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
{"title":"Towards a qAOP framework for predictive toxicology - Linking data to decisions","authors":"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","doi":"10.1016/j.comtox.2021.100195","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>in silico</em>, <em>in vitro</em> and <em>in vivo</em> 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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100195"},"PeriodicalIF":3.1000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850654/pdf/","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111321000414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 12
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 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.
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