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Aurisano N, Fantke P, Chiu WA, Judson R, Jang S, Unnikrishnan A, Jolliet O. Probabilistic Reference and 10% Effect Concentrations for Characterizing Inhalation Non-cancer and Developmental/Reproductive Effects for 2,160 Substances. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8278-8288. [PMID: 38697947 PMCID: PMC11097392 DOI: 10.1021/acs.est.4c00207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/05/2024]
Abstract
Chemicals assessment and management frameworks rely on regulatory toxicity values, which are based on points of departure (POD) identified following rigorous dose-response assessments. Yet, regulatory PODs and toxicity values for inhalation exposure (i.e., reference concentrations [RfCs]) are available for only ∼200 chemicals. To address this gap, we applied a workflow to determine surrogate inhalation route PODs and corresponding toxicity values, where regulatory assessments are lacking. We curated and selected inhalation in vivo data from the U.S. EPA's ToxValDB and adjusted reported effect values to chronic human equivalent benchmark concentrations (BMCh) following the WHO/IPCS framework. Using ToxValDB chemicals with existing PODs associated with regulatory toxicity values, we found that the 25th %-ile of a chemical's BMCh distribution (POD p 25 BMC h ) could serve as a suitable surrogate for regulatory PODs (Q2 ≥ 0.76, RSE ≤ 0.82 log10 units). We applied this approach to derive POD p 25 BMC h for 2,095 substances with general non-cancer toxicity effects and 638 substances with reproductive/developmental toxicity effects, yielding a total coverage of 2,160 substances. From these POD p 25 BMC h , we derived probabilistic RfCs and human population effect concentrations. With this work, we have expanded the number of chemicals with toxicity values available, thereby enabling a much broader coverage for inhalation risk and impact assessment.
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Affiliation(s)
- Nicolò Aurisano
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, Kgs., Lyngby 2800, Denmark
| | - Peter Fantke
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, Kgs., Lyngby 2800, Denmark
| | - Weihsueh A. Chiu
- Department
of Veterinary Integrative Biosciences, College of Veterinary Medicine
and Biomedical Sciences, Texas A&M University, College Station, Texas 77843, United
States
| | - Richard Judson
- National
Center for Computational Toxicology, U.S.
Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27711, United States
| | - Suji Jang
- Department
of Veterinary Integrative Biosciences, College of Veterinary Medicine
and Biomedical Sciences, Texas A&M University, College Station, Texas 77843, United
States
| | - Aswani Unnikrishnan
- National
Center for Computational Toxicology, U.S.
Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27711, United States
| | - Olivier Jolliet
- Quantitative
Sustainability Assessment, Department of Environmental and Resource
Engineering, Technical University of Denmark, Bygningstorvet 115, Kgs., Lyngby 2800, Denmark
- Department
of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States
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2
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Kalyva ME, Vist GE, Diemar MG, López-Soop G, Bozada TJ, Luechtefeld T, Roggen EL, Dirven H, Vinken M, Husøy T. Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: A systematic scoping review. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 352:124109. [PMID: 38718961 DOI: 10.1016/j.envpol.2024.124109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024]
Abstract
Exposure assessment is a crucial component of environmental health research, providing essential information on the potential risks associated with various chemicals. A systematic scoping review was conducted to acquire an overview of accessible human exposure assessment methods and computational tools to support and ultimately improve risk assessment. The systematic scoping review was performed in Sysrev, a web platform that introduces machine learning techniques into the review process aiming for increased accuracy and efficiency. Included publications were restricted to a publication date after the year 2000, where exposure methods were properly described. Exposure assessments methods were found to be used for a broad range of environmental chemicals including pesticides, metals, persistent chemicals, volatile organic compounds, and other chemical classes. Our results show that after the year 2000, for all the types of exposure routes, probabilistic analysis, and computational methods to calculate human exposure have increased. Sixty-three mathematical models and toolboxes were identified that have been developed in Europe, North America, and globally. However, only twelve occur frequently and their usefulness were associated with exposure route, chemical classes and input parameters used to estimate exposure. The outcome of the combined associations can function as a basis and/or guide for decision making for the selection of most appropriate method and tool to be used for environmental chemical human exposure assessments in Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment (ONTOX) project and elsewhere. Finally, the choice of input parameters used in each mathematical model and toolbox shown by our analysis can contribute to the harmonization process of the exposure models and tools increasing the prospect for comparison between studies and consistency in the regulatory process in the future.
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Affiliation(s)
- Maria E Kalyva
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway.
| | - Gunn E Vist
- Norwegian Institute of Public Health, Division for Health Services, Oslo, Norway
| | | | - Graciela López-Soop
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway
| | - T J Bozada
- Toxtrack LLC, Baltimore, MD, United States
| | - Thomas Luechtefeld
- Toxtrack LLC, Baltimore, MD, United States; Insilica LLC, Bethesda, MD, United States
| | - Erwin L Roggen
- 3Rs Management and Consulting ApS, Kongens Lyngby, Denmark
| | - Hubert Dirven
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway
| | - Mathieu Vinken
- Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Trine Husøy
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway
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3
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Kvasnicka J, Aurisano N, von Borries K, Lu EH, Fantke P, Jolliet O, Wright FA, Chiu WA. Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38693844 DOI: 10.1021/acs.est.4c00172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.
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Affiliation(s)
- Jacob Kvasnicka
- Department of Veterinary Physiology and Pharmacology, Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
| | - Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Kerstin von Borries
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - En-Hsuan Lu
- Department of Veterinary Physiology and Pharmacology, Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Olivier Jolliet
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Fred A Wright
- Departments of Statistics and Biological Sciences and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Weihsueh A Chiu
- Department of Veterinary Physiology and Pharmacology, Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
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4
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Simon TW, Ryman J, Becker RA. Commentary: Value of information case study strongly supports use of the Threshold of Toxicological Concern (TTC). Regul Toxicol Pharmacol 2024; 149:105594. [PMID: 38555099 DOI: 10.1016/j.yrtph.2024.105594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 04/02/2024]
Abstract
A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.
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5
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Tkalec Ž, Antignac JP, Bandow N, Béen FM, Belova L, Bessems J, Le Bizec B, Brack W, Cano-Sancho G, Chaker J, Covaci A, Creusot N, David A, Debrauwer L, Dervilly G, Duca RC, Fessard V, Grimalt JO, Guerin T, Habchi B, Hecht H, Hollender J, Jamin EL, Klánová J, Kosjek T, Krauss M, Lamoree M, Lavison-Bompard G, Meijer J, Moeller R, Mol H, Mompelat S, Van Nieuwenhuyse A, Oberacher H, Parinet J, Van Poucke C, Roškar R, Togola A, Trontelj J, Price EJ. Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment. ENVIRONMENT INTERNATIONAL 2024; 186:108585. [PMID: 38521044 DOI: 10.1016/j.envint.2024.108585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 03/25/2024]
Abstract
The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.
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Affiliation(s)
- Žiga Tkalec
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Jožef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia.
| | | | - Nicole Bandow
- German Environment Agency, Laboratory for Water Analysis, Colditzstraße 34, 12099 Berlin, Germany.
| | - Frederic M Béen
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; KWR Water Research Institute, Nieuwegein, The Netherlands.
| | - Lidia Belova
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium.
| | - Jos Bessems
- Flemish Institute for Technological Research (VITO), Mol, Belgium.
| | | | - Werner Brack
- Helmholtz Centre for Environmental Research GmbH - UFZ, Department of Effect-Directed Analysis, Permoserstraße 15, 04318 Leipzig, Germany; Goethe University Frankfurt, Department of Evolutionary Ecology and Environmental Toxicology, Max-von-Laue-Strasse 13, 60438 Frankfurt, Germany.
| | | | - Jade Chaker
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
| | - Adrian Covaci
- Toxicological Center, University of Antwerp, 2610 Wilrijk, Belgium.
| | - Nicolas Creusot
- INRAE, French National Research Institute For Agriculture, Food & Environment, UR1454 EABX, Bordeaux Metabolome, MetaboHub, Gazinet Cestas, France.
| | - Arthur David
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, Rennes, France.
| | - Laurent Debrauwer
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France.
| | | | - Radu Corneliu Duca
- Unit Environmental Hygiene and Human Biological Monitoring, Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg; Environment and Health, Department of Public Health and Primary Care, Katholieke Universiteit of Leuven (KU Leuven), 3000 Leuven, Belgium.
| | - Valérie Fessard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory of Fougères, Toxicology of Contaminants Unit, 35306 Fougères, France.
| | - Joan O Grimalt
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Catalonia, Spain.
| | - Thierry Guerin
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Strategy and Programs Department, F-94701 Maisons-Alfort, France.
| | - Baninia Habchi
- INRS, Département Toxicologie et Biométrologie Laboratoire Biométrologie 1, rue du Morvan - CS 60027 - 54519, Vandoeuvre Cedex, France.
| | - Helge Hecht
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
| | - Juliane Hollender
- Swiss Federal Institute of Aquatic Science and Technology - Eawag, 8600 Dübendorf, Switzerland; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland.
| | - Emilien L Jamin
- Toxalim (Research Centre in Food Toxicology), INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University (UPS), Toulouse, France.
| | - Jana Klánová
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
| | - Tina Kosjek
- Jožef Stefan Institute, Department of Environmental Sciences, Ljubljana, Slovenia.
| | - Martin Krauss
- Helmholtz Centre for Environmental Research GmbH - UFZ, Department of Effect-Directed Analysis, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Marja Lamoree
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Gwenaelle Lavison-Bompard
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory for Food Safety, Pesticides and Marine Biotoxins Unit, F-94701 Maisons-Alfort, France.
| | - Jeroen Meijer
- Vrije Universiteit Amsterdam, Amsterdam Institute for Life and Environment (A-LIFE), Section Chemistry for Environment and Health, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
| | - Ruth Moeller
- Unit Medical Expertise and Data Intelligence, Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg.
| | - Hans Mol
- Wageningen Food Safety Research - Part of Wageningen University and Research, Akkermaalsbos 2, 6708 WB, Wageningen, The Netherlands.
| | - Sophie Mompelat
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory of Fougères, Toxicology of Contaminants Unit, 35306 Fougères, France.
| | - An Van Nieuwenhuyse
- Environment and Health, Department of Public Health and Primary Care, Katholieke Universiteit of Leuven (KU Leuven), 3000 Leuven, Belgium; Department of Health Protection, Laboratoire National de Santé (LNS), 1 Rue Louis Rech, L-3555 Dudelange, Luxembourg.
