Increasing the acceptance of in silico toxicology through development of protocols and position papers

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-02-01 DOI:10.1016/j.comtox.2021.100209
Glenn J. Myatt , Arianna Bassan , Dave Bower , Kevin M. Crofton , Kevin P. Cross , Jessica C. Graham , Catrin Hasselgren , Robert A. Jolly , Scott Miller , Manuela Pavan , Raymond R Tice , Craig Zwickl , Candice Johnson
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引用次数: 8

Abstract

In silico toxicology protocols are currently needed to support the acceptance and deployment of computational toxicology methods as alternative methods for health hazard identification. Such protocols combine relevant in silico results with available experimental data to derive an assessment of major toxicological endpoints supported by a confidence score reflecting the uncertainty in the assessment. The protocols also identify relevant effects and/or mechanisms which can be used to guide the assessment of a toxicological endpoint. In addition, sufficient documentation of procedures and methods used to support an assessment is essential for both internal and external decision-making. The combination of relevant data, confidence scoring, and reporting provides a hazard assessment framework intended to increase the acceptance of in silico results in a toxicologic assessment. This article describes key principles and components of such protocols, including the hazard assessment framework and recommendations demonstrating how evaluating relevance, completeness, and confidence can be performed and documented. Also discussed are criteria used to develop an in silico protocol based on the state of the science and the importance of developing position papers to outline roadmaps for future in silico protocols used to guide assessments of more complex toxicological endpoints, such as cancer or neurotoxicity. The current status of providing such protocols is summarized for specific in silico protocols that are already published, in development, or planned.

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通过制定方案和立场文件,提高对计算机毒理学的接受度
目前需要计算机毒理学协议,以支持接受和部署计算毒理学方法,作为确定健康危害的替代方法。这些方案将相关的计算机结果与现有的实验数据结合起来,得出主要毒理学终点的评估,并得到反映评估不确定性的置信度评分的支持。该方案还确定了可用于指导毒理学终点评估的相关影响和/或机制。此外,用于支持评估的程序和方法的充分文件对于内部和外部决策都是必不可少的。相关数据、置信度评分和报告的结合提供了一个危害评估框架,旨在提高计算机结果在毒理学评估中的接受度。本文描述了这些协议的关键原则和组成部分,包括危害评估框架和建议,展示了如何执行和记录评估相关性、完整性和置信度。还讨论了基于科学状况制定计算机程序的标准,以及制定立场文件概述未来用于指导更复杂毒理学终点(如癌症或神经毒性)评估的计算机程序路线图的重要性。对于已经发布、正在开发或计划中的特定的计算机协议,总结了提供此类协议的当前状态。
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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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