Chihae Yang , James F Rathman , Bruno Bienfait , Matthew Burbank , Ann Detroyer , Steven J. Enoch , James W. Firman , Steve Gutsell , Nicola J. Hewitt , Bryan Hobocienski , Gerry Kenna , Judith C. Madden , Tomasz Magdziarz , Jörg Marusczyk , Aleksandra Mostrag-Szlichtyng , Christopher-Tilman Krueger , Cathy Lester , Catherine Mahoney , Abdulkarim Najjar , Gladys Ouedraogo , Mark T.D. Cronin
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The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of <em>in silico</em> and <em>in vitro</em> data. To this end, ChemTunes•ToxGPS® has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes•ToxGPS® platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for <em>Perilla frutescens</em> through the workflows of threshold of toxicological concern (TTC), <em>in silico</em> predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced <em>in silico</em> tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the workgroup and international regulatory entities.</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":"1","resultStr":"{\"title\":\"The role of a molecular informatics platform to support next generation risk assessment\",\"authors\":\"Chihae Yang , James F Rathman , Bruno Bienfait , Matthew Burbank , Ann Detroyer , Steven J. Enoch , James W. Firman , Steve Gutsell , Nicola J. Hewitt , Bryan Hobocienski , Gerry Kenna , Judith C. Madden , Tomasz Magdziarz , Jörg Marusczyk , Aleksandra Mostrag-Szlichtyng , Christopher-Tilman Krueger , Cathy Lester , Catherine Mahoney , Abdulkarim Najjar , Gladys Ouedraogo , Mark T.D. 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The role of a molecular informatics platform to support next generation risk assessment
Chemoinformatics has been successfully employed in safety assessment through various regulatory programs for which information from databases, as well as predictive methodologies including computational methods, are accepted. One example is the European Union Cosmetics Products Regulations, for which Cosmetics Europe (CE) research activities in non-animal methods have been managed by the Long Range Science Strategy (LRSS) program. The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of in silico and in vitro data. To this end, ChemTunes•ToxGPS® has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes•ToxGPS® platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for Perilla frutescens through the workflows of threshold of toxicological concern (TTC), in silico predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced in silico tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the workgroup and international regulatory entities.
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