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Insbruck, 6020 Innsbruck, Austria.
| | - Julien Parinet
- ANSES, French Agency for Food, Environmental and Occupational Health & Safety, Laboratory for Food Safety, Pesticides and Marine Biotoxins Unit, F-94701 Maisons-Alfort, France.
| | - Christof Van Poucke
- Flanders Research Institute for Agriculture, Fisheries And Food (ILVO), Brusselsesteenweg 370, 9090 Melle, Belgium.
| | - Robert Roškar
- University of Ljubljana, Faculty of Pharmacy, Slovenia.
| | - Anne Togola
- BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France.
| | | | - Elliott J Price
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic.
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6
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Huang L, Aurisano N, Fantke P, Dissanayake A, Edirisinghe LGLM, Jolliet O. Near-field exposures and human health impacts for organic chemicals in interior paints: A high-throughput screening. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133145. [PMID: 38154180 DOI: 10.1016/j.jhazmat.2023.133145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 12/30/2023]
Abstract
Interior paints contain organic chemicals that might be harmful to painters and building residents. This study aims to develop a high-throughput approach to screen near-field human exposures and health impacts related to organic chemicals in interior paints. We developed mass balance models for both water- and solvent-based paints, predicting emissions during wet and dry phases. We then screened exposures and risks, focusing on Sri Lanka where residential houses are frequently repainted. These models accurately predict paint drying time and indoor air concentrations of organic chemicals. Exposures of both painter and household resident were estimated for 65 organic chemicals in water-based and 26 in solvent-based paints, considering 12 solvents. Chemicals of concerns (CoCs) were identified, and maximum acceptable chemical contents (MACs) were calculated. Water-based paints generally pose lower health risks than solvent-based paints but might contain biocides of high concern. The total human health impact of one painting event on all household adults ranges from 1.5 × 10-3 to 2.1 × 10-2 DALYs for solvent-based paints, and from 4.1 × 10-4 to 9.5 × 10-3 DALYs for water-based paints. The present approach is a promising way to support the formulation of safer paint, and is integrated in the USEtox scientific consensus model for use in life cycle assessment, chemical substitution and risk screening.
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Affiliation(s)
- Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark
| | | | | | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA; Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800 Kgs. Lyngby, Denmark.
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7
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Phillips KA, Chao A, Church RL, Favela K, Garantziotis S, Isaacs KK, Meyer B, Rice A, Sayre R, Wetmore BA, Yau A, Wambaugh JF. Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1802-1812. [PMID: 38217501 DOI: 10.1021/acs.est.3c05092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.
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Affiliation(s)
- Katherine A Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Alex Chao
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Rebecca L Church
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Kristin Favela
- Southwest Research Institute, San Antonio, Texas 78238, United States
| | - Stavros Garantziotis
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Brian Meyer
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Annette Rice
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Risa Sayre
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Barbara A Wetmore
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Alice Yau
- Southwest Research Institute, San Antonio, Texas 78238, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
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8
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Lin HC, Rusyn I, Chiu WA. Assessing proarrhythmic potential of environmental chemicals using a high throughput in vitro-in silico model with human induced pluripotent stem cell-derived cardiomyocytes. ALTEX 2024; 41:37-49. [PMID: 37921411 PMCID: PMC10898275 DOI: 10.14573/altex.2306231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/19/2023] [Indexed: 11/04/2023]
Abstract
QT prolongation and the potentially fatal arrhythmia Torsades de Pointes are common causes for withdrawing or restricting drugs; however, little is known about similar liabilities of environmental chemicals. Current in vitro-in silico models for testing proarrhythmic liabilities, using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM), provide an opportunity to address this data gap. These methods are still low- to medium-throughput and not suitable for testing the tens of thousands of chemicals in commerce. We hypothesized that combining high-throughput population- based in vitro testing in hiPSC-CMs with a fully in silico data analysis workflow can offer sensitive and specific predictions of proarrhythmic potential. We calibrated the model with a published hiPSC-CM dataset of drugs known to be positive or negative for proarrhythmia and tested its performance using internal cross-validation and external validation. Additionally, we used computational down-sampling to examine three study designs for hiPSC-CM data: one replicate of one donor, five replicates of one donor, and one replicate of a population of five donors. We found that the population of five donors had the best performance for predicting proarrhythmic potential. The resulting model was then applied to predict the proarrhythmic potential of environmental chemicals, additionally characterizing risk through margin of exposure (MOE) calculations. Out of over 900 environmental chemicals tested, over 150 were predicted to have proarrhythmic potential, but only seven chemicals had a MOE < 1. We conclude that a high-throughput in vitro-in silico approach using population-based hiPSC-CM testing provides a reasonable strategy to screen environmental chemicals for proarrhythmic potential.
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Affiliation(s)
- Hsing-Chieh Lin
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Ivan Rusyn
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - Weihsueh A. Chiu
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
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9
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Isaacs KK, Wall JT, Paul Friedman K, Franzosa JA, Goeden H, Williams AJ, Dionisio KL, Lambert JC, Linnenbrink M, Singh A, Wambaugh JF, Bogdan AR, Greene C. Screening for drinking water contaminants of concern using an automated exposure-focused workflow. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024; 34:136-147. [PMID: 37193773 DOI: 10.1038/s41370-023-00552-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The number of chemicals present in the environment exceeds the capacity of government bodies to characterize risk. Therefore, data-informed and reproducible processes are needed for identifying chemicals for further assessment. The Minnesota Department of Health (MDH), under its Contaminants of Emerging Concern (CEC) initiative, uses a standardized process to screen potential drinking water contaminants based on toxicity and exposure potential. OBJECTIVE Recently, MDH partnered with the U.S. Environmental Protection Agency (EPA) Office of Research and Development (ORD) to accelerate the screening process via development of an automated workflow accessing relevant exposure data, including exposure new approach methodologies (NAMs) from ORD's ExpoCast project. METHODS The workflow incorporated information from 27 data sources related to persistence and fate, release potential, water occurrence, and exposure potential, making use of ORD tools for harmonization of chemical names and identifiers. The workflow also incorporated data and criteria specific to Minnesota and MDH's regulatory authority. The collected data were used to score chemicals using quantitative algorithms developed by MDH. The workflow was applied to 1867 case study chemicals, including 82 chemicals that were previously manually evaluated by MDH. RESULTS Evaluation of the automated and manual results for these 82 chemicals indicated reasonable agreement between the scores although agreement depended on data availability; automated scores were lower than manual scores for chemicals with fewer available data. Case study chemicals with high exposure scores included disinfection by-products, pharmaceuticals, consumer product chemicals, per- and polyfluoroalkyl substances, pesticides, and metals. Scores were integrated with in vitro bioactivity data to assess the feasibility of using NAMs for further risk prioritization. SIGNIFICANCE This workflow will allow MDH to accelerate exposure screening and expand the number of chemicals examined, freeing resources for in-depth assessments. The workflow will be useful in screening large libraries of chemicals for candidates for the CEC program.
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Affiliation(s)
- Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA.
| | - Jonathan T Wall
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Jill A Franzosa
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Helen Goeden
- Minnesota Department of Health, 625 Robert St. N, St. Paul, MN, 55155, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Kathie L Dionisio
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Jason C Lambert
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Monica Linnenbrink
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Amar Singh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Alexander R Bogdan
- Minnesota Department of Health, 625 Robert St. N, St. Paul, MN, 55155, USA
| | - Christopher Greene
- Minnesota Department of Health, 625 Robert St. N, St. Paul, MN, 55155, USA
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Stanfield Z, Setzer RW, Hull V, Sayre RR, Isaacs KK, Wambaugh JF. Characterizing Chemical Exposure Trends from NHANES Urinary Biomonitoring Data. ENVIRONMENTAL HEALTH PERSPECTIVES 2024; 132:17009. [PMID: 38285237 PMCID: PMC10824265 DOI: 10.1289/ehp12188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/19/2023] [Accepted: 12/12/2023] [Indexed: 01/30/2024]
Abstract
BACKGROUND Xenobiotic metabolites are widely present in human urine and can indicate recent exposure to environmental chemicals. Proper inference of which chemicals contribute to these metabolites can inform human exposure and risk. Furthermore, longitudinal biomonitoring studies provide insight into how chemical exposures change over time. OBJECTIVES We constructed an exposure landscape for as many human-exposure relevant chemicals over as large a time span as possible to characterize exposure trends across demographic groups and chemical types. METHODS We analyzed urine data of nine 2-y cohorts (1999-2016) from the National Health and Nutrition Examination Survey (NHANES). Chemical daily intake rates (in milligrams per kilogram bodyweight per day) were inferred, using the R package bayesmarker, from metabolite concentrations in each cohort individually to identify exposure trends. Trends for metabolites and parents were clustered to find chemicals with similar exposure patterns. Exposure variation by age, gender, and body mass index were also assessed. RESULTS Intake rates for 179 parent chemicals were inferred from 151 metabolites (96 measured in five or more cohorts). Seventeen metabolites and 44 parent chemicals exhibited fold-changes ≥ 10 between any two cohorts (deltamethrin, di-n -octyl phthalate, and di-isononyl phthalate had the greatest exposure increases). Di-2-ethylhexyl phthalate intake began decreasing in 2007, whereas both di-isobutyl and di-isononyl phthalate began increasing shortly before. Intake for four parabens was markedly higher in females, especially reproductive-age females, compared with males and children. Cadmium and arsenobetaine exhibited higher exposure for individuals > 65 years of age and lower for individuals < 20 years of age. DISCUSSION With appropriate analysis, NHANES indicates trends in chemical exposures over the past two decades. Decreases in exposure are observable as the result of regulatory action, with some being accompanied by increases in replacement chemicals. Age- and gender-specific variations in exposure were observed for multiple chemicals. Continued estimation of demographic-specific exposures is needed to both monitor and identify potential vulnerable populations. https://doi.org/10.1289/EHP12188.
