1
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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. Environ Sci Technol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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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. J Expo Sci Environ Epidemiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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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. Environ Health Perspect 2024; 132:17009. [PMID: 38285237 PMCID: PMC10824265 DOI: 10.1289/ehp12188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Minucci JM, Purucker ST, Isaacs KK, Wambaugh JF, Phillips KA. A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data. Environ Sci Technol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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5
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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. J Expo Sci Environ Epidemiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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6
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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. J Expo Sci Environ Epidemiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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7
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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. J Expo Sci Environ Epidemiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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8
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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. J Expo Sci Environ Epidemiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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9
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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10
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Lowe CN, Phillips KA, Favela KA, Yau AY, Wambaugh JF, Sobus JR, Williams AJ, Pfirrman AJ, Isaacs KK. Chemical Characterization of Recycled Consumer Products Using Suspect Screening Analysis. Environ Sci Technol 2021; 55:11375-11387. [PMID: 34347456 PMCID: PMC8475772 DOI: 10.1021/acs.est.1c01907] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Recycled materials are found in many consumer products as part of a circular economy; however, the chemical content of recycled products is generally uncharacterized. A suspect screening analysis using two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) was applied to 210 products (154 recycled, 56 virgin) across seven categories. Chemicals in products were tentatively identified using a standard spectral library or confirmed using chemical standards. A total of 918 probable chemical structures identified (112 of which were confirmed) in recycled materials versus 587 (110 confirmed) in virgin materials. Identified chemicals were characterized in terms of their functional use and structural class. Recycled paper products and construction materials contained greater numbers of chemicals than virgin products; 733 identified chemicals had greater occurrence in recycled compared to virgin materials. Products made from recycled materials contained greater numbers of fragrances, flame retardants, solvents, biocides, and dyes. The results were clustered to identify groups of chemicals potentially associated with unique chemical sources, and identified chemicals were prioritized for further study using high-throughput hazard and exposure information. While occurrence is not necessarily indicative of risk, these results can be used to inform the expansion of existing models or identify exposure pathways currently neglected in exposure assessments.
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Affiliation(s)
- Charles N. Lowe
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, 37831, United States
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina, 27709, United States
| | - Katherine A. Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina, 27709, United States
| | - Kristin A. Favela
- Southwest Research Institute, San Antonio, Texas, 78759, United States
| | - Alice Y. Yau
- Southwest Research Institute, San Antonio, Texas, 78759, 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, 27709, United States
| | - Jon R. Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina, 27709, United States
| | - Antony J. Williams
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina, 27709, United States
| | - Ashley J. Pfirrman
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina, 27709, United States
- Oak Ridge Associated Universities, Oak Ridge, Tennessee, 37831, 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, 27709, United States
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11
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Stanfield Z, Addington CK, Dionisio KL, Lyons D, Tornero-Velez R, Phillips KA, Buckley TJ, Isaacs KK. Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures. Environ Health Perspect 2021; 129:67006. [PMID: 34160298 PMCID: PMC8221370 DOI: 10.1289/ehp8610] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
BACKGROUND Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge. OBJECTIVES We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. METHODS We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products. RESULTS We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. DISCUSSION Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. https://doi.org/10.1289/EHP8610.
