A framework to support the application of the OECD guidance documents on (Q)SAR model validation and prediction assessment for regulatory decisions

IF 3.1 Q2 TOXICOLOGY
Christopher Barber, Crina Heghes, Laura Johnston
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引用次数: 0

Abstract

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.

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
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。
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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
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