A strategy to define applicability domains for read-across

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-05-01 DOI:10.1016/j.comtox.2022.100220
Cynthia Pestana , Steven J. Enoch , James W. Firman , Judith C. Madden , Nicoleta Spînu , Mark T.D. Cronin
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引用次数: 1

Abstract

The definition, characterisation and assessment of the similarity between target and source molecules are cornerstones of the acceptance of a read-across prediction to fill a data gap for a toxicological endpoint. There is much guidance and many frameworks which are applicable in a regulatory context, but as yet no formalised process exists by which to determine whether or not the properties of an analogue (or chemicals within a category) fall within an appropriate domain from which a reliable read-across prediction can be made. This investigation has synthesised much of the existing knowledge in this area into a practical strategy to enable the domain of a read-across prediction to be defined, in terms of chemistry (structure and properties), toxicodynamics and toxicokinetics. The strategy is robust, comprehensive, flexible, and can be implemented readily. It enables the relative similarity and dissimilarity, between target and source molecules, for both the analogue and category approaches, to be analysed and provides a basis for alternative scenarios such as read-across based on formation of a common metabolite or biological profile to be defiend. Herein, the read-across domains for the repeated dose toxicity of a group of triazoles and imidazoles have been evaluated. The most challenging aspect to this approach will continue to be determining what is an “acceptable” degree of similarity when performing read-across for a specific purpose.

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定义跨读适用域的策略
靶分子和源分子之间相似性的定义、表征和评估是接受跨读预测以填补毒理学终点数据空白的基石。有许多指导和许多框架适用于监管环境,但目前还没有正式的过程来确定类似物(或类别内的化学品)的性质是否属于可以进行可靠读取预测的适当领域。这项研究综合了该领域的许多现有知识,形成了一种实用的策略,可以从化学(结构和性质)、毒性动力学和毒性动力学的角度来定义跨读预测领域。该战略稳健、全面、灵活,易于实施。它可以分析靶分子和源分子之间的相对相似性和不相似性,用于模拟和分类方法,并为替代方案提供基础,例如基于共同代谢物或生物概况的形成进行解读。本文对一组三唑和咪唑的重复剂量毒性进行了跨域读取评价。这种方法最具挑战性的方面仍然是,在为特定目的执行读取时,确定什么是“可接受的”相似性程度。
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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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