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Affiliation(s)
- Zachary Stanfield
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee, USA
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Cody K Addington
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee, USA
| | - Kathie L Dionisio
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - David Lyons
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Rogelio Tornero-Velez
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Katherine A Phillips
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Timothy J Buckley
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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12
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Seltzer KM, Pennington E, Rao V, Murphy BN, Strum M, Isaacs KK, Pye HOT. Reactive organic carbon emissions from volatile chemical products. Atmos Chem Phys 2021; 21:5079-5100. [PMID: 34122530 PMCID: PMC8193795 DOI: 10.5194/acp-21-5079-2021] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Volatile chemical products (VCPs) are an increasingly important source of anthropogenic reactive organic carbon (ROC) emissions. Among these sources are everyday items, such as personal care products, general cleaners, architectural coatings, pesticides, adhesives, and printing inks. Here, we develop VCPy, a new framework to model organic emissions from VCPs throughout the United States, including spatial allocation to regional and local scales. Evaporation of a species from a VCP mixture in the VCPy framework is a function of the compound-specific physiochemical properties that govern volatilization and the timescale relevant for product evaporation. We introduce two terms to describe these processes: evaporation timescale and use timescale. Using this framework, predicted national per capita organic emissions from VCPs are 9.5 kg per person per year (6.4 kg C per person per year) for 2016, which translates to 3.05 Tg (2.06 Tg C), making VCPs a dominant source of anthropogenic organic emissions in the United States. Uncertainty associated with this framework and sensitivity to select parameters were characterized through Monte Carlo analysis, resulting in a 95 % confidence interval of national VCP emissions for 2016 of 2.61-3.53 Tg (1.76-2.38 Tg C). This nationwide total is broadly consistent with the U.S. EPA's 2017 National Emission Inventory (NEI); however, county-level and categorical estimates can differ substantially from NEI values. VCPy predicts higher VCP emissions than the NEI for approximately half of all counties, with 5 % of all counties having greater than 55 % higher emissions. Categorically, application of the VCPy framework yields higher emissions for personal care products (150 %) and paints and coatings (25 %) when compared to the NEI, whereas pesticides (-54 %) and printing inks (-13 %) feature lower emissions. An observational evaluation indicates emissions of key species from VCPs are reproduced with high fidelity using the VCPy framework (normalized mean bias of -13 % with r =0.95). Sector-wide, the effective secondary organic aerosol yield and maximum incremental reactivity of VCPs are 5.3 % by mass and 1.58 gO3 g-1, respectively, indicating VCPs are an important, and likely to date underrepresented, source of secondary pollution in urban environments.
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Affiliation(s)
- Karl M Seltzer
- Oak Ridge Institute for Science and Education Postdoctoral Fellow in the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Elyse Pennington
- Oak Ridge Institute for Science and Education Fellow in the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
- California Institute of Technology, Pasadena, CA 91125, USA
| | - Venkatesh Rao
- Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Benjamin N Murphy
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Madeleine Strum
- Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Kristin K Isaacs
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Havala O T Pye
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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13
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Qin M, Murphy BN, Isaacs KK, McDonald BC, Lu Q, McKeen SA, Koval L, Robinson AL, Efstathiou C, Allen C, Pye HO. Criteria pollutant impacts of volatile chemical products informed by near-field modeling. Nat Sustain 2020; N/A:1-57. [PMID: 33134558 PMCID: PMC7592713 DOI: 10.1038/s41893-020-00614-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 08/26/2020] [Indexed: 05/20/2023]
Abstract
Consumer, industrial, and commercial product usage is a source of exposure to potentially hazardous chemicals. In addition, cleaning agents, personal care products, coatings, and other volatile chemical products (VCPs), evaporate and react in the atmosphere producing secondary pollutants. Here, we show high air emissions from VCP usage (≥ 14 kg person-1 yr-1, at least 1.7× higher than current operational estimates) are supported by multiple estimation methods and constraints imposed by ambient levels of ozone, hydroxyl radical (OH) reactivity, and the organic component of fine particulate matter (PM2.5) in Pasadena, California. A near-field model, which estimates human chemical exposure during or in the vicinity of product use, indicates these high air emissions are consistent with organic product usage up to ~75 kg person-1 yr-1, and inhalation of consumer products could be a non-negligible exposure pathway. After constraining the PM2.5 yield to 5% by mass, VCPs produce ~41% of the photochemical organic PM2.5 (1.1 ± 0.3 μg m-3) and ~17% of maximum daily 8-hr average ozone (9 ± 2 ppb) in summer Los Angeles. Therefore, both toxicity and ambient criteria pollutant formation should be considered when organic substituents are developed for VCPs in pursuit of safer and sustainable products and cleaner air.
