Evaluating confidence in toxicity assessments based on experimental data and in silico predictions

IF 3.1 Q2 TOXICOLOGY
Candice Johnson , Lennart T. Anger , Romualdo Benigni , David Bower , Frank Bringezu , Kevin M. Crofton , Mark T.D. Cronin , Kevin P. Cross , Magdalena Dettwiler , Markus Frericks , Fjodor Melnikov , Scott Miller , David W. Roberts , Diana Suarez-Rodrigez , Alessandra Roncaglioni , Elena Lo Piparo , Raymond R. Tice , Craig Zwickl , Glenn J. Myatt
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引用次数: 8

Abstract

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within integrated approaches to testing and assessment (IATA).

Abstract Image

评估基于实验数据和计算机预测的毒性评估的可信度
了解毒理学评估的可靠性和相关性对于衡量总体置信度和传达与之相关的不确定性程度非常重要。评估可靠性和相关性的过程对实验数据有很好的定义。需要为计算机预测建立类似的标准,因为它们在填补数据空白方面变得越来越重要,需要合理地整合为额外的证据线。因此,计算机评估可以更有信心和更协调地进行交流。目前的工作扩展了以前的可靠性、相关性和置信度的定义,并建立了一个概念框架,将这些定义应用于计算机数据。该方法用于两个案例研究:1)邻苯二甲酸酐,其中实验数据很容易获得;2)4-羟基-3-丙氧基苯甲醛,一个数据贫乏的案例,主要依赖于硅方法,表明硅评估的可靠性、相关性和置信度可以在测试和评估的综合方法中有效沟通(IATA)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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