Produção Nacional
Revisado por pares
CATMoS: Collaborative Acute Toxicity Modeling Suite
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 4 Linguagem: Francês
10.1289/ehp8495
ISSN1552-9924
AutoresKamel Mansouri, Agnes L. Karmaus, Jeremy Fitzpatrick, Grace Patlewicz, Prachi Pradeep, Domenico Alberga, Nathalie Alépée, Timothy E. H. Allen, Dave Allen, Vinícius M. Alves, Carolina Horta Andrade, Tyler R. Auernhammer, Davide Ballabio, Shannon Bell, Emilio Benfenati, Sudin Bhattacharya, Joyce V. Bastos, Stephen A. Boyd, J.B. Brown, Stephen J. Capuzzi, Yaroslav Chushak, Heather L. Ciallella, Alex M. Clark, Viviana Consonni, Pankaj Daga, Sean Ekins, Sherif Farag, Maxim V. Fedorov, Denis Fourches, Domenico Gadaleta, Feng Gao, Jeffery M. Gearhart, Garett Goh, Jonathan M. Goodman, Francesca Grisoni, Chris Grulke, Thomas Härtung, Matthew Hirn, Pavel Karpov, Alexandru Korotcov, Giovanna J. Lavado, Michael S. Lawless, Xinhao Li, Thomas Luechtefeld, Filippo Lunghini, Giuseppe Felice Mangiatordi, Gilles Marcou, Dan H. Marsh, Todd M. Martin, Andrea Mauri, Eugene Muratov, Glenn J. Myatt, Ðắc-Trung Nguyễn, Orazio Nicolotti, Reine Note, Paritosh Pande, Amanda K. Parks, Tyler Peryea, Ahsan Habib Polash, Robert Ralló, Alessandra Roncaglioni, Craig Rowlands, Patricia Ruiz, Daniel P. Russo, Ahmed E Sayed, Risa Sayre, Timothy Sheils, Charles Siegel, Arthur C. Silva, Anton Simeonov, Sergey Sosnin, Noel Southall, Judy Strickland, Yun Tang, Brian J. Teppen, Igor V. Tetko, Dennis Thomas, Valery Tkachenko, Roberto Todeschini, Cosimo Toma, Ignacio J. Tripodi, Daniela Trisciuzzi, Alexander Tropsha, Alexandre Varnek, Kristijan Vuković, Zhongyu Wang, Liguo Wang, Katrina M. Waters, Andrew J. Wedlake, Sanjeeva J. Wijeyesakere, Dan Wilson, Zijun Xiao, Hongbin Yang, Gergely Zahoránszky-Kőhalmi, Alexey Zakharov, Fagen F. Zhang, Zhen Zhang, Tongan Zhao, Hao Zhu, Kimberley M. Zorn, Warren Casey, Nicole Kleinstreuer,
Tópico(s)Metabolomics and Mass Spectrometry Studies
ResumoVol. 129, No. 4 ResearchOpen AccessCATMoS: Collaborative Acute Toxicity Modeling Suiteis corrected byErratum: CATMoS: Collaborative Acute Toxicity Modeling SuiteErratum: CATMoS: Collaborative Acute Toxicity Modeling Suite Kamel Mansouri, Agnes L. Karmaus, Jeremy Fitzpatrick, Grace Patlewicz, Prachi Pradeep, Domenico Alberga, Nathalie Alepee, Timothy E.H. Allen, Dave Allen, Vinicius M. Alves, Carolina H. Andrade, Tyler R. Auernhammer, Davide Ballabio, Shannon Bell, Emilio Benfenati, Sudin Bhattacharya, Joyce V. Bastos, Stephen Boyd, J.B. Brown, Stephen J. Capuzzi, Yaroslav Chushak, Heather Ciallella, Alex M. Clark, Viviana Consonni, Pankaj R. Daga, Sean Ekins, Sherif Farag, Maxim Fedorov, Denis Fourches, Domenico Gadaleta, Feng Gao, Jeffery M. Gearhart, Garett Goh, Jonathan M. Goodman, Francesca Grisoni, Christopher M. Grulke, Thomas Hartung, Matthew Hirn, Pavel Karpov, Alexandru Korotcov, Giovanna J. Lavado, Michael Lawless, Xinhao Li, Thomas Luechtefeld, Filippo Lunghini, Giuseppe F. Mangiatordi, Gilles Marcou, Dan Marsh, Todd Martin, Andrea Mauri, Eugene N. Muratov, Glenn J. Myatt, Dac-Trung Nguyen, Orazio Nicolotti, Reine Note, Paritosh Pande, Amanda K. Parks, Tyler Peryea, Ahsan H. Polash, Robert Rallo, Alessandra Roncaglioni, Craig Rowlands, Patricia Ruiz, Daniel P. Russo, Ahmed Sayed, Risa Sayre, Timothy Sheils, Charles Siegel, Arthur C. Silva, Anton Simeonov, Sergey Sosnin, Noel Southall, Judy Strickland, Yun Tang, Brian Teppen, Igor V. Tetko, Dennis Thomas, Valery Tkachenko, Roberto Todeschini, Cosimo Toma, Ignacio Tripodi, Daniela Trisciuzzi, Alexander Tropsha, Alexandre Varnek, Kristijan Vukovic, Zhongyu Wang, Liguo Wang, Katrina M. Waters, Andrew J. Wedlake, Sanjeeva J. Wijeyesakere, Dan Wilson, Zijun Xiao, Hongbin Yang, Gergely Zahoranszky-Kohalmi, Alexey V. Zakharov, Fagen F. Zhang, Zhen Zhang, Tongan Zhao, Hao Zhu, Kimberley M. Zorn, Warren Casey, and Nicole C. Kleinstreuer Kamel Mansouri Address correspondence to Nicole Kleinstreuer, Email: E-mail Address: [email protected]; or, Kamel Mansouri, Email: E-mail Address: kame[email protected]; 530 Davis Dr, Durham, NC 27703, USA Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA , Agnes L. Karmaus Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA , Jeremy Fitzpatrick ScitoVation, Research Triangle Park, North Carolina, USA , Grace Patlewicz Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA , Prachi Pradeep Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA , Domenico Alberga Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy , Nathalie Alepee L'Oréal Research & Innovation, Aulnay-sous-Bois, France , Timothy E.H. Allen Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK , Dave Allen Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA , Vinicius M. Alves 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 Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil , Carolina H. Andrade Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil , Tyler R. Auernhammer The Dow Chemical Company, Midland, Michigan, USA , Davide Ballabio Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy , Shannon Bell Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA , Emilio Benfenati Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy , Sudin Bhattacharya Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA , Joyce V. Bastos Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil , Stephen Boyd Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA , J.B. Brown Kyoto University Graduate School of Medicine, Kyoto, Japan , Stephen J. Capuzzi 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 , Yaroslav Chushak Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA , Heather Ciallella Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA , Alex M. Clark Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA , Viviana Consonni Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy , Pankaj R. Daga Simulations Plus, Inc., Lancaster, California, USA , Sean Ekins Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA , Sherif Farag 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 , Maxim Fedorov Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia , Denis Fourches Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA , Domenico Gadaleta Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy , Feng Gao Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA , Jeffery M. Gearhart Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA , Garett Goh Pacific Northwest National Laboratory, Richland, Washington, USA , Jonathan M. Goodman Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK , Francesca Grisoni Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy , Christopher M. Grulke Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA , Thomas Hartung Underwriters Laboratories, Northbrook, Illinois, USA , Matthew Hirn Department of Computational Mathematics, Science & Engineering, Department of Mathematics, Michigan State University, East Lansing, Michigan, USA , Pavel Karpov Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany , Alexandru Korotcov Science Data Software, LLC, Rockville, Maryland, USA , Giovanna J. Lavado Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy , Michael Lawless Simulations Plus, Inc., Lancaster, California, USA , Xinhao Li Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA , Thomas Luechtefeld Underwriters Laboratories, Northbrook, Illinois, USA , Filippo Lunghini Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France , Giuseppe F. Mangiatordi Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy , Gilles Marcou Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France , Dan Marsh Underwriters Laboratories, Northbrook, Illinois, USA , Todd Martin Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA , Andrea Mauri Alvascience Srl, Lecco, Italy , Eugene N. Muratov 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 Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil , Glenn J. Myatt Leadscope Inc., Columbus, Ohio, USA , Dac-Trung Nguyen National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Orazio Nicolotti Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy , Reine Note L'Oréal Research & Innovation, Aulnay-sous-Bois, France , Paritosh Pande Pacific Northwest National Laboratory, Richland, Washington, USA , Amanda K. Parks The Dow Chemical Company, Midland, Michigan, USA , Tyler Peryea National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Ahsan H. Polash Kyoto University Graduate School of Medicine, Kyoto, Japan , Robert Rallo Pacific Northwest National Laboratory, Richland, Washington, USA , Alessandra Roncaglioni Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy , Craig Rowlands Underwriters Laboratories, Northbrook, Illinois, USA , Patricia Ruiz Office of Innovation and Analytics, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA , Daniel P. Russo Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA , Ahmed Sayed Rosettastein Consulting UG, Freising, Germany , Risa Sayre Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA , Timothy Sheils National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Charles Siegel Pacific Northwest National Laboratory, Richland, Washington, USA , Arthur C. Silva Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil , Anton Simeonov National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Sergey Sosnin Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia , Noel Southall National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Judy Strickland Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA , Yun Tang Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China , Brian Teppen Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA , Igor V. Tetko Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany BIGCHEM GmbH, Unterschleissheim, Germany , Dennis Thomas Pacific Northwest National Laboratory, Richland, Washington, USA , Valery Tkachenko Science Data Software, LLC, Rockville, Maryland, USA , Roberto Todeschini Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy , Cosimo Toma Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy , Ignacio Tripodi Computer Science/Interdisciplinary Quantitative Biology, University of Colorado, Boulder, Colorado, USA , Daniela Trisciuzzi Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy , Alexander Tropsha 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 , Alexandre Varnek Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France , Kristijan Vukovic Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy , Zhongyu Wang School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China , Liguo Wang School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China , Katrina M. Waters Pacific Northwest National Laboratory, Richland, Washington, USA , Andrew J. Wedlake Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK , Sanjeeva J. Wijeyesakere The Dow Chemical Company, Midland, Michigan, USA , Dan Wilson The Dow Chemical Company, Midland, Michigan, USA , Zijun Xiao School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China , Hongbin Yang Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China , Gergely Zahoranszky-Kohalmi National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Alexey V. Zakharov National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Fagen F. Zhang The Dow Chemical Company, Midland, Michigan, USA , Zhen Zhang Dow Agrosciences, Indianapolis, Indiana, USA , Tongan Zhao National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA , Hao Zhu Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA , Kimberley M. Zorn Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA , Warren Casey National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA , and Nicole C. Kleinstreuer Address correspondence to Nicole Kleinstreuer, Email: E-mail Address: [email protected]; or, Kamel Mansouri, Email: E-mail Address: [email protected]; 530 Davis Dr, Durham, NC 27703, USA National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA Published:30 April 2021CID: 047013https://doi.org/10.1289/EHP8495Cited by:4AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground: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/EHP8495IntroductionAcute systemic toxicity studies are required by regulators around the world to inform chemical hazard classification, labeling, and risk management. The testing to assess acute systemic toxicity is conducted in vivo through a predefined route of exposure (oral, dermal, or via inhalation) during a fixed observation period as described in test guidelines issued by the Organization for Economic Cooperation and Development (OECD) (OECD 2002a, 2002b, 2002c, 2008). Five U.S. agencies [Consumer Product Safety Commission (CPSC), Department of Defense (DoD), Department of Transportation (DoT), Environmental Protection Agency (U.S. EPA), Occupational Safety and Health Administration (OSHA)], as well as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) in Europe use the median Lethal Dose 50 (LD50; the dose of a substance that would be expected to kill half the animals in a test group) from acute oral toxicity data for the classification and labeling of chemical substances (ECHA 2008; Kleinstreuer et al. 2018; Strickland et al. 2018). However, in vivo acute oral toxicity testing is cost- and time-prohibitive and raises ethical concerns related to the use of many animals. Given the large number of new and existing substances requiring assessment, there is a pressing need for cost-effective and rapid nonanimal alternatives.Recent technological advances in computational resources and artificial intelligence have increased the accuracy and speed of machine learning algorithms. As a result, in silico approaches such as quantitative structure–activity relationships (QSARs) are being increasingly recognized as alternatives to bridge the lack of knowledge about chemical properties and their biological activities. QSARs are being promoted for their ability to accurately predict toxicological end points at low cost but also for being reliable, reproducible, and broadly applicable to the diversity of chemicals requiring testing (Dearden et al. 2009; Worth et al. 2005). Consequently, the integration of nonanimal methods for assessing chemical toxicity is gaining momentum. In Europe, REACH regulations call for the use of nonanimal methods to assess chemical toxicity (Benfenati et al. 2011; European