{"title":"From model performance to decision support – The rise of computational toxicology in chemical safety assessments","authors":"","doi":"10.1016/j.comtox.2024.100303","DOIUrl":null,"url":null,"abstract":"<div><p>In silico systems can reduce the need for (animal) testing, increase human safety and support critical decisions. They are increasingly being cited in regulatory guidance documents and are forming a key element of New Approach Methodologies (NAMs). Performance is being improved through a combination of new methodologies, increased understanding of mechanistic toxicology and access to experimental data from new assays. Trust and acceptance of in silico methodologies requires them to be accurate and transparent while also providing an explanation and confidence-assessment for each prediction. This paper summarises the state-of-art of in silico models and provides an action plan for further advances in this field.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111324000057","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: 0
Abstract
In silico systems can reduce the need for (animal) testing, increase human safety and support critical decisions. They are increasingly being cited in regulatory guidance documents and are forming a key element of New Approach Methodologies (NAMs). Performance is being improved through a combination of new methodologies, increased understanding of mechanistic toxicology and access to experimental data from new assays. Trust and acceptance of in silico methodologies requires them to be accurate and transparent while also providing an explanation and confidence-assessment for each prediction. This paper summarises the state-of-art of in silico models and provides an action plan for further advances in this field.
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