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Affiliation(s)
- Zachary Stanfield
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - R. Woodrow Setzer
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Victoria Hull
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Associated Universities, Oak Ridge, Tennessee, USA
| | - Risa R. Sayre
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Associated Universities, Oak Ridge, Tennessee, USA
| | - Kristin K. Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - John F. Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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11
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Zhang Z, Sangion A, Wang S, Gouin T, Brown T, Arnot JA, Li L. Hazard vs. exposure: Does it make a difference in identifying chemicals with persistence and mobility concerns? WATER RESEARCH 2023; 245:120610. [PMID: 37717328 DOI: 10.1016/j.watres.2023.120610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/30/2023] [Accepted: 09/09/2023] [Indexed: 09/19/2023]
Abstract
Persistent and mobile (PM) chemicals are considered emerging threats to the environment and drinking water because they can be transported over long distances, penetrate natural and artificial barriers, and resist removal by traditional water treatment procedures. Current chemical regulatory frameworks raise concerns over PM chemicals due to their potential to cause high human exposure through drinking water contamination. However, the criteria used to screen and identify these chemicals often rely on hazard properties related to stability and sorption, such as biodegradation half-lives and organic-carbon-normalized sorption coefficients as respective measures of P and M. Here, we conduct a model-based assessment to examine the consistency between hazard-based and exposure-based approaches in assessing PM chemicals, by evaluating whether chemicals identified as highly P and M are consistently associated with high drinking water exposure potential (DWEP). We discover that chemicals with the top DWEPs tend to be PM chemicals, but the reverse is not always true, because DWEPs are also impacted by volatilization for air-distributed chemicals and advective particle-bound transport for particle-bound chemicals. Our findings suggest that the hazard metrics are better suited for de-prioritizing, as opposed to prioritizing, chemicals that are unlikely to result in significant human exposure through drinking water, as unfavorable values of hazard metrics are a necessary but not sufficient condition for a high DWEP. We also find that distinct mechanisms determine the DWEP in different sources of drinking water: Sorption and stability are more influential on the DWEP of chemicals in groundwater and surface water, respectively, whereas both sorption and stability equally impact water undergoing riverbank filtration. Future studies should focus on optimizing the identification of persistent and mobile chemicals to ensure that exposure potential is taken into consideration.
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Affiliation(s)
- Zhizhen Zhang
- School of Public Health, University of Nevada, Reno, 1664, N. Virginia Street, Reno, Nevada 89557-274, United States
| | | | - Shenghong Wang
- School of Public Health, University of Nevada, Reno, 1664, N. Virginia Street, Reno, Nevada 89557-274, United States
| | - Todd Gouin
- TG Environmental Research, Sharnbrook, Bedford MK44 1PL, United Kingdom
| | - Trevor Brown
- ARC Arnot Research & Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Jon A Arnot
- ARC Arnot Research & Consulting, Toronto, Ontario M4M 1W4, Canada; Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Li Li
- School of Public Health, University of Nevada, Reno, 1664, N. Virginia Street, Reno, Nevada 89557-274, United States.
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Martens M, Stierum R, Schymanski EL, Evelo CT, Aalizadeh R, Aladjov H, Arturi K, Audouze K, Babica P, Berka K, Bessems J, Blaha L, Bolton EE, Cases M, Damalas DΕ, Dave K, Dilger M, Exner T, Geerke DP, Grafström R, Gray A, Hancock JM, Hollert H, Jeliazkova N, Jennen D, Jourdan F, Kahlem P, Klanova J, Kleinjans J, Kondic T, Kone B, Lynch I, Maran U, Martinez Cuesta S, Ménager H, Neumann S, Nymark P, Oberacher H, Ramirez N, Remy S, Rocca-Serra P, Salek RM, Sallach B, Sansone SA, Sanz F, Sarimveis H, Sarntivijai S, Schulze T, Slobodnik J, Spjuth O, Tedds J, Thomaidis N, Weber RJ, van Westen GJ, Wheelock CE, Williams AJ, Witters H, Zdrazil B, Županič A, Willighagen EL. ELIXIR and Toxicology: a community in development. F1000Res 2023; 10:ELIXIR-1129. [PMID: 37842337 PMCID: PMC10568213 DOI: 10.12688/f1000research.74502.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Rob Stierum
- Risk Analysis for Products In Development (RAPID), Netherlands Organisation for applied scientific research TNO, Utrecht, 3584 CB, The Netherlands
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6229 EN, The Netherlands
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Hristo Aladjov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, 1113, Bulgaria
| | - Kasia Arturi
- Department Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600, Switzerland
| | | | - Pavel Babica
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Karel Berka
- Department of Physical Chemistry, Palacky University Olomouc, Olomouc, 77146, Czech Republic
| | | | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Dimitrios Ε. Damalas
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Kirtan Dave
- School of Science, GSFC University, Gujarat, 391750, India
| | - Marco Dilger
- Forschungs- und Beratungsinstitut Gefahrstoffe (FoBiG) GmbH, Freiburg im Breisgau, 79106, Germany
| | | | - Daan P. Geerke
- AIMMS Division of Molecular Toxicology, Vrije Universiteit, Amsterdam, 1081 HZ, The Netherlands
| | - Roland Grafström
- Department of Toxicology, Misvik Biology, Turku, 20520, Finland
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Alasdair Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, UK
| | | | - Henner Hollert
- Department Evolutionary Ecology & Environmental Toxicology (E3T), Goethe-University, Frankfurt, D-60438, Germany
| | | | - Danyel Jennen
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Fabien Jourdan
- MetaboHUB, French metabolomics infrastructure in Metabolomics and Fluxomics, Toulouse, France
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Jana Klanova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jos Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Todor Kondic
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Boï Kone
- Faculty of Pharmacy, Malaria Research and Training Center, Bamako, BP:1805, Mali
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, 50411, Estonia
| | | | - Hervé Ménager
- Institut Français de Bioinformatique, Evry, F-91000, France
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Paris, F-75015, France
| | - Steffen Neumann
- Research group Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle, 06120, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Innsbruck, A-6020, Austria
| | - Noelia Ramirez
- Institut d'Investigacio Sanitaria Pere Virgili-Universitat Rovira i Virgili, Tarragona, 43007, Spain
| | | | - Philippe Rocca-Serra
- Data Readiness Group, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Reza M. Salek
- International Agency for Research on Cancer, World Health Organisation, Lyon, 69372, France
| | - Brett Sallach
- Department of Environment and Geography, University of York, UK, York, YO10 5NG, UK
| | | | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, 08003, Spain
| | | | | | - Tobias Schulze
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, 04318, Germany
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, SE-75124, Sweden
| | - Jonathan Tedds
- ELIXIR Hub, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Nikolaos Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Ralf J.M. Weber
- School of Biosciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Gerard J.P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research, Leiden, 2333 CC, The Netherlands
| | - Craig E. Wheelock
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm SE-141-86, Sweden
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 17177, Sweden
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | | | - Barbara Zdrazil
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, 1090, Austria
| | - Anže Županič
- Department Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
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Buckley TJ, Egeghy PP, Isaacs K, Richard AM, Ring C, Sayre RR, Sobus JR, Thomas RS, Ulrich EM, Wambaugh JF, Williams AJ. Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency. ENVIRONMENT INTERNATIONAL 2023; 178:108097. [PMID: 37478680 PMCID: PMC10588682 DOI: 10.1016/j.envint.2023.108097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.
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Affiliation(s)
- Timothy J Buckley
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States.
| | - Peter P Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Kristin Isaacs
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Caroline Ring
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Risa R Sayre
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Russell S Thomas
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
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Sayre RR, Setzer RW, Serre ML, Wambaugh JF. Characterizing surface water concentrations of hundreds of organic chemicals in United States for environmental risk prioritization. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:610-619. [PMID: 36446910 PMCID: PMC10619030 DOI: 10.1038/s41370-022-00501-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Thousands of chemicals are observed in freshwater, typically at trace levels. Measurements are collected for different purposes, so sample characteristics vary. Due to inconsistent data availability for exposure and hazard, it is complex to prioritize which chemicals may pose risks. OBJECTIVE We evaluated the influence of data curation and statistical practices aggregating surface water measurements of organic chemicals into exposure distributions intended for prioritizing based on nation-scale potential risk. METHODS The Water Quality Portal includes millions of observations describing over 1700 chemicals in 93% of hydrologic subbasins across the United States. After filtering to maintain quality and applicability while including all possible samples, we compared concentrations across sample types. We evaluated statistical methods to estimate per-chemical distributions for chosen samples. Overlaps between resulting exposure ranges and distributions representing no-effect concentrations for multiple freshwater species were used to rank estimated chemical risks for further assessment. RESULTS When we apply explicit data quality and statistical assumptions, we find that there are 186 organic chemicals for which we can make screening-level estimates of surface water chemical concentration. Of the original 1700 observed chemicals, this number decreased primarily due to a predominance of censored values (that is, observations indicating concentrations too low to be measured). We further identify 423 chemicals where all measurements were censored but, through consideration of detection limits, risk might still be prioritized based on the detection limits themselves. In the final set of 1.5 million samples, the median environmental concentration of one chemical (acetic acid) exceeded the 5th percentile of no-effect concentrations for the most delicate freshwater species (the highest priority risk condition identified here), and a further 29 chemicals were identified for possible further evaluation based on a small margin between occurrence and toxicity values. SIGNIFICANCE This method shows the broad range of chemical concentrations seen for organic chemicals across the country and identifies methods of determining their central tendency, allowing for researchers to characterize higher-than-normal or lower-than-normal surface water conditions as well as providing an overall indication of the presence of organic chemicals in the United States. The highest chemical concentrations did not always indicate the highest-risk conditions. Even when accounting for the high level of uncertainty in these data due to differences in data collection and reporting across the set, some chemicals may still be categorized as higher environmental risk than others using this method, providing value to chemical safety decision makers and researchers by suggesting avenues for more focused investigation.
