitative prediction of hemolytic activity of peptides Species specific kinetics of imidacloprid and carbendazim in mouse and rat and consequences for biomonitoring In silico analysis of the melamine structural analogues interaction with calcium-sensing receptor: A potential for nephrotoxicity
×
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