面向预测毒理学的qAOP框架——将数据与决策联系起来

IF 3.1 Q2 TOXICOLOGY
Alicia Paini , Ivana Campia , Mark T.D. Cronin , David Asturiol , Lidia Ceriani , Thomas E. Exner , Wang Gao , Caroline Gomes , Johannes Kruisselbrink , Marvin Martens , M.E. Bette Meek , David Pamies , Julia Pletz , Stefan Scholz , Andreas Schüttler , Nicoleta Spînu , Daniel L. Villeneuve , Clemens Wittwehr , Andrew Worth , Mirjam Luijten
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引用次数: 12

摘要

不良结果通路(AOP)是一个概念结构,有助于组织和解释代表多个生物水平的机制数据,并衍生于一系列方法学方法,包括硅,体外和体内分析。AOPs在化学品安全评价范式中发挥着越来越重要的作用,AOPs的量化是更可靠地预测化学诱导不良反应的重要一步。建模方法需要识别、提取和使用可靠的数据和信息,以支持在AOP开发中包含定量考虑。广泛和不断增长的数字资源可用于支持定量aop的建模,提供了广泛的信息,但也需要对其实际应用进行指导。基于一组专家的反馈和三个qAOP案例研究,提出了一个qAOP开发框架。拟议的框架为在这一领域工作的监管机构和科学家提供了一种协调一致的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Towards a qAOP framework for predictive toxicology - Linking data to decisions

Towards a qAOP framework for predictive toxicology - Linking data to decisions

Towards a qAOP framework for predictive toxicology - Linking data to decisions

Towards a qAOP framework for predictive toxicology - Linking data to decisions

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

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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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