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Affiliation(s)
- Risa R Sayre
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC, 27709, USA.
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, 135 Dauer Dr, Chapel Hill, NC, 27599, USA.
| | - R Woodrow Setzer
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC, 27709, USA
| | - Marc L Serre
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, 135 Dauer Dr, Chapel Hill, NC, 27599, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC, 27709, USA
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, 135 Dauer Dr, Chapel Hill, NC, 27599, USA
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Ateia M, Buren JV, Barrett W, Martin T, Back GG. Sunrise of PFAS Replacements: A Perspective on Fluorine-Free Foams. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023; 11:7986-7996. [PMID: 37476647 PMCID: PMC10354943 DOI: 10.1021/acssuschemeng.3c01124] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
One type of firefighting foam, referred to as aqueous filmforming foams (AFFF), is known to contain per- and polyfluoroalkyl substances (PFAS). The concerns raised with PFAS, and their potential environmental and health impacts, have led to a surge in research on fluorine-free alternatives both in the United States and globally. Particularly, in January 2023, a new military specification (MIL-PRF-32725) for fluorine-free foam was released in accordance with Congressional requirements for the U.S. Department of Defense. This paper provides a critical analysis of the present state of the various fluorine-free options that have been developed to date. A nuanced perspective of the challenges and opportunities of more sustainable replacements is explored by examining the performance, cost, and regulatory considerations associated with these fluorine-free alternatives. Ultimately, this evaluation shows that the transition to fluorine-free replacements is likely to be complex and multifaceted, requiring careful consideration of the trade-offs involved. Yet, the ongoing work will provide valuable insights for future research on alternatives to AFFF and enhancing the safety and sustainability of fire suppression systems.
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Affiliation(s)
- Mohamed Ateia
- Center for Environmental Solutions & Emergency Response, U.S. Environmental Protection Agency, Cincinnati, Ohio 45204, United States; Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas 77005, United States
| | - Jean Van Buren
- Center for Environmental Solutions & Emergency Response, U.S. Environmental Protection Agency, Cincinnati, Ohio 45204, United States
| | - William Barrett
- Center for Environmental Solutions & Emergency Response, U.S. Environmental Protection Agency, Cincinnati, Ohio 45204, United States
| | - Todd Martin
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio 45204, United States
| | - Gerard G Back
- Jensen Hughes, Inc., Halethorpe, Maryland 21227, United States
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16
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Minucci JM, Purucker ST, Isaacs KK, Wambaugh JF, Phillips KA. A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:5947-5956. [PMID: 36995295 PMCID: PMC10100548 DOI: 10.1021/acs.est.2c08234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 06/19/2023]
Abstract
A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m-3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA's Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.
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Affiliation(s)
- Jeffrey M. Minucci
- Center
for Public Health and Environmental Assessment, Office of Research
and Development, US Environmental Protection
Agency, 109 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - S. Thomas Purucker
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
| | - Kristin K. Isaacs
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
| | - John F. Wambaugh
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
| | - Katherine A. Phillips
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
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17
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Nyffeler J, Willis C, Harris FR, Foster MJ, Chambers B, Culbreth M, Brockway RE, Davidson-Fritz S, Dawson D, Shah I, Friedman KP, Chang D, Everett LJ, Wambaugh JF, Patlewicz G, Harrill JA. Application of cell painting for chemical hazard evaluation in support of screening-level chemical assessments. Toxicol Appl Pharmacol 2023; 468:116513. [PMID: 37044265 DOI: 10.1016/j.taap.2023.116513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/03/2023] [Accepted: 04/08/2023] [Indexed: 04/14/2023]
Abstract
'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ~100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.
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Affiliation(s)
- Jo Nyffeler
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, TN 37831, United States of America
| | - Clinton Willis
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Felix R Harris
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - M J Foster
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - Bryant Chambers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Megan Culbreth
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard E Brockway
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - Sarah Davidson-Fritz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Daniel Dawson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Katie Paul Friedman
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Dan Chang
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Logan J Everett
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - John F Wambaugh
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Grace Patlewicz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Joshua A Harrill
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
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18
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Hernandez-Betancur JD, Ruiz-Mercado GJ, Martin M. Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023; 11:3594-3602. [PMID: 36911873 PMCID: PMC9993395 DOI: 10.1021/acssuschemeng.2c05662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.
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Affiliation(s)
| | - Gerardo J. Ruiz-Mercado
- Office
of Research & Development, US Environmental
Protection Agency, Cincinnati, Ohio 45268, United States
- Chemical
Engineering Graduate Program, Universidad
del Atlántico, Puerto Colombia 080007, Colombia
| | - Mariano Martin
- Department
of Chemical Engineering, University of Salamanca, Salamanca 37008, Spain
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19
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Zhao F, Li L, Lin P, Chen Y, Xing S, Du H, Wang Z, Yang J, Huan T, Long C, Zhang L, Wang B, Fang M. HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:37009. [PMID: 36913238 PMCID: PMC10010393 DOI: 10.1289/ehp11305] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 12/15/2022] [Accepted: 02/14/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans. OBJECTIVES Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health concern. METHODS We curated the CBs of compounds mostly measured at population levels and developed an ML model for chemical CB predictions by considering chemical daily exposure (DE) and exposure pathway indicators (δij), half-lives (t1/2), and volume of distribution (Vd). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted CB and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances. RESULTS We curated the CBs of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and 2.07μM, the mean absolute error (MAE) values of 1.28 and 1.56μM, the mean absolute percentage error (MAPE) of 0.29 and 0.23, and R2 of 0.80 and 0.72 across test and testing sets. Subsequently, the human CBs of 7,858 ToxCast chemicals were successfully predicted, ranging from 1.29×10-6 to 1.79×10-2 μM. The predicted CBs were then combined with ToxCast in vitro bioassays to prioritize the ToxCast chemicals across 12 in vitro assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants. DISCUSSION We have shown that the accurate prediction of "internal exposure" from "external exposure" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.
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Affiliation(s)
- Fanrong Zhao
- Department of Environmental Science and Engineering, Fudan University, Shanghai, P.R. China
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Li Li
- School of Community Health Sciences, University of Nevada, Reno, Reno, Nevada, USA
| | - Penghui Lin
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Yue Chen
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Huili Du
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, P.R. China
| | - Zheng Wang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Junjie Yang
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Tao Huan
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cheng Long
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Limao Zhang
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
| | - Bin Wang
- Institute of Reproductive and Child Health, Peking University/Key Laboratory of Reproductive Health, National Health Commission of the People’s Republic of China, Beijing, P.R. China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, P.R. China
| | - Mingliang Fang
- Department of Environmental Science and Engineering, Fudan University, Shanghai, P.R. China
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
- Institute of Eco-Chongming, Shanghai, P.R. China
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20
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Aurisano N, Jolliet O, Chiu WA, Judson R, Jang S, Unnikrishnan A, Kosnik MB, Fantke P. Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:37016. [PMID: 36989077 PMCID: PMC10056221 DOI: 10.1289/ehp11524] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 02/06/2023] [Accepted: 03/03/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals. OBJECTIVES Using in vivo experimental animal data from the U.S. Environmental Protection Agency's Toxicity Value Database, we developed a semiautomated approach to determine surrogate oral route PODs, and corresponding toxicity values where regulatory assessments are unavailable. METHODS We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses (BMDh). We hypothesized that a quantile of the BMDh distribution could serve as a surrogate POD and determined the appropriate quantile by calibration to regulatory PODs. Finally, we characterized uncertainties around the surrogate PODs from intra- and interstudy variability and derived probabilistic toxicity values using a standardized workflow. RESULTS The BMDh distribution for each chemical was adequately fit by a lognormal distribution, and the 25th percentile best predicted the available regulatory PODs [R2≥0.78, residual standard error (RSE)≤0.53 log10 units]. We derived surrogate PODs for 10,145 chemicals from the curated data set, differentiating between general noncancer and reproductive/developmental effects, with typical uncertainties (at 95% confidence) of a factor of 10 and 12, respectively. From these PODs, probabilistic reference doses (1% incidence at 95% confidence), as well as human population effect doses (10% incidence), were derived. DISCUSSION In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.
