硅方法急性毒性评估的原则和程序。

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-11-01 DOI:10.1016/j.comtox.2022.100237
Craig M. Zwickl , Jessica C. Graham , Robert A. Jolly , Arianna Bassan , Ernst Ahlberg , Alexander Amberg , Lennart T. Anger , Lisa Beilke , Phillip Bellion , Alessandro Brigo , Heather Burleigh-Flayer , Mark T.D. Cronin , Amy A. Devlin , Trevor Fish , Susanne Glowienke , Kamila Gromek , Agnes L. Karmaus , Ray Kemper , Sunil Kulkarni , Elena Lo Piparo , Glenn J. Myatt
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引用次数: 2

摘要

硅模型中的急性毒性正被用于支持越来越多的应用领域,包括(1)产品研发,(2)产品批准和注册,以及(3)化学品的运输、储存和处理。这种模型的采用受到阻碍,部分原因是缺乏描述如何执行和记录计算机分析的指导。为了解决这个问题,提出了一个急性毒性危害评估框架。该框架结合了来自不同来源的结果,包括计算机方法和体外或体内实验。对有助于预测体内结果(即LD50)的计算机内方法进行了分析,得出的结论是,使用计算机内方法获得的预测现在非常适合可靠地支持基于LD50的急性毒性评估,用于GHS分类。概述了体外研究的终点,这些终点通常用于预测急性毒性(例如细胞毒性/细胞致死性以及针对特定机制的测定)。对潜在毒性的途径和关键触发机制的了解增加,以及体外数据的可用性增加,使得从仅基于LD50等终点的评估转变为可以在体外或通过使用计算机预测模型准确评估的基于机制的终点。本文还强调了使用证据权重考虑因素对所有可用信息进行专家审查的重要性,并通过一系列不同的实际用例说明了计算机方法如何支持急性毒性评估。
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Principles and procedures for assessment of acute toxicity incorporating in silico methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

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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
期刊最新文献
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