Commission, Environment Directorate General 2007; Lahl and Hawxwell 2006). Similarly, in 2020, U.S. EPA created a New Approach Methods (NAMs) Work Plan to prioritize agency efforts and resources toward activities that will reduce the use of animal testing while continuing to protect human health and the environment (U.S. EPA 2020). Furthermore, the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), consisting of representatives from 16 U.S. federal agencies, has several workgroups focused on the development or validation of NAMs. These workgroups contribute to the goals of the ICCVAM Strategic Roadmap for Establishing New Approaches to Evaluate the Safety of Chemicals and Medical Products in the United States (Interagency Coordinating Committee on the Validation of Alternative Methods 2018). One of the ICCVAM ad hoc workgroups established was the Acute Toxicity Workgroup (ATWG), which sought to develop an implementation plan for identifying, evaluating, and applying alternative methods for acute systemic toxicity (Kleinstreuer et al. 2018; Lowit et al. 2017). An initial ATWG study was conducted to assess the acute toxicity data regulatory requirements, needs, and decision contexts of member agencies as well as to understand the current acceptance of alternative methods (Strickland et al. 2018). Subsequent charges of the ATWG were to identify, acquire, and curate high-quality data from reference test methods that could be used to evaluate existing models for acute toxicity as well as investigate the feasibility of developing new models. Focusing initially on the oral route of exposure to evaluate existing in silico models, the ATWG organized an international collaborative project to develop new in silico models for predicting acute oral systemic toxicity (Kleinstreuer et al. 2018; Strickland et al. 2018).International consortia have successfully developed collaborative computational solutions for challenging toxicological problems. Examples in the area of endocrine disruption screening include the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) (Mansouri et al. 2016a) and the Collaborative Modeling Project for Androgen Receptor (CoMPARA) (Mansouri et al. 2020). The predictive consensus models from these projects have been integrated to assess the endocrine activity potential of organic chemicals within the EPA's Endocrine Disruptor Screening Program (EDSP) (U.S. EPA-NCCT 2014b). The global network of experts represented by these successful consortia was leveraged for the current acute oral systemic toxicity modeling project, and the legacy workflows from CERAPP and CoMPARA were adapted and applied for the data analysis and modeling conducted herein.For the current project, the U.S. National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) and the U.S. EPA's Center for Computational Toxicology and Exposure (CCTE) collected and curated rat oral LD50 data for more than 15,000 substances from public sources to produce data sets that were used during the project as training and evaluation sets (Karmaus et al. 2019; Kleinstreuer et al. 2018). Thirty-five international collaborators representing various sectors, including government, industry, and academia, participated in this effort, which produced a total of 139 different models. All submitted models were both quantitatively and qualitatively evaluated. A workshop was convened ( https://ntp.niehs.nih.gov/go/atwksp-2018) to bring contributing computational modelers and regulatory decision makers together to discuss the feasibility of using in silico predictions for regulatory use in lieu of in vivo acute oral systemic toxicity testing (Kleinstreuer et al. 2018). Ultimately, predictions within the applicability domains of the developed models were combined into consensus predictions based on a weight-of-evidence (WoE) approach, forming the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS was then implemented into the open-source, open-data OPERA [OPEn (q)saR App] tool to enable further screening of new chemicals (Mansouri et al. 2016b, 2018). This paper provides a description of the data on which the CATMoS models are based, the evaluation process, and development of consensus models. We close with a discussion of the limitations of CATMoS and a description of implementation and additional evaluation of the model.Materials and MethodsU.S. Regulatory Uses for Acute Oral Toxicity DataPrior to identifying any existing alternative methods or investing in the development, optimization, and validation of new ones, it is important to understand the current regulatory needs and decision contexts, including the use and acceptance of nonanimal data for the toxicological end point of concern. Strickland et al. (2018) described the use of acute oral toxicity data by ICCVAM regulatory agencies to provide a basis for identifying opportunities for flexibility with regard to replacing or reducing the need for in vivo acute oral toxicity studies (Strickland et al. 2018). The regulatory needs of these agencies require three different types of acute toxicity outcomes, as detailed in Table 1: a) an LD50 value estimate; b) a binary outcome based on a single threshold; and c) a multiclass scheme based on different thresholds. Two binary models were relevant to U.S. agencies: a) the identification of whether a chemical was "very toxic" (i.e., LD50≤50mg/kg); and b) identification of whether a chemical was "nontoxic" (i.e., LD50>2,000mg/kg). Multiclass schemes in use by several agencies included hazard categories defined by the U.S. EPA and the U.N. Globally Harmonized System of Classification and Labeling of Chemicals (GHS), which consist of four or five categories, respectively (Table 2) (Strickland et al. 2018).Table 1 Acute oral toxicity classification strategies used by U.S. regulatory agencies.Table 1 has three columns, namely, Requirement, Description, and Agencies.RequirementDescriptionAgenciesBinaryLD50 values above or below specific thresholdCPSC, DoDMulti-classMultiple ranges of LD50 valuesEPA, OSHA, DOTLD50 valueDiscrete LD50 valuesEPA, CPSC, DoDNote: See (Strickland et al. 2018). CPSC, Consumer Product Safety Commission; DoD, U.S. Department of Defense; DOT, U.S. Department of Transportation; EPA, U.S. Environmental Protection Agency; OSHA, U.S. Occupational Safety and Health Administration.Table 2 U.S. EPA and GHS hazard labeling categories.Table 2 has three columns, namely, Categories, Environmental Protection Agency dose of a substance that would be expected to kill half the animals in a test group thresholds, and United Nations Globally Harmonized System of Classification and Labeling of Chemicals dose of a substance that would be expected to kill half the animals in a test group thresholds.CategoriesEPA LD50 thresholdsGHS LD50 thresholds1≤50mg/kg≤5mg/kgII>50≤500mg/kg>5≤50mg/kgIII>500≤5,000mg/kg>50≤300mg/kgIV>5,000mg/kg>300≤2,000mg/kgVNA>2000mg/kgNote: NA, No EPA Cat V; See (Strickland et al. 2018). EPA, U.S. Environmental Protection Agency; GHS, U.N. Globally Harmonized System of Classification and Labeling of Chemicals; LD50,dose of a substance that would be expected to kill half the animals in a test group.Based on this information, for this project we asked participants to develop models to predict one or more of the following end points: Very toxic (VT; LD50≤50mg/kg vs. all others)Nontoxic (NT; LD50>2,000mg/kg vs. all others)U.S. EPA hazard categories (U.S. EPA 2016)GHS hazard categories (United Nations 2015)Point estimate LD50 values.Data SetsData collection and preprocessing.The data set underlying the modeling effort for this project was initially compiled by NICEATM and U.S. EPA's CCTE. Briefly, LD50 data were collected from rat acute oral systemic toxicity tests, including limit tests, ranges/confidence intervals, and discrete LD50 values. The full data set included 21,200 LD50 entries for 15,688 substances. These data came from a variety of publicly available databases, including OECD's eChemPortal, the National Library of Medicine's Hazardous Substances Data Bank (NLM HSDB), ChemIDplus databases, and the European Commission Joint Research Center's (JRC) AcutoxBase (Karmaus et al. 2019; Kleinstreuer et al. 2018; NTP 2018). Data were reviewed to ensure that LD50 values with obvious errors in the extracted data such as unit conversion errors (e.g., comma and decimal separator misplacements) were either fixed or removed. After this review, 16,209 LD50 values remained. Many of the chemicals represented had multiple LD50 entries, requiring that a single representative value per Chemical Abstracts Service Registry Number (CASRN) identifier be defined to facilitate modeling efforts. Based on ATWG feedback and to define the representative LD50 as a protective value while accounting for the distribution across multiple LD50s, the median of the lowest quartile was computed using only discrete LD50 values (omitting limit test data and range and confidence interval data). A detailed summary of the data compilation is available online on the NICEATM webpage dedicated to the collaborative modeling project (NTP 2020).To obtain chemical structure information, CASRNs served as identifiers to search the U.S. EPA's DSSTox database hosted in the CompTox Chemicals Dashboard (Grulke et al. 2019; Richard and Williams 2002; U.S. EPA-NCCT 2014a; Williams et al. 2017) as well as other cross-checked online databases: ChemIDPlus (NIH 2016), PubChem (Bolton et al. 2008) and ChemSpider (Royal Society of Chemistry 2015). The collected structures wer
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