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Affiliation(s)
- Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Olivier Jolliet
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Richard Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Suji Jang
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Aswani Unnikrishnan
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Marissa B. Kosnik
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
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21
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Price PS. The Hazard Index at thirty-seven: new science new insights. CURRENT OPINION IN TOXICOLOGY 2023. [DOI: 10.1016/j.cotox.2023.100388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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22
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Beal MA, Audebert M, Barton-Maclaren T, Battaion H, Bemis JC, Cao X, Chen C, Dertinger SD, Froetschl R, Guo X, Johnson G, Hendriks G, Khoury L, Long AS, Pfuhler S, Settivari RS, Wickramasuriya S, White P. Quantitative in vitro to in vivo extrapolation of genotoxicity data provides protective estimates of in vivo dose. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2023; 64:105-122. [PMID: 36495195 DOI: 10.1002/em.22521] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Genotoxicity assessment is a critical component in the development and evaluation of chemicals. Traditional genotoxicity assays (i.e., mutagenicity, clastogenicity, and aneugenicity) have been limited to dichotomous hazard classification, while other toxicity endpoints are assessed through quantitative determination of points-of-departures (PODs) for setting exposure limits. The more recent higher-throughput in vitro genotoxicity assays, many of which also provide mechanistic information, offer a powerful approach for determining defined PODs for potency ranking and risk assessment. In order to obtain relevant human dose context from the in vitro assays, in vitro to in vivo extrapolation (IVIVE) models are required to determine what dose would elicit a concentration in the body demonstrated to be genotoxic using in vitro assays. Previous work has demonstrated that application of IVIVE models to in vitro bioactivity data can provide PODs that are protective of human health, but there has been no evaluation of how these models perform with in vitro genotoxicity data. Thus, the Genetic Toxicology Technical Committee, under the Health and Environmental Sciences Institute, conducted a case study on 31 reference chemicals to evaluate the performance of IVIVE application to genotoxicity data. The results demonstrate that for most chemicals considered here (20/31), the PODs derived from in vitro data and IVIVE are health protective relative to in vivo PODs from animal studies. PODs were also protective by assay target: mutations (8/13 chemicals), micronuclei (9/12), and aneugenicity markers (4/4). It is envisioned that this novel testing strategy could enhance prioritization, rapid screening, and risk assessment of genotoxic chemicals.
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Affiliation(s)
- Marc A Beal
- Bureau of Chemical Safety, Health Products and Food Branch, Health Canada, Ottawa, Ontario, Canada
| | - Marc Audebert
- Toxalim UMR1331, Toulouse University, INRAE, Toulouse, France
| | - Tara Barton-Maclaren
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Hannah Battaion
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | | | - Xuefei Cao
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Connie Chen
- Health and Environmental Sciences Institute, Washington, District of Columbia, USA
| | | | | | - Xiaoqing Guo
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | | | | | - Alexandra S Long
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Stefan Pfuhler
- Global Product Stewardship, Procter & Gamble, Cincinnati, Ohio, USA
| | - Raja S Settivari
- Mammalian Toxicology Center, Corteva Agriscience, Newark, Delaware, USA
| | - Shamika Wickramasuriya
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Paul White
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
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23
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Eccles KM, Karmaus AL, Kleinstreuer NC, Parham F, Rider CV, Wambaugh JF, Messier KP. A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158905. [PMID: 36152849 PMCID: PMC9979101 DOI: 10.1016/j.scitotenv.2022.158905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/09/2022] [Accepted: 09/17/2022] [Indexed: 05/14/2023]
Abstract
In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.
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Affiliation(s)
- Kristin M Eccles
- National Institute of Environmental Health Science, Division of the Translational Toxicology, Durham, USA
| | - Agnes L Karmaus
- Integrated Laboratory Systems, an Inotiv Company, Morrisville, NC, USA
| | - Nicole C Kleinstreuer
- National Institute of Environmental Health Science, Division of the Translational Toxicology, Durham, USA
| | - Fred Parham
- National Institute of Environmental Health Science, Division of the Translational Toxicology, Durham, USA
| | - Cynthia V Rider
- National Institute of Environmental Health Science, Division of the Translational Toxicology, Durham, USA
| | - John F Wambaugh
- United States Environmental Protection Agency, Center for Computational Toxicology and Exposure, Durham, USA
| | - Kyle P Messier
- National Institute of Environmental Health Science, Division of the Translational Toxicology, Durham, USA.
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24
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Braun G, Escher BI. Prioritization of mixtures of neurotoxic chemicals for biomonitoring using high-throughput toxicokinetics and mixture toxicity modeling. ENVIRONMENT INTERNATIONAL 2023; 171:107680. [PMID: 36502700 DOI: 10.1016/j.envint.2022.107680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Modern society continues to pollute the environment with larger quantities of chemicals that have also become more structurally and functionally diverse. Risk assessment of chemicals can hardly keep up with the sheer numbers that lead to complex mixtures of increasing chemical diversity including new chemicals, substitution products on top of still abundant legacy compounds. Fortunately, over the last years computational tools have helped us to identify and prioritize chemicals of concern. These include toxicokinetic models to predict exposure to chemicals as well as new approach methodologies such as in-vitro bioassays to address toxicodynamic effects. Combined, they allow for a prediction of mixtures and their respective effects and help overcome the lack of data we face for many chemicals. In this study we propose a high-throughput approach using experimental and predicted exposure, toxicokinetic and toxicodynamic data to simulate mixtures, to which a virtual population is exposed to and predict their mixture effects. The general workflow is adaptable for any type of toxicity, but we demonstrated its applicability with a case study on neurotoxicity. If no experimental data for neurotoxicity were available, we used baseline toxicity predictions as a surrogate. Baseline toxicity is the minimal toxicity any chemical has and might underestimate the true contribution to the mixture effect but many neurotoxicants are not by orders of magnitude more potent than baseline toxicity. Therefore, including baseline-toxic effects in mixture simulations yields a more realistic picture than excluding them in mixture simulations. This workflow did not only correctly identify and prioritize known chemicals of concern like benzothiazoles, organochlorine pesticides and plasticizers but we were also able to identify new potential neurotoxicants that we recommend to include in future biomonitoring studies and if found in humans, to also include in neurotoxicity screening.
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Affiliation(s)
- Georg Braun
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
| | - Beate I Escher
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany; Environmental Toxicology, Department of Geosciences, Eberhard Karls University Tübingen, Tübingen, Germany
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Chepelev N, Long AS, Beal M, Barton‐Maclaren T, Johnson G, Dearfield KL, Roberts DJ, van Benthem J, White P. Establishing a quantitative framework for regulatory interpretation of genetic toxicity dose-response data: Margin of exposure case study of 48 compounds with both in vivo mutagenicity and carcinogenicity dose-response data. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2023; 64:4-15. [PMID: 36345771 PMCID: PMC10107494 DOI: 10.1002/em.22517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/28/2022] [Accepted: 11/01/2022] [Indexed: 05/03/2023]
Abstract
Quantitative relationships between carcinogenic potency and mutagenic potency have been previously examined using a benchmark dose (BMD)-based approach. We extended those analyses by using human exposure data for 48 compounds to calculate carcinogenicity-derived and genotoxicity-derived margin of exposure values (MOEs) that can be used to prioritize substances for risk management. MOEs for 16 of the 48 compounds were below 10,000, and consequently highlighted for regulatory concern. Of these, 15 were highlighted using genotoxicity-derived (micronucleus [MN] dose-response data) MOEs. A total of 13 compounds were highlighted using carcinogenicity-derived MOEs; 12 compounds were overlapping. MOEs were also calculated using transgenic rodent (TGR) mutagenicity data. For 10 of the 12 compounds examined using TGR data, the results similarly revealed that mutagenicity-derived MOEs yield regulatory decisions that correspond with those based on carcinogenicity-derived MOEs. The effect of benchmark response (BMR) on MOE determination was also examined. Reinterpretation of the analyses using a BMR of 50% indicated that four out of 15 compounds prioritized using MN-derived MOEs based on a default BMR of 5% would have been missed. The results indicate that regulatory decisions based on in vivo genotoxicity dose-response data would be consistent with those based on carcinogenicity dose-response data; in some cases, genotoxicity-based decisions would be more conservative. Going forward, and in the absence of carcinogenicity data, in vivo genotoxicity assays (MN and TGR) can be used to effectively prioritize substances for regulatory action. Routine use of the MOE approach necessitates the availability of reliable human exposure estimates, and consensus regarding appropriate BMRs for genotoxicity endpoints.
