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迈向用于预测毒理学的定量构效关系(qAOP)框架——将数据与决策联系起来。

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

作者信息

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

机构信息

European Commission, Joint Research Centre (JRC), Ispra, Italy.

Liverpool John Moores University, Liverpool, United Kingdom.

出版信息

Comput Toxicol. 2022 Feb;21:100195. doi: 10.1016/j.comtox.2021.100195.

Abstract

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 , and 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.

摘要

不良结局途径(AOP)是一种概念性构建,有助于组织和解释代表多个生物学水平且源自一系列方法学途径(包括[具体方法1]、[具体方法2]和[具体方法3]分析)的机制数据。AOP在化学安全评估范式中发挥着越来越重要的作用,对AOP进行量化是朝着更可靠地预测化学诱导的不良反应迈出的重要一步。建模方法需要识别、提取和使用可靠的数据与信息,以支持在AOP开发中纳入定量考量。有大量且不断增加的数字资源可用于支持定量AOP的建模,这些资源提供了广泛的信息,但在实际应用中也需要指导。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。所提出的框架为该领域的监管者和科学家提供了一种统一的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37e3/8850654/2b5d4c9c66c5/gr1.jpg

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