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
{"title":"评估基于实验数据和计算机预测的毒性评估的可信度","authors":"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","doi":"10.1016/j.comtox.2021.100204","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>in silico</em> predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, <em>in silico</em> 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 <em>in silico</em> 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 <em>in silico</em> methods, showing that reliability, relevance, and confidence of <em>in silico</em> assessments can be effectively communicated within integrated approaches to testing and assessment (IATA).</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100204"},"PeriodicalIF":3.1000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Evaluating confidence in toxicity assessments based on experimental data and in silico predictions\",\"authors\":\"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\",\"doi\":\"10.1016/j.comtox.2021.100204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <em>in silico</em> predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, <em>in silico</em> 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 <em>in silico</em> 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 <em>in silico</em> methods, showing that reliability, relevance, and confidence of <em>in silico</em> assessments can be effectively communicated within integrated approaches to testing and assessment (IATA).</p></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":\"21 \",\"pages\":\"Article 100204\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2022-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468111321000505\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TOXICOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111321000505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
Evaluating confidence in toxicity assessments based on experimental data and in silico predictions
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).
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