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Affiliation(s)
- Momei Qin
- Oak Ridge Institute for Science and Education (ORISE) Research Participant at the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, China
- Correspondence to: Momei Qin () and Havala Pye ()
| | - Benjamin N. Murphy
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Kristin K. Isaacs
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Brian C. McDonald
- Chemical Sciences Laboratory, NOAA Earth System Research Laboratories, Boulder, Colorado, USA
| | - Quanyang Lu
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Stuart A. McKeen
- Chemical Sciences Laboratory, NOAA Earth System Research Laboratories, Boulder, Colorado, USA
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
| | - Lauren Koval
- Oak Ridge Institute for Science and Education (ORISE) Research Participant at the Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Allen L. Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Christos Efstathiou
- General Dynamics Information Technology Research Triangle Park, North Carolina, USA
| | - Chris Allen
- General Dynamics Information Technology Research Triangle Park, North Carolina, USA
| | - Havala O.T. Pye
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Correspondence to: Momei Qin () and Havala Pye ()
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14
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Addington CK, Phillips KA, Isaacs KK. Estimation of the Emission Characteristics of SVOCs from Household Articles Using Group Contribution Methods. Environ Sci Technol 2020; 54:110-119. [PMID: 31822065 PMCID: PMC7346891 DOI: 10.1021/acs.est.9b06118] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The risk to humans from chemicals in consumer products is a function of both hazard and exposure. There is an ongoing effort to quantify chemical exposure due to household articles such as furniture and building materials. Polymers and plastic materials make up a substantial portion of these articles, which may contain chemical additives such as plasticizers. When these additives are not bound to the polymer matrix, they are free to diffuse throughout it and leach or emit from the surface. We have implemented a methodology to predict plasticizer emission from polyvinyl chloride (PVC) products, based on group contribution methods that consider a free volume effect to estimate activity coefficients for chemicals in polymer-solvent solutions. Using the estimated activity coefficients, we calculate steady-state gas phase concentrations for plasticizers in equilibrium with the polymer surface (y0). The method uses only the structure of the chemical and polymer, the weight fraction, and physical-chemical properties, allowing rapid estimation of y0 at different weight fractions in PVC. Using the predicted y0 values and weight fraction data gleaned from public databases, we estimate plasticizer exposures associated with 72 PVC-containing articles using a high-throughput model. We also investigate potential exposures associated with plasticizer substitutions in these products.
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Affiliation(s)
- Cody K. Addington
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee 37830, United States
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27709, United States
| | - Katherine A. Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27709, United States
| | - Kristin K. Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27709, United States
- Corresponding Author: 109 T.W Alexander Dr., NC 27711, USA, , Phone: (919) 541-2785
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15
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Isaacs KK, Dionisio K, Phillips K, Bevington C, Egeghy P, Price PS. Establishing a system of consumer product use categories to support rapid modeling of human exposure. J Expo Sci Environ Epidemiol 2020; 30:171-183. [PMID: 31712628 PMCID: PMC7745729 DOI: 10.1038/s41370-019-0187-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 05/22/2023]
Abstract
Consumer product categorizations for use in predicting human chemical exposure provide a bridge between product composition data and consumer product use pattern information. Furthermore, the categories reflect other factors relevant to developing consumer product exposure scenarios, such as microenvironment of use (e.g., indoors or outdoors), method of application/form of release (e.g., spray versus liquid), release to various media, removal processes (e.g., rinse-off or wipe-off), and route-specific exposure factors (dermal surface areas of application, fraction of release in respirable form). While challenging, developing harmonized product categories can generalize the factors described above allowing for rapid parameterization of route-specific exposure scenario algorithms for new chemical/product applications and efficient utilization of new data on product use or composition. This can be accomplished via mapping product categories to likewise categorized release and use patterns or exposure factors. Here, hierarchical product use categories (PUCs) for consumer products that provide such mappings are presented and crosswalked with other internationally harmonized product categories for consumer exposure assessment. The PUCs were defined by applying use and exposure scenario information to the products in EPA's Chemical and Products Database (CPDat). This paper demonstrates how these PUCs are being used to rapidly parameterize algorithms for scenario-specific use, fate, and exposure in a probabilistic aggregate model of human exposure to chemicals used in consumer products. The PUCs provide a generic representation of consumer products for use in exposure assessment and provide an efficient framework for flexible and rapid data reporting and consumer exposure model parameterization.
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Affiliation(s)
- Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - Kathie Dionisio
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Charles Bevington
- Office of Pollution Prevention and Toxics, 1200 Pennsylvania Avenue, North West Washington, DC, 20460, USA
| | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Paul S Price
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
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16
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Thomas RS, Bahadori T, Buckley TJ, Cowden J, Deisenroth C, Dionisio KL, Frithsen JB, Grulke CM, Gwinn MR, Harrill JA, Higuchi M, Houck KA, Hughes MF, Hunter ES, Isaacs KK, Judson RS, Knudsen TB, Lambert JC, Linnenbrink M, Martin TM, Newton SR, Padilla S, Patlewicz G, Paul-Friedman K, Phillips KA, Richard AM, Sams R, Shafer TJ, Setzer RW, Shah I, Simmons JE, Simmons SO, Singh A, Sobus JR, Strynar M, Swank A, Tornero-Valez R, Ulrich EM, Villeneuve DL, Wambaugh JF, Wetmore BA, Williams AJ. The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. Toxicol Sci 2019; 169:317-332. [PMID: 30835285 PMCID: PMC6542711 DOI: 10.1093/toxsci/kfz058] [Citation(s) in RCA: 195] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.
