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CATMoS: Collaborative Acute Toxicity Modeling Suite.
Mansouri, Kamel; Karmaus, Agnes L; Fitzpatrick, Jeremy; Patlewicz, Grace; Pradeep, Prachi; Alberga, Domenico; Alepee, Nathalie; Allen, Timothy E H; Allen, Dave; Alves, Vinicius M; Andrade, Carolina H; Auernhammer, Tyler R; Ballabio, Davide; Bell, Shannon; Benfenati, Emilio; Bhattacharya, Sudin; Bastos, Joyce V; Boyd, Stephen; Brown, J B; Capuzzi, Stephen J; Chushak, Yaroslav; Ciallella, Heather; Clark, Alex M; Consonni, Viviana; Daga, Pankaj R; Ekins, Sean; Farag, Sherif; Fedorov, Maxim; Fourches, Denis; Gadaleta, Domenico; Gao, Feng; Gearhart, Jeffery M; Goh, Garett; Goodman, Jonathan M; Grisoni, Francesca; Grulke, Christopher M; Hartung, Thomas; Hirn, Matthew; Karpov, Pavel; Korotcov, Alexandru; Lavado, Giovanna J; Lawless, Michael; Li, Xinhao; Luechtefeld, Thomas; Lunghini, Filippo; Mangiatordi, Giuseppe F; Marcou, Gilles; Marsh, Dan; Martin, Todd; Mauri, Andrea.
Affiliation
  • Mansouri K; Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA.
  • Karmaus AL; National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA.
  • Fitzpatrick J; Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA.
  • Patlewicz G; ScitoVation, Research Triangle Park, North Carolina, USA.
  • Pradeep P; Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.
  • Alberga D; Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.
  • Alepee N; Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.
  • Allen TEH; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy.
  • Allen D; L'Oréal Research & Innovation, Aulnay-sous-Bois, France.
  • Alves VM; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
  • Andrade CH; Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA.
  • Auernhammer TR; 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.
  • Ballabio D; Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil.
  • Bell S; Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil.
  • Benfenati E; The Dow Chemical Company, Midland, Michigan, USA.
  • Bhattacharya S; Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy.
  • Bastos JV; Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA.
  • Boyd S; Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
  • Brown JB; Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA.
  • Capuzzi SJ; Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil.
  • Chushak Y; Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA.
  • Ciallella H; Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Clark AM; 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.
  • Consonni V; Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA.
  • Daga PR; Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA.
  • Ekins S; Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA.
  • Farag S; Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
  • Fedorov M; Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy.
  • Fourches D; Simulations Plus, Inc., Lancaster, California, USA.
  • Gadaleta D; Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA.
  • Gao F; 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.
  • Gearhart JM; Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia.
  • Goh G; Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA.
  • Goodman JM; Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA.
  • Grisoni F; Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
  • Grulke CM; Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA.
  • Hartung T; Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA.
  • Hirn M; Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA.
  • Karpov P; Pacific Northwest National Laboratory, Richland, Washington, USA.
  • Korotcov A; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
  • Lavado GJ; Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy.
  • Lawless M; Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.
  • Li X; Underwriters Laboratories, Northbrook, Illinois, USA.
  • Luechtefeld T; Department of Computational Mathematics, Science & Engineering, Department of Mathematics, Michigan State University, East Lansing, Michigan, USA.
  • Lunghini F; Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany.
  • Mangiatordi GF; Science Data Software, LLC, Rockville, Maryland, USA.
  • Marcou G; Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
  • Marsh D; Simulations Plus, Inc., Lancaster, California, USA.
  • Martin T; Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA.
  • Mauri A; Underwriters Laboratories, Northbrook, Illinois, USA.
Environ Health Perspect ; 129(4): 47013, 2021 04.
Article in En | MEDLINE | ID: mdl-33929906
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Government Agencies Type of study: Prognostic_studies Limits: Animals Country/Region as subject: America do norte Language: En Journal: Environ Health Perspect Year: 2021 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Government Agencies Type of study: Prognostic_studies Limits: Animals Country/Region as subject: America do norte Language: En Journal: Environ Health Perspect Year: 2021 Document type: Article Affiliation country: