Amanda N. Buerger , Andrey Massarsky , Anthony Russell , Nicole Zoghby , Carole Hirn , Daniel Mucs , Irene Baskerville-Abraham , Andrew Maier
{"title":"Evaluation of chemical grouping workflows for flavor inhalation risk assessment: Selected furan moiety-containing chemicals as a case study","authors":"Amanda N. Buerger , Andrey Massarsky , Anthony Russell , Nicole Zoghby , Carole Hirn , Daniel Mucs , Irene Baskerville-Abraham , Andrew Maier","doi":"10.1016/j.comtox.2023.100269","DOIUrl":null,"url":null,"abstract":"<div><p><span><span>Read-across and chemical grouping approaches are increasingly utilized in risk assessment and regulatory submissions. The European Food Safety Authority (EFSA) has established Chemical Group (CG) 14, which contains furfuryl and furan derivatives both with and without side-chain substituents; however, the rationale for this grouping is not available based on current EFSA documentation. Therefore, this study aimed to identify the chemicals belonging to CG14, evaluate the constituent chemicals for metabolic, biological, and toxicological properties via clustering tools, and apply existing chemical grouping workflows for identifying representative chemicals for this group to support testing strategies. Membership to CG14 was difficult to identify, and varied by EFSA source (e.g., published reports, OpenFoodTox database). Based on predictions from the Organisation for Economic Co-operation and Development (OECD) </span>Quantitative Structure Activity Relationship (QSAR) Toolbox, ChemACE, SMARTCyp, and WhichCyp, as well as data extracted from the U.S. Environmental Protection Agency’s (EPA’s) Toxicity Forecaster (ToxCast) on CompTox Chemicals Dashboard and the European Chemicals Agency (ECHA) Dossier, no suitable metabolic, toxicological, structural, or mechanistic </span>clustering method was identified for CG14. Biological effect data were too sparse to refine the chemical subgroupings within CG14 with confidence based on existing read-across principles for chemical grouping. This paucity of data limits the development of a tiered testing strategy in which more complete testing would be conducted for selected representative CG14 compounds only. Therefore, efforts to generate key pieces of data (e.g., mode of action [MOA], metabolism) for chemical grouping and read-across are needed to apply this workflow to EFSA CG14.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-05-01","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/S2468111323000105","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
Read-across and chemical grouping approaches are increasingly utilized in risk assessment and regulatory submissions. The European Food Safety Authority (EFSA) has established Chemical Group (CG) 14, which contains furfuryl and furan derivatives both with and without side-chain substituents; however, the rationale for this grouping is not available based on current EFSA documentation. Therefore, this study aimed to identify the chemicals belonging to CG14, evaluate the constituent chemicals for metabolic, biological, and toxicological properties via clustering tools, and apply existing chemical grouping workflows for identifying representative chemicals for this group to support testing strategies. Membership to CG14 was difficult to identify, and varied by EFSA source (e.g., published reports, OpenFoodTox database). Based on predictions from the Organisation for Economic Co-operation and Development (OECD) Quantitative Structure Activity Relationship (QSAR) Toolbox, ChemACE, SMARTCyp, and WhichCyp, as well as data extracted from the U.S. Environmental Protection Agency’s (EPA’s) Toxicity Forecaster (ToxCast) on CompTox Chemicals Dashboard and the European Chemicals Agency (ECHA) Dossier, no suitable metabolic, toxicological, structural, or mechanistic clustering method was identified for CG14. Biological effect data were too sparse to refine the chemical subgroupings within CG14 with confidence based on existing read-across principles for chemical grouping. This paucity of data limits the development of a tiered testing strategy in which more complete testing would be conducted for selected representative CG14 compounds only. Therefore, efforts to generate key pieces of data (e.g., mode of action [MOA], metabolism) for chemical grouping and read-across are needed to apply this workflow to EFSA CG14.
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