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Affiliation(s)
- Russell S. Thomas
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Tina Bahadori
- National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency
| | - Timothy J. Buckley
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - John Cowden
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Chad Deisenroth
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Kathie L. Dionisio
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Jeffrey B. Frithsen
- Chemical Safety for Sustainability National Research Program, Office of Research and Development, US Environmental Protection Agency
| | - Christopher M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Maureen R. Gwinn
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Joshua A. Harrill
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Mark Higuchi
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Keith A. Houck
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Michael F. Hughes
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - E. Sidney Hunter
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Thomas B. Knudsen
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Jason C. Lambert
- National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency
| | - Monica Linnenbrink
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Todd M. Martin
- National Risk Management Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Seth R. Newton
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Stephanie Padilla
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Grace Patlewicz
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Katie Paul-Friedman
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Katherine A. Phillips
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Reeder Sams
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Timothy J. Shafer
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - R. Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Jane E. Simmons
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Steven O. Simmons
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Amar Singh
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Jon R. Sobus
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Mark Strynar
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Adam Swank
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Rogelio Tornero-Valez
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Elin M. Ulrich
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Daniel L Villeneuve
- National Health and Environmental Effects Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
| | - Barbara A. Wetmore
- National Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
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17
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Ring CL, Arnot JA, Bennett DH, Egeghy PP, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips KA, Price PS, Shin HM, Westgate JN, Setzer RW, Wambaugh JF. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. Environ Sci Technol 2019; 53:719-732. [PMID: 30516957 PMCID: PMC6690061 DOI: 10.1021/acs.est.8b04056] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based exposure pathway models range from 73 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ∼0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479 926 chemicals, mostly with minimal exposure information. This approach identifies 1880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 μg/kg BW/day for 474572 compounds.
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Affiliation(s)
- Caroline L. Ring
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Jon A. Arnot
- ARC Arnot Research and Consulting, 36 Sproat Ave. Toronto, ON, Canada, M4M 1W4
- Department of Physical & Environmental Sciences, University of Toronto Scarborough 1265 Military Trail, Toronto, ON, Canada, M1C 1A4
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Cir, Toronto, ON, Canada, M5S 1A8
| | - Deborah H. Bennett
- Department of Public Health Sciences, University of California, Davis, California, 95616
| | - Peter P. Egeghy
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Peter Fantke
- Quantitative Sustainability Assessment Division, Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109
| | - Katherine A. Phillips
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Paul S. Price
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - Hyeong-Moo Shin
- Department of Earth and Environmental Sciences, University of Texas, Arlington, Texas, 76019
| | - John N. Westgate
- ARC Arnot Research and Consulting, 36 Sproat Ave. Toronto, ON, Canada, M4M 1W4
| | - R. Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
| | - John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711
- Corresponding Author: John F. Wambaugh, 109 T.W. Alexander Dr, NC 27711, USA, , Phone: (919) 541-7641
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18
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Nicolas CI, Mansouri K, Phillips KA, Grulke CM, Richard AM, Williams AJ, Rabinowitz J, Isaacs KK, Yau A, Wambaugh JF. Rapid experimental measurements of physicochemical properties to inform models and testing. Sci Total Environ 2018; 636:901-909. [PMID: 29729507 PMCID: PMC6214190 DOI: 10.1016/j.scitotenv.2018.04.266] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 04/14/2023]
Abstract
The structures and physicochemical properties of chemicals are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize the risk for thousands of environmental chemicals, but experimental values are often lacking. In an attempt to efficiently fill data gaps in physicochemical property information, we generated new data for 200 structurally diverse compounds, which were rigorously selected from the USEPA ToxCast chemical library, and whose structures are available within the Distributed Structure-Searchable Toxicity Database (DSSTox). This pilot study evaluated rapid experimental methods to determine five physicochemical properties, including the log of the octanol:water partition coefficient (known as log(Kow) or logP), vapor pressure, water solubility, Henry's law constant, and the acid dissociation constant (pKa). For most compounds, experiments were successful for at least one property; log(Kow) yielded the largest return (176 values). It was determined that 77 ToxPrint structural features were enriched in chemicals with at least one measurement failure, indicating which features may have played a role in rapid method failures. To gauge consistency with traditional measurement methods, the new measurements were compared with previous measurements (where available). Since quantitative structure-activity/property relationship (QSAR/QSPR) models are used to fill gaps in physicochemical property information, 5 suites of QSPRs were evaluated for their predictive ability and chemical coverage or applicability domain of new experimental measurements. The ability to have accurate measurements of these properties will facilitate better exposure predictions in two ways: 1) direct input of these experimental measurements into exposure models; and 2) construction of QSPRs with a wider applicability domain, as their predicted physicochemical values can be used to parameterize exposure models in the absence of experimental data.
