Craig M. Zwickl , Jessica C. Graham , Robert A. Jolly , Arianna Bassan , Ernst Ahlberg , Alexander Amberg , Lennart T. Anger , Lisa Beilke , Phillip Bellion , Alessandro Brigo , Heather Burleigh-Flayer , Mark T.D. Cronin , Amy A. Devlin , Trevor Fish , Susanne Glowienke , Kamila Gromek , Agnes L. Karmaus , Ray Kemper , Sunil Kulkarni , Elena Lo Piparo , Glenn J. Myatt
{"title":"硅方法急性毒性评估的原则和程序。","authors":"Craig M. Zwickl , Jessica C. Graham , Robert A. Jolly , Arianna Bassan , Ernst Ahlberg , Alexander Amberg , Lennart T. Anger , Lisa Beilke , Phillip Bellion , Alessandro Brigo , Heather Burleigh-Flayer , Mark T.D. Cronin , Amy A. Devlin , Trevor Fish , Susanne Glowienke , Kamila Gromek , Agnes L. Karmaus , Ray Kemper , Sunil Kulkarni , Elena Lo Piparo , Glenn J. Myatt","doi":"10.1016/j.comtox.2022.100237","DOIUrl":null,"url":null,"abstract":"<div><p>Acute <em>toxicity in silico</em> models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an <em>in silico</em> analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including <em>in silico</em> methods and <em>in vitro</em> or <em>in vivo</em> experiments. <em>In silico</em> methods that can assist the prediction of <em>in vivo</em> outcomes (<em>i.e.</em>, LD<sub>50</sub>) are analyzed concluding that predictions obtained using <em>in silico</em> approaches are now well-suited for reliably supporting assessment of LD<sub>50</sub>-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from <em>in vitro</em> studies commonly evaluated for predicting acute toxicity (<em>e.g.</em>, cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of <em>in vitro</em> data allow for a shift away from assessments solely based on endpoints such as LD<sub>50</sub>, to mechanism-based endpoints that can be accurately assessed <em>in vitro</em> or by using <em>in silico</em> prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how <em>in silico</em> approaches support the assessment of acute toxicity.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"24 ","pages":"Article 100237"},"PeriodicalIF":3.1000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Principles and procedures for assessment of acute toxicity incorporating in silico methods\",\"authors\":\"Craig M. Zwickl , Jessica C. Graham , Robert A. Jolly , Arianna Bassan , Ernst Ahlberg , Alexander Amberg , Lennart T. Anger , Lisa Beilke , Phillip Bellion , Alessandro Brigo , Heather Burleigh-Flayer , Mark T.D. Cronin , Amy A. Devlin , Trevor Fish , Susanne Glowienke , Kamila Gromek , Agnes L. Karmaus , Ray Kemper , Sunil Kulkarni , Elena Lo Piparo , Glenn J. Myatt\",\"doi\":\"10.1016/j.comtox.2022.100237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Acute <em>toxicity in silico</em> models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an <em>in silico</em> analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including <em>in silico</em> methods and <em>in vitro</em> or <em>in vivo</em> experiments. <em>In silico</em> methods that can assist the prediction of <em>in vivo</em> outcomes (<em>i.e.</em>, LD<sub>50</sub>) are analyzed concluding that predictions obtained using <em>in silico</em> approaches are now well-suited for reliably supporting assessment of LD<sub>50</sub>-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from <em>in vitro</em> studies commonly evaluated for predicting acute toxicity (<em>e.g.</em>, cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of <em>in vitro</em> data allow for a shift away from assessments solely based on endpoints such as LD<sub>50</sub>, to mechanism-based endpoints that can be accurately assessed <em>in vitro</em> or by using <em>in silico</em> prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how <em>in silico</em> approaches support the assessment of acute toxicity.</p></div>\",\"PeriodicalId\":37651,\"journal\":{\"name\":\"Computational Toxicology\",\"volume\":\"24 \",\"pages\":\"Article 100237\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468111322000251\",\"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/S2468111322000251","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
Principles and procedures for assessment of acute toxicity incorporating in silico methods
Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of the Globally Harmonized System (GHS) classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.
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