Kamel Mansouri1,2, Agnes L Karmaus1, Jeremy Fitzpatrick3, Grace Patlewicz4, Prachi Pradeep4,5, Domenico Alberga6, Nathalie Alepee7, Timothy E H Allen8, Dave Allen1, Vinicius M Alves9,10, Carolina H Andrade10, Tyler R Auernhammer11, Davide Ballabio12, Shannon Bell1, Emilio Benfenati13, Sudin Bhattacharya14, Joyce V Bastos10, Stephen Boyd15, J B Brown16, Stephen J Capuzzi9, Yaroslav Chushak17,18, Heather Ciallella19, Alex M Clark20, Viviana Consonni12, Pankaj R Daga21, Sean Ekins20, Sherif Farag9, Maxim Fedorov22, Denis Fourches23,24, Domenico Gadaleta13, Feng Gao15, Jeffery M Gearhart17,18, Garett Goh25, Jonathan M Goodman8, Francesca Grisoni12, Christopher M Grulke4, Thomas Hartung26, Matthew Hirn27, Pavel Karpov28, Alexandru Korotcov29, Giovanna J Lavado13, Michael Lawless21, Xinhao Li23, Thomas Luechtefeld26, Filippo Lunghini30, Giuseppe F Mangiatordi6, Gilles Marcou30, Dan Marsh26, Todd Martin31, Andrea Mauri32, Eugene N Muratov9,10, Glenn J Myatt33, Dac-Trung Nguyen34, Orazio Nicolotti6, Reine Note7, Paritosh Pande25, Amanda K Parks11, Tyler Peryea34, Ahsan H Polash16, Robert Rallo25, Alessandra Roncaglioni13, Craig Rowlands26, Patricia Ruiz35, Daniel P Russo19, Ahmed Sayed36, Risa Sayre4,5, Timothy Sheils34, Charles Siegel25, Arthur C Silva10, Anton Simeonov34, Sergey Sosnin22, Noel Southall34, Judy Strickland1, Yun Tang37, Brian Teppen15, Igor V Tetko28,38, Dennis Thomas25, Valery Tkachenko29, Roberto Todeschini12, Cosimo Toma13, Ignacio Tripodi39, Daniela Trisciuzzi6, Alexander Tropsha9, Alexandre Varnek30, Kristijan Vukovic13, Zhongyu Wang40, Liguo Wang40, Katrina M Waters25, Andrew J Wedlake8, Sanjeeva J Wijeyesakere11, Dan Wilson11, Zijun Xiao40, Hongbin Yang37, Gergely Zahoranszky-Kohalmi34, Alexey V Zakharov34, Fagen F Zhang11, Zhen Zhang41, Tongan Zhao34, Hao Zhu19, Kimberley M Zorn20, Warren Casey2, Nicole C Kleinstreuer2. 1. Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA. 2. National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA. 3. ScitoVation, Research Triangle Park, North Carolina, USA. 4. Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA. 5. Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA. 6. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy. 7. L'Oréal Research & Innovation, Aulnay-sous-Bois, France. 8. Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. 9. Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA. 10. Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil. 11. The Dow Chemical Company, Midland, Michigan, USA. 12. Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy. 13. Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy. 14. Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA. 15. Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA. 16. Kyoto University Graduate School of Medicine, Kyoto, Japan. 17. Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA. 18. Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA. 19. Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA. 20. Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA. 21. Simulations Plus, Inc., Lancaster, California, USA. 22. Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia. 23. Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA. 24. Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA. 25. Pacific Northwest National Laboratory, Richland, Washington, USA. 26. Underwriters Laboratories, Northbrook, Illinois, USA. 27. Department of Computational Mathematics, Science & Engineering, Department of Mathematics, Michigan State University, East Lansing, Michigan, USA. 28. Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany. 29. Science Data Software, LLC, Rockville, Maryland, USA. 30. Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France. 31. Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA. 32. Alvascience Srl, Lecco, Italy. 33. Leadscope Inc., Columbus, Ohio, USA. 34. National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA. 35. Office of Innovation and Analytics, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. 36. Rosettastein Consulting UG, Freising, Germany. 37. Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China. 38. BIGCHEM GmbH, Unterschleissheim, Germany. 39. Computer Science/Interdisciplinary Quantitative Biology, University of Colorado, Boulder, Colorado, USA. 40. School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China. 41. Dow Agrosciences, Indianapolis, Indiana, USA.
Abstract
BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.
BACKGROUND:Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.
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