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Affiliation(s)
- Chantel I Nicolas
- ScitoVation, LLC 6 Davis Drive, Durham, NC 27703, USA; National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Kamel Mansouri
- ScitoVation, LLC 6 Davis Drive, Durham, NC 27703, USA; National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Katherine A Phillips
- National Exposure Research Laboratory, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Christopher M Grulke
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - James Rabinowitz
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Kristin K Isaacs
- National Exposure Research Laboratory, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Alice Yau
- Southwest Research Institute, San Antonio, TX 78238, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA.
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Sobus JR, Wambaugh JF, Isaacs KK, Williams AJ, McEachran AD, Richard AM, Grulke CM, Ulrich EM, Rager JE, Strynar MJ, Newton SR. Integrating tools for non-targeted analysis research and chemical safety evaluations at the US EPA. J Expo Sci Environ Epidemiol 2018; 28:411-426. [PMID: 29288256 PMCID: PMC6661898 DOI: 10.1038/s41370-017-0012-y] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 08/04/2017] [Accepted: 08/25/2017] [Indexed: 05/18/2023]
Abstract
Tens-of-thousands of chemicals are registered in the U.S. for use in countless processes and products. Recent evidence suggests that many of these chemicals are measureable in environmental and/or biological systems, indicating the potential for widespread exposures. Traditional public health research tools, including in vivo studies and targeted analytical chemistry methods, have been unable to meet the needs of screening programs designed to evaluate chemical safety. As such, new tools have been developed to enable rapid assessment of potentially harmful chemical exposures and their attendant biological responses. One group of tools, known as "non-targeted analysis" (NTA) methods, allows the rapid characterization of thousands of never-before-studied compounds in a wide variety of environmental, residential, and biological media. This article discusses current applications of NTA methods, challenges to their effective use in chemical screening studies, and ways in which shared resources (e.g., chemical standards, databases, model predictions, and media measurements) can advance their use in risk-based chemical prioritization. A brief review is provided of resources and projects within EPA's Office of Research and Development (ORD) that provide benefit to, and receive benefits from, NTA research endeavors. A summary of EPA's Non-Targeted Analysis Collaborative Trial (ENTACT) is also given, which makes direct use of ORD resources to benefit the global NTA research community. Finally, a research framework is described that shows how NTA methods will bridge chemical prioritization efforts within ORD. This framework exists as a guide for institutions seeking to understand the complexity of chemical exposures, and the impact of these exposures on living systems.
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Affiliation(s)
- Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Andrew D McEachran
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Christopher M Grulke
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Julia E Rager
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
- ToxStrategies, Inc., 9390 Research Blvd., Suite 100, Austin, TX, 78759, USA
| | - Mark J Strynar
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Seth R Newton
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
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20
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Dionisio KL, Phillips K, Price PS, Grulke CM, Williams A, Biryol D, Hong T, Isaacs KK. The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products. Sci Data 2018; 5:180125. [PMID: 29989593 PMCID: PMC6038847 DOI: 10.1038/sdata.2018.125] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 04/30/2018] [Indexed: 01/29/2023] Open
Abstract
Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on more than 75,000 chemicals and more than 15,000 consumer products. The data's primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.