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Affiliation(s)
- Nikolai Chepelev
- Environmental Health Science and Research BureauHealth CanadaOttawaOntarioCanada
| | - Alexandra S. Long
- Department of Pharmacology and ToxicologyUniversity of TorontoTorontoOntarioCanada
| | - Marc Beal
- Existing Substances Risk Assessment BureauHealth CanadaOttawaOntarioCanada
| | | | - George Johnson
- Swansea University Medical SchoolSwansea UniversitySwanseaUK
| | | | | | - Jan van Benthem
- National Institute for Public Health and the Environment (RIVM)BilthovenThe Netherlands
| | - Paul White
- Environmental Health Science and Research BureauHealth CanadaOttawaOntarioCanada
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Berthiaume A, Arnot JA, Toose L. Risk-based prioritization of organic substances in the Canadian National Pollutant Release Inventory using an evaluative regional-scale multimedia mass balance model. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2022; 18:1722-1732. [PMID: 35238162 PMCID: PMC9790719 DOI: 10.1002/ieam.4601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/01/2022] [Accepted: 03/01/2022] [Indexed: 05/21/2023]
Abstract
The National Pollutant Release Inventory (NPRI) is a Canadian inventory of facility-reported data on releases, transfers, and disposals of over 300 pollutants, but it does not contain information on chemical properties or other characteristics critical to understanding environmental and human health risks. To reconcile this gap, we use the Risk Assessment IDentification And Ranking (RAIDAR) model to integrate NPRI release data with chemical property information in a multimedia mass balance model to combine exposure estimates with toxicity hazard data yielding an estimate of risk for 198 NPRI organic substances reported in 2010-2019. The presented case study further corroborates the hypothesis that risk-based ranking gives rise to different chemical priorities versus ranking based on release quantity alone. Chemicals like propane and hexane (except n-hexane) are in the top 10 highest-ranked organic substances based on emission quantities reported to NPRI but are ranked outside the top 10 based on corresponding regional-scale risk estimates. On the contrary, dioxins and furans are ranked very low based on emissions quantities reported to NPRI but are ranked higher based on corresponding risk estimates. The results also suggest that although quantities of some NPRI organic pollutant releases change over time, the ensuing risk estimates are not always directly proportional to these changes. This can be explained by changes in mode of entry to the environment that can influence the overall fate and exposure of the same chemicals, highlighting the complex dynamics that can occur when simulating fate and risk as opposed to quantity alone. Limitations are discussed and recommendations are provided for improving the priority setting methods, including reducing the uncertainty of the NPRI data and the need for multimedia models to address point source emissions. Integr Environ Assess Manag 2022;18:1722-1732. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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Affiliation(s)
- Alicia Berthiaume
- Science and Risk Assessment DirectorateEnvironment and Climate Change CanadaGatineauQuebecCanada
| | - Jon A. Arnot
- ARC Arnot Research and Consulting Inc.TorontoOntarioCanada
- Department of Physical and Environmental SciencesUniversity of Toronto ScarboroughTorontoOntarioCanada
- Department of Pharmacology and ToxicologyUniversity of TorontoTorontoOntarioCanada
| | - Liisa Toose
- ARC Arnot Research and Consulting Inc.TorontoOntarioCanada
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Stanfield Z, Setzer RW, Hull V, Sayre RR, Isaacs KK, Wambaugh JF. Bayesian inference of chemical exposures from NHANES urine biomonitoring data. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:833-846. [PMID: 35978002 PMCID: PMC9979158 DOI: 10.1038/s41370-022-00459-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 07/05/2022] [Accepted: 07/12/2022] [Indexed: 05/25/2023]
Abstract
BACKGROUND Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data. OBJECTIVE Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty. METHODS Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts. RESULTS Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015-2016 NHANES cohort. SIGNIFICANCE The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.
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Affiliation(s)
- Zachary Stanfield
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - R Woodrow Setzer
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Victoria Hull
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
- Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37830, USA
| | - Risa R Sayre
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
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Sabbioni G, Castaño A, Esteban López M, Göen T, Mol H, Riou M, Tagne-Fotso R. Literature review and evaluation of biomarkers, matrices and analytical methods for chemicals selected in the research program Human Biomonitoring for the European Union (HBM4EU). ENVIRONMENT INTERNATIONAL 2022; 169:107458. [PMID: 36179646 DOI: 10.1016/j.envint.2022.107458] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
Humans are potentially exposed to a large amount of chemicals present in the environment and in the workplace. In the European Human Biomonitoring initiative (Human Biomonitoring for the European Union = HBM4EU), acrylamide, mycotoxins (aflatoxin B1, deoxynivalenol, fumonisin B1), diisocyanates (4,4'-methylenediphenyl diisocyanate, 2,4- and 2,6-toluene diisocyanate), and pyrethroids were included among the prioritized chemicals of concern for human health. For the present literature review, the analytical methods used in worldwide biomonitoring studies for these compounds were collected and presented in comprehensive tables, including the following parameter: determined biomarker, matrix, sample amount, work-up procedure, available laboratory quality assurance and quality assessment information, analytical techniques, and limit of detection. Based on the data presented in these tables, the most suitable methods were recommended. According to the paradigm of biomonitoring, the information about two different biomarkers of exposure was evaluated: a) internal dose = parent compounds and metabolites in urine and blood; and b) the biologically effective = dose measured as blood protein adducts. Urine was the preferred matrix used for deoxynivalenol, fumonisin B1, and pyrethroids (biomarkers of internal dose). Markers of the biological effective dose were determined as hemoglobin adducts for diisocyanates and acrylamide, and as serum-albumin-adducts of aflatoxin B1 and diisocyanates. The analyses and quantitation of the protein adducts in blood or the metabolites in urine were mostly performed with LC-MS/MS or GC-MS in the presence of isotope-labeled internal standards. This review also addresses the critical aspects of the application, use and selection of biomarkers. For future biomonitoring studies, a more comprehensive approach is discussed to broaden the selection of compounds.
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Affiliation(s)
- Gabriele Sabbioni
- Università della Svizzera Italiana (USI), Research and Transfer Service, Lugano, Switzerland; Institute of Environmental and Occupational Toxicology, Airolo, Switzerland; Walther-Straub-Institute for Pharmacology and Toxicology, Ludwig-Maximilians-University Munich, Munich, Germany.
| | - Argelia Castaño
- National Centre for Environmental Health, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain.
| | - Marta Esteban López
- National Centre for Environmental Health, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain.
| | - Thomas Göen
- Institute and Outpatient Clinic of Occupational, Social and Environmental Medicine, Friedrich-Alexander Universität Erlangen-Nürnberg (IPASUM), Erlangen, Germany.
| | - Hans Mol
- Wageningen Food Safety Research, Part of Wageningen University & Research, Wageningen, the Netherlands.
| | - Margaux Riou
- Department of Environmental and Occupational Health, Santé publique France, The National Public Health Agency, Saint-Maurice, France.
| | - Romuald Tagne-Fotso
- Department of Environmental and Occupational Health, Santé publique France, The National Public Health Agency, Saint-Maurice, France.
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Koval LE, Dionisio KL, Friedman KP, Isaacs KK, Rager JE. Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:794-807. [PMID: 35710593 DOI: 10.15139/s3/umpckw] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 05/28/2023]
Abstract
BACKGROUND Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking. OBJECTIVE This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk. METHODS The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources. RESULTS A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators. SIGNIFICANCE Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk. IMPACT STATEMENT Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.
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Affiliation(s)
- Lauren E Koval
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathie L Dionisio
- Immediate Office of the Assistant Administrator, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
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Arnot JA, Toose L, Armitage JM, Sangion A, Looky A, Brown TN, Li L, Becker RA. Developing an internal threshold of toxicological concern (iTTC). JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:877-884. [PMID: 36347933 PMCID: PMC9731903 DOI: 10.1038/s41370-022-00494-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Threshold of Toxicological Concern (TTC) approaches are used for chemical safety assessment and risk-based priority setting for data poor chemicals. TTCs are derived from in vivo No Observed Effect Level (NOEL) datasets involving an external administered dose from a single exposure route, e.g., oral intake rate. Thus, a route-specific TTC can only be compared to a route-specific exposure estimate and such TTCs cannot be used for other exposure scenarios such as aggregate exposures. OBJECTIVE Develop and apply a method for deriving internal TTCs (iTTCs) that can be used in chemical assessments for multiple route-specific exposures (e.g., oral, inhalation or dermal) or aggregate exposures. METHODS Chemical-specific toxicokinetics (TK) data and models are applied to calculate internal concentrations (whole-body and blood) from the reported administered oral dose NOELs used to derive the Munro TTCs. The new iTTCs are calculated from the 5th percentile of cumulative distributions of internal NOELs and the commonly applied uncertainty factor of 100 to extrapolate animal testing data for applications in human health assessment. RESULTS The new iTTCs for whole-body and blood are 0.5 nmol/kg and 0.1 nmol/L, respectively. Because the iTTCs are expressed on a molar basis they are readily converted to chemical mass iTTCs using the molar mass of the chemical of interest. For example, the median molar mass in the dataset is 220 g/mol corresponding to an iTTC of 22 ng/L-blood (22 pg/mL-blood). The iTTCs are considered broadly applicable for many organic chemicals except those that are genotoxic or acetylcholinesterase inhibitors. The new iTTCs can be compared with measured or estimated whole-body or blood exposure concentrations for chemical safety screening and priority-setting. SIGNIFICANCE Existing Threshold of Toxicological Concern (TTC) approaches are limited in their applications for route-specific exposure scenarios only and are not suitable for chemical risk and safety assessments under conditions of aggregate exposure. New internal Threshold of Toxicological Concern (iTTC) values are developed to address data gaps in chemical safety estimation for multi-route and aggregate exposures.
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Affiliation(s)
- Jon A Arnot
- ARC Arnot Research and Consulting Inc., Toronto, ON, Canada.
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada.
| | - Liisa Toose
- ARC Arnot Research and Consulting Inc., Toronto, ON, Canada
| | | | - Alessandro Sangion
- ARC Arnot Research and Consulting Inc., Toronto, ON, Canada
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, ON, Canada
| | | | - Trevor N Brown
- ARC Arnot Research and Consulting Inc., Toronto, ON, Canada
| | - Li Li
- School of Public Health, University of Nevada Reno, Reno, NV, USA
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Wambaugh JF, Rager JE. Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:783-793. [PMID: 36347934 PMCID: PMC9742338 DOI: 10.1038/s41370-022-00492-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/21/2022] [Accepted: 10/21/2022] [Indexed: 05/10/2023]
Abstract
Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.
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Affiliation(s)
- John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, USA.