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Affiliation(s)
- Kathie L. Dionisio
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Paul S. Price
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Christopher M. Grulke
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Antony Williams
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Derya Biryol
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Tao Hong
- ICF International, 2635 Meridian Pkwy #200, Durham, NC 27713, USA
| | - Kristin K. Isaacs
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
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21
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Isaacs KK, Phillips KA, Biryol D, Dionisio KL, Price PS. Consumer product chemical weight fractions from ingredient lists. J Expo Sci Environ Epidemiol 2018; 28:216-222. [PMID: 29115287 PMCID: PMC6082127 DOI: 10.1038/jes.2017.29] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 07/25/2017] [Accepted: 08/12/2017] [Indexed: 05/29/2023]
Abstract
Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.
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Affiliation(s)
- Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, E205-02, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Katherine A Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, E205-02, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Derya Biryol
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, E205-02, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Kathie L Dionisio
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, E205-02, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
| | - Paul S Price
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, E205-02, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA
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22
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Phillips KA, Yau A, Favela KA, Isaacs KK, McEachran A, Grulke C, Richard AM, Williams AJ, Sobus JR, Thomas RS, Wambaugh JF. Suspect Screening Analysis of Chemicals in Consumer Products. Environ Sci Technol 2018; 52:3125-3135. [PMID: 29405058 PMCID: PMC6168952 DOI: 10.1021/acs.est.7b04781] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
A two-dimensional gas chromatography-time-of-flight/mass spectrometry (GC×GC-TOF/MS) suspect screening analysis method was used to rapidly characterize chemicals in 100 consumer products-which included formulations (e.g., shampoos, paints), articles (e.g., upholsteries, shower curtains), and foods (cereals)-and therefore supports broader efforts to prioritize chemicals based on potential human health risks. Analyses yielded 4270 unique chemical signatures across the products, with 1602 signatures tentatively identified using the National Institute of Standards and Technology 2008 spectral database. Chemical standards confirmed the presence of 119 compounds. Of the 1602 tentatively identified chemicals, 1404 were not present in a public database of known consumer product chemicals. Reported data and model predictions of chemical functional use were applied to evaluate the tentative chemical identifications. Estimated chemical concentrations were compared to manufacturer-reported values and other measured data. Chemical presence and concentration data can now be used to improve estimates of chemical exposure, and refine estimates of risk posed to human health and the environment.
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Affiliation(s)
- Katherine A. Phillips
- National Exposure Research Laboratory U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Alice Yau
- Southwest Research Institute, San Antonio, TX
| | | | - Kristin K. Isaacs
- National Exposure Research Laboratory U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Andrew McEachran
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA 37830
- National Center for Computational Toxicology U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Christopher Grulke
- National Center for Computational Toxicology U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Ann M. Richard
- National Center for Computational Toxicology U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Antony J. Williams
- National Center for Computational Toxicology U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Jon R. Sobus
- National Exposure Research Laboratory U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - Russell S. Thomas
- National Center for Computational Toxicology U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
| | - John F. Wambaugh
- National Center for Computational Toxicology U.S. Environmental Protection Agency, Office of Research and Development, 109 T. W. Alexander Drive, RTP, NC USA 27711
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Craig Barber M, Isaacs KK, Tebes-Stevens C. Developing and applying metamodels of high resolution process-based simulations for high throughput exposure assessment of organic chemicals in riverine ecosystems. Sci Total Environ 2017; 605-606:471-481. [PMID: 28672236 PMCID: PMC6093200 DOI: 10.1016/j.scitotenv.2017.06.198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 06/23/2017] [Accepted: 06/23/2017] [Indexed: 05/03/2023]
Abstract
As defined by Wikipedia (https://en.wikipedia.org/wiki/Metamodeling), "(a) metamodel or surrogate model is a model of a model, and metamodeling is the process of generating such metamodels." The goals of metamodeling include, but are not limited to (1) developing functional or statistical relationships between a model's input and output variables for model analysis, interpretation, or information consumption by users' clients; (2) quantifying a model's sensitivity to alternative or uncertain forcing functions, initial conditions, or parameters; and (3) characterizing the model's response or state space. Using five models developed by the US Environmental Protection Agency, we generate a metamodeling database of the expected environmental and biological concentrations of 644 organic chemicals released into nine US rivers from wastewater treatment works (WTWs) assuming multiple loading rates and sizes of populations serviced. The chemicals of interest have log n-octanol/water partition coefficients (logKOW) ranging from 3 to 14, and the rivers of concern have mean annual discharges ranging from 1.09 to 3240m3/s. Log-linear regression models are derived to predict mean annual dissolved and total water concentrations and total sediment concentrations of chemicals of concern based on their logKOW, Henry's Law Constant, and WTW loading rate and on the mean annual discharges of the receiving rivers. Metamodels are also derived to predict mean annual chemical concentrations in fish, invertebrates, and periphyton. We corroborate a subset of these metamodels using field studies focused on brominated flame retardants and discuss their application for high throughput screening of exposures to human and ecological populations and for analysis and interpretation of field data.