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Julia E Rager
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Koval LE, Dionisio KL, Friedman KP, Isaacs KK, Rager JE. Environmental mixtures and breast cancer: identifying co-exposure patterns between understudied vs breast cancer-associated chemicals using chemical inventory informatics. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:794-807. [PMID: 35710593 PMCID: PMC9742149 DOI: 10.1038/s41370-022-00451-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 05/15/2023]
Abstract
BACKGROUND Although evidence linking environmental chemicals to breast cancer is growing, mixtures-based exposure evaluations are lacking. OBJECTIVE This study aimed to identify environmental chemicals in use inventories that co-occur and share properties with chemicals that have association with breast cancer, highlighting exposure combinations that may alter disease risk. METHODS The occurrence of chemicals within chemical use categories was characterized using the Chemical and Products Database. Co-exposure patterns were evaluated for chemicals that have an association with breast cancer (BC), no known association (NBC), and understudied chemicals (UC) identified through query of the Silent Spring Institute's Mammary Carcinogens Review Database and the U.S. Environmental Protection Agency's Toxicity Reference Database. UCs were ranked based on structure and physicochemical similarities and co-occurrence patterns with BCs within environmentally relevant exposure sources. RESULTS A total of 6793 chemicals had data available for exposure source occurrence analyses. 50 top-ranking UCs spanning five clusters of co-occurring chemicals were prioritized, based on shared properties with co-occuring BCs, including chemicals used in food production and consumer/personal care products, as well as potential endocrine system modulators. SIGNIFICANCE Results highlight important co-exposure conditions that are likely prevalent within our everyday environments that warrant further evaluation for possible breast cancer risk. IMPACT STATEMENT Most environmental studies on breast cancer have focused on evaluating relationships between individual, well-known chemicals and breast cancer risk. This study set out to expand this research field by identifying understudied chemicals and mixtures that may occur in everyday environments due to their patterns of commercial use. Analyses focused on those that co-occur alongside chemicals associated with breast cancer, based upon in silico chemical database querying and analysis. Particularly in instances when understudied chemicals share physicochemical properties and structural features with carcinogens, these chemical mixtures represent conditions that should be studied in future clinical, epidemiological, and toxicological studies.
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Affiliation(s)
- Lauren E Koval
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kathie L Dionisio
- Immediate Office of the Assistant Administrator, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
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Isaacs KK, Egeghy P, Dionisio KL, Phillips KA, Zidek A, Ring C, Sobus JR, Ulrich EM, Wetmore BA, Williams AJ, Wambaugh JF. The chemical landscape of high-throughput new approach methodologies for exposure. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:820-832. [PMID: 36435938 PMCID: PMC9882966 DOI: 10.1038/s41370-022-00496-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 05/25/2023]
Abstract
The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.
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Affiliation(s)
- Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Peter Egeghy
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathie L Dionisio
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Katherine A Phillips
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Angelika Zidek
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Caroline Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jon R Sobus
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Elin M Ulrich
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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Nicolas CI, Linakis MW, Minto MS, Mansouri K, Clewell RA, Yoon M, Wambaugh JF, Patlewicz G, McMullen PD, Andersen ME, Clewell III HJ. Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods. Front Pharmacol 2022; 13:980747. [PMID: 36278238 PMCID: PMC9586287 DOI: 10.3389/fphar.2022.980747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA’s ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA’s ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.
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Affiliation(s)
- Chantel I. Nicolas
- Office of Chemical Safety and Pollution Prevention, US EPA, Washington, DC, United States
| | | | | | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, United States
| | | | | | - John F. Wambaugh
- Center for Computational Toxicology and Exposure Office of Research and Development, US EPA, Research Triangle Park, NC, United States
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure Office of Research and Development, US EPA, Research Triangle Park, NC, United States
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Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF. Evaluation of a rapid, generic human gestational dose model. Reprod Toxicol 2022; 113:172-188. [PMID: 36122840 PMCID: PMC9761697 DOI: 10.1016/j.reprotox.2022.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 08/30/2022] [Accepted: 09/14/2022] [Indexed: 10/14/2022]
Abstract
Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.
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Affiliation(s)
- Dustin F Kapraun
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Mark Sfeir
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Robert G Pearce
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Sarah E Davidson-Fritz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Annie Lumen
- National Center for Toxicological Research, US Food and Drug Administration, USA
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
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Hubbard HF, Ring CL, Hong T, Henning CC, Vallero DA, Egeghy PP, Goldsmith MR. Exposure Prioritization ( Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool. TOXICS 2022; 10:569. [PMID: 36287849 PMCID: PMC9609548 DOI: 10.3390/toxics10100569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/24/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.
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Affiliation(s)
| | - Caroline L. Ring
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27713, USA
| | - Tao Hong
- ICF International, 2635 Meridian Parkway, Durham, NC 27713, USA
| | - Cara C. Henning
- ICF International, 2635 Meridian Parkway, Durham, NC 27713, USA
| | - Daniel A. Vallero
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27713, USA
| | - Peter P. Egeghy
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27713, USA
| | - Michael-Rock Goldsmith
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Durham, NC 27713, USA
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El-Masri H, Paul Friedman K, Isaacs K, Wetmore BA. Advances in computational methods along the exposure to toxicological response paradigm. Toxicol Appl Pharmacol 2022; 450:116141. [PMID: 35777528 PMCID: PMC9619339 DOI: 10.1016/j.taap.2022.116141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.
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Affiliation(s)
- Hisham El-Masri
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
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Roell K, Koval LE, Boyles R, Patlewicz G, Ring C, Rider CV, Ward-Caviness C, Reif DM, Jaspers I, Fry RC, Rager JE. Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research. FRONTIERS IN TOXICOLOGY 2022; 4:893924. [PMID: 35812168 PMCID: PMC9257219 DOI: 10.3389/ftox.2022.893924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/30/2022] [Indexed: 01/09/2023] Open
Abstract
Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.
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Affiliation(s)
- Kyle Roell
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Lauren E. Koval
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Rebecca Boyles
- Research Computing, RTI International, Durham, NC, United States
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Caroline Ring
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Cynthia V. Rider
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Cavin Ward-Caviness
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Chapel Hill, NC, United States
| | - David M. Reif
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
| | - Ilona Jaspers
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Department of Pediatrics, Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Rebecca C. Fry
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Julia E. Rager
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- *Correspondence: Julia E. Rager,
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Isaacs KK, Wall JT, Williams AR, Hobbie KA, Sobus JR, Ulrich E, Lyons D, Dionisio KL, Williams AJ, Grulke C, Foster CA, McCoy J, Bevington C. A harmonized chemical monitoring database for support of exposure assessments. Sci Data 2022; 9:314. [PMID: 35710792 PMCID: PMC9203490 DOI: 10.1038/s41597-022-01365-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 05/05/2022] [Indexed: 11/09/2022] Open
Abstract
Direct monitoring of chemical concentrations in different environmental and biological media is critical to understanding the mechanisms by which human and ecological receptors are exposed to exogenous chemicals. Monitoring data provides evidence of chemical occurrence in different media and can be used to inform exposure assessments. Monitoring data provide required information for parameterization and evaluation of predictive models based on chemical uses, fate and transport, and release or emission processes. Finally, these data are useful in supporting regulatory chemical assessment and decision-making. There are a wide variety of public monitoring data available from existing government programs, historical efforts, public data repositories, and peer-reviewed literature databases. However, these data are difficult to access and analyze in a coordinated manner. Here, data from 20 individual public monitoring data sources were extracted, curated for chemical and medium, and harmonized into a sustainable machine-readable data format for support of exposure assessments.
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Affiliation(s)
- Kristin K Isaacs
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - Jonathan T Wall
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | | | - Kevin A Hobbie
- ICF International, 2635 Meridian Pkwy #200, Durham, NC, 27713, USA
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Elin Ulrich
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - David Lyons
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Kathie L Dionisio
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Christopher Grulke
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | | | - Josiah McCoy
- ICF International, 2635 Meridian Pkwy #200, Durham, NC, 27713, USA
| | - Charles Bevington
- U.S. Consumer Product Safety Commission 5 Research Place Rockville, Rockville, MD, 20850, USA
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Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells. TOXICS 2022; 10:toxics10050199. [PMID: 35622613 PMCID: PMC9146550 DOI: 10.3390/toxics10050199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/16/2022] [Indexed: 01/27/2023]
Abstract
There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.
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41
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Huang L, Fantke P, Ritscher A, Jolliet O. Chemicals of concern in building materials: A high-throughput screening. JOURNAL OF HAZARDOUS MATERIALS 2022; 424:127574. [PMID: 34799153 DOI: 10.1016/j.jhazmat.2021.127574] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/05/2021] [Accepted: 10/19/2021] [Indexed: 05/24/2023]
Abstract
Chemicals used in building materials can be a major passive emission source indoors, associated with the deterioration of indoor environmental quality. This study aims to screen the various chemicals used in building materials for potential near-field human exposures and related health risks, identifying chemicals and products of concern to inform risk reduction efforts. We propose a mass balance-based and high-throughput suited model for predicting chemical emissions from building materials considering indoor sorption. Using this model, we performed a screening-level human exposure assessment for chemicals in building materials, starting from product chemical composition data reported in the Pharos Building Products Database for the USA. Health risks and MAximum chemical Contents from High-Throughput Screening (MACHTS) were determined, combining exposure estimates with toxicity information. Exposures were estimated for > 300 unique chemical-product combinations from the Pharos databases, of which 73% (25%) had non-cancer (cancer) toxicity data available. We identified 55 substances as chemicals of high concern, with actual chemical contents exceeding MACHTS by up to a factor 105, in particular diisocyanates and formaldehyde. This stresses the need for more refined investigations to select safer alternatives. This study serves as a suitable starting point for prioritizing chemicals/products and thus developing safer and more sustainable building materials.
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Affiliation(s)
- Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark
| | - Amélie Ritscher
- Individual Contractor, Economy Division, United Nations Environment Programme, 8-14 Avenue de la Paix, CH-1211 Geneva 10, Switzerland
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA; Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark.
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42
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Schmidt S. Filling in the Blanks: A New Tool to Predict Chemical Pathways from Production to Exposure. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:24002. [PMID: 35175097 PMCID: PMC8852263 DOI: 10.1289/ehp10756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
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Gustavsson M, Molander S, Backhaus T, Kristiansson E. Estimating the release of chemical substances from consumer products, textiles and pharmaceuticals to wastewater. CHEMOSPHERE 2022; 287:131854. [PMID: 34461333 DOI: 10.1016/j.chemosphere.2021.131854] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/16/2021] [Accepted: 08/08/2021] [Indexed: 06/13/2023]
Abstract
Chemical emissions from households originate from a wide range of sources and results in highly diverse mixtures. This makes traditional monitoring based on analytical chemistry challenging, especially for compounds that appear in low concentrations. We therefore developed a method for predicting emissions of chemicals from households into wastewater, relying on consumption patterns from multiple data sources. The method was then used to predict the emissions of chemical preparations, chemicals leaching from textiles and prescription pharmaceuticals in Sweden. In total we predicted emissions of 2007 chemicals with a combined emission of 62,659 tonnes per year - or 18 g/person and day. Of the emitted chemicals, 2.0% (w/w) were either classified as hazardous to the environment or were both persistent and mobile. We also show that chemical emissions come from a wide range of uses and that the total emission of any individual chemical is determined primarily by its use pattern, not by the total amount used. This emphasizes the need for continuous updates and additional knowledge generation both on emission factors and excretion rates as well as a need for improved reporting on the intended use of individual chemicals. Finally, we scrutinize the model and its uncertainty and suggest areas that need improvement to increase the accuracy of future emission modelling. We conclude that emission modelling can help guide environmental monitoring and provide input into management strategies aimed at reducing the environmental effect caused by hazardous chemicals.
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Affiliation(s)
- M Gustavsson
- Department of Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden.
| | - S Molander
- Division of Environmental Systems Analysis, Department of Technology Management and Economics, Chalmers University of Technology, Gothenburg, Sweden.
| | - T Backhaus
- Department of Biology and Environment Science, University of Gothenburg, Gothenburg, Sweden.
| | - E Kristiansson
- Department of Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden.
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44
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McCord JP, Groff LC, Sobus JR. Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization. ENVIRONMENT INTERNATIONAL 2022; 158:107011. [PMID: 35386928 PMCID: PMC8979303 DOI: 10.1016/j.envint.2021.107011] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.
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Affiliation(s)
- James P. McCord
- Center for Environmental Measurement and Modeling, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Corresponding author. (J.P. McCord)
| | - Louis C. Groff
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Jon R. Sobus
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
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Zhang Z, Wang S, Li L. Emerging investigator series: the role of chemical properties in human exposure to environmental chemicals. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2021; 23:1839-1862. [PMID: 34542121 DOI: 10.1039/d1em00252j] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
One of the ultimate goals of environmental exposure science is to mechanistically understand how chemical properties and human behavior interactively determine human exposure to the wide spectrum of chemicals present in the environment. This comprehensive review assembles state-of-the-art knowledge of the role of partitioning, dissociation, mass transfer, and reactive properties in human contact with and absorption of organic chemicals via oral, dermal, and respiratory routes. Existing studies have revealed that chemicals with different properties vary greatly in mass distribution and occurrence among multiple exposure media, resulting in distinct patterns of human intake from the environment. On the other hand, these chemicals encounter different levels of resistance in the passage of intestinal, dermal, and pulmonary absorption barriers and demonstrate different levels of bioavailability, due to the selectivity of biochemical, anatomical and physiological structures of these absorption barriers. Moving forward, the research community needs to gain more in-depth mechanistic insights into the complex processes in human exposure, advance the technique to better characterize and predict chemical properties, generate and leverage experimental data for a more diverse range of chemicals, and describe better the interactions between chemical properties and human behavior.
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Affiliation(s)
- Zhizhen Zhang
- School of Public Health, University of Nevada Reno, 1664 N. Virginia Street, 89557-274, Reno, Nevada, USA.
| | - Shenghong Wang
- School of Public Health, University of Nevada Reno, 1664 N. Virginia Street, 89557-274, Reno, Nevada, USA.
| | - Li Li
- School of Public Health, University of Nevada Reno, 1664 N. Virginia Street, 89557-274, Reno, Nevada, USA.
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Li L, Sangion A, Wania F, Armitage JM, Toose L, Hughes L, Arnot JA. Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:127006. [PMID: 34882502 PMCID: PMC8658982 DOI: 10.1289/ehp9372] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
BACKGROUND Large numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management. OBJECTIVE We developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals. METHODS We consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts. RESULTS Seventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks. CONCLUSION Requiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.
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Affiliation(s)
- Li Li
- School of Public Health, University of Nevada, Reno, Reno, Nevada, USA
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Alessandro Sangion
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
| | - Frank Wania
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | | | - Liisa Toose
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
| | - Lauren Hughes
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
| | - Jon A. Arnot
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
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47
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Uncovering global-scale risks from commercial chemicals in air. Nature 2021; 600:456-461. [PMID: 34912090 DOI: 10.1038/s41586-021-04134-6] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 10/13/2021] [Indexed: 12/20/2022]
Abstract
Commercial chemicals are used extensively across urban centres worldwide1, posing a potential exposure risk to 4.2 billion people2. Harmful chemicals are often assessed on the basis of their environmental persistence, accumulation in biological organisms and toxic properties, under international and national initiatives such as the Stockholm Convention3. However, existing regulatory frameworks rely largely upon knowledge of the properties of the parent chemicals, with minimal consideration given to the products of their transformation in the atmosphere. This is mainly due to a dearth of experimental data, as identifying transformation products in complex mixtures of airborne chemicals is an immense analytical challenge4. Here we develop a new framework-combining laboratory and field experiments, advanced techniques for screening suspect chemicals, and in silico modelling-to assess the risks of airborne chemicals, while accounting for atmospheric chemical reactions. By applying this framework to organophosphate flame retardants, as representative chemicals of emerging concern5, we find that their transformation products are globally distributed across 18 megacities, representing a previously unrecognized exposure risk for the world's urban populations. More importantly, individual transformation products can be more toxic and up to an order-of-magnitude more persistent than the parent chemicals, such that the overall risks associated with the mixture of transformation products are also higher than those of the parent flame retardants. Together our results highlight the need to consider atmospheric transformations when assessing the risks of commercial chemicals.
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Abstract
Chemicals are measured regularly in air, food, the environment, and the workplace. Biomonitoring of chemicals in biological fluids is a tool to determine the individual exposure. Blood protein adducts of xenobiotics are a marker of both exposure and the biologically effective dose. Urinary metabolites and blood metabolites are short term exposure markers. Stable hemoglobin adducts are exposure markers of up to 120 days. Blood protein adducts are formed with many xenobiotics at different sites of the blood proteins. Newer methods apply the techniques developed in the field of proteomics. Larger adducted peptides with 20 amino acids are used for quantitation. Unfortunately, at present the methods do not reach the limits of detection obtained with the methods looking at single amino acid adducts or at chemically cleaved adducts. Therefore, to progress in the field new approaches are needed.
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Lowe K, Dawson J, Phillips K, Minucci J, Wambaugh JF, Qian H, Ramanarayanan T, Egeghy P, Ingle B, Brunner R, Mendez E, Embry M, Tan YM. Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies. Regul Toxicol Pharmacol 2021; 127:105073. [PMID: 34743952 DOI: 10.1016/j.yrtph.2021.105073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/23/2021] [Accepted: 10/27/2021] [Indexed: 10/20/2022]
Abstract
Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.
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Affiliation(s)
- Kelly Lowe
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Jeffrey Dawson
- U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Jeffrey Minucci
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | | | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Brandall Ingle
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
| | - Rachel Brunner
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
| | - Elizabeth Mendez
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Michelle Embry
- Health and Environmental Sciences Institute, Washington, DC, USA.
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Program, Durham, NC, USA
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Armitage JM, Hughes L, Sangion A, Arnot JA. Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: Implications for model selection and tiered modeling frameworks. ENVIRONMENT INTERNATIONAL 2021; 154:106557. [PMID: 33892222 DOI: 10.1016/j.envint.2021.106557] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/05/2021] [Accepted: 04/02/2021] [Indexed: 05/21/2023]
Abstract
This study describes the development and intercomparison of generic physiologically-based toxicokinetic (PBTK) models for humans comprised of internally consistent one-compartment (1Co-) and multi-compartment (MCo-) implementations (G-PBTK). The G-PBTK models were parameterized for an adult male (70 kg) using common physiological parameters and in vitro biotransformation rate estimates and subsequently evaluated using independent concentration versus time data (n = 6) and total elimination half-lives (n = 15) for diverse organic chemicals. The model performance is acceptable considering the inherent uncertainty in the biotransformation rate data and the absence of model calibration. The G-PBTK model was then applied using hypothetical neutral organics, acidic ionizable organics and basic ionizable organics (IOCs) to identify combinations of partitioning properties and biotransformation rates leading to substantial discrepancies between 1Co- and MCo-PBTK calculations for whole body concentrations and half-lives. The 1Co- and MCo-PBTK model calculations for key toxicokinetic parameters are broadly consistent unless biotransformation is rapid (e.g., half-life less than five days). When half-lives are relatively short, discrepancies are greatest for the neutral organics and least for the acidic IOCs which follows from the estimated volumes of distribution (e.g., VDSS = 9.6-15.4 L/kg vs 0.3-1.6 L/kg for the neutral and acidic compounds respectively) and the related approach to internal chemical equilibrium. The model intercomparisons demonstrate that 1Co-PBTK models can be applied with confidence to many exposure scenarios, particularly those focused on chronic or repeat exposures and for prioritization and screening-level decision contexts. However, MCo-PBTK models may be necessary in certain contexts, particularly for intermittent, short-term and highly variable exposures. A key recommendation to guide model selection and the development of tiered PBTK modeling frameworks that emerges from this study is the need to harmonize models with respect to parameterization and process descriptions to the greatest extent possible when proceeding from the application of simpler to more complex modeling tools as part of chemical assessment activities.
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Affiliation(s)
- James M Armitage
- AES Armitage Environmental Sciences, Inc., Ottawa, Ontario K1L 8C3, Canada; Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada.
| | - Lauren Hughes
- ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Alessandro Sangion
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Jon A Arnot
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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