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Affiliation(s)
- M Craig Barber
- US Environmental Protection Agency, National Exposure Research Laboratory, Systems Exposure Division, 960 College Station Road, Athens, GA 30605-2700, USA.
| | - Kristin K Isaacs
- US Environmental Protection Agency, National Exposure Research Laboratory, Computational Exposure Division, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, USA.
| | - Caroline Tebes-Stevens
- US Environmental Protection Agency, National Exposure Research Laboratory, Systems Exposure Division, 960 College Station Road, Athens, GA 30605-2700, USA.
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Dionisio KL, Nolte CG, Spero TL, Graham S, Caraway N, Foley KM, Isaacs KK. Characterizing the impact of projected changes in climate and air quality on human exposures to ozone. J Expo Sci Environ Epidemiol 2017; 27:260-270. [PMID: 28120830 PMCID: PMC8958429 DOI: 10.1038/jes.2016.81] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 11/23/2016] [Indexed: 05/21/2023]
Abstract
The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O3) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O3 exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O3 exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O3 exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O3 are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O3 exposure as a result of changes in climate that could impact human health.
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Affiliation(s)
- Kathie L. Dionisio
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
| | - Christopher G. Nolte
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
| | - Tanya L. Spero
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
| | - Stephen Graham
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Kristen M. Foley
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, RTP, NC, USA
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25
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Phillips KA, Wambaugh JF, Grulke CM, Dionisio KL, Isaacs KK. High-throughput screening of chemicals as functional substitutes using structure-based classification models. Green Chem 2017; 19:1063-1074. [PMID: 30505234 PMCID: PMC6260937 DOI: 10.1039/c6gc02744j] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Identifying chemicals that provide a specific function within a product, yet have minimal impact on the human body or environment, is the goal of most formulation chemists and engineers practicing green chemistry. We present a methodology to identify potential chemical functional substitutes from large libraries of chemicals using machine learning based models. We collect and analyze publicly available information on the function of chemicals in consumer products or industrial processes to identify a suite of harmonized function categories suitable for modeling. We use structural and physicochemical descriptors for these chemicals to build 41 quantitative structure-use relationship (QSUR) models for harmonized function categories using random forest classification. We apply these models to screen a library of nearly 6400 chemicals with available structure information for potential functional substitutes. Using our Functional Use database (FUse), we could identify uses for 3121 chemicals; 4412 predicted functional uses had a probability of 80% or greater. We demonstrate the potential application of the models to high-throughput (HT) screening for "candidate alternatives" by merging the valid functional substitute classifications with hazard metrics developed from HT screening assays for bioactivity. A descriptor set could be obtained for 6356 Tox21 chemicals that have undergone a battery of HT in vitro bioactivity screening assays. By applying QSURs, we were able to identify over 1600 candidate chemical alternatives. These QSURs can be rapidly applied to thousands of additional chemicals to generate HT functional use information for combination with complementary HT toxicity information for screening for greener chemical alternatives.
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Affiliation(s)
- Katherine A. Phillips
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee 37830, USA
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
- ; Tel: +1-919-541-4966
| | - John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Christopher M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Kathie L. Dionisio
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
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Csiszar SA, Meyer DE, Dionisio KL, Egeghy P, Isaacs KK, Price PS, Scanlon KA, Tan YM, Thomas K, Vallero D, Bare JC. Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals. Environ Sci Technol 2016; 50:11922-11934. [PMID: 27668689 PMCID: PMC7388028 DOI: 10.1021/acs.est.6b02277] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.
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Affiliation(s)
- Susan A Csiszar
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, hosted at U.S. Environmental Protection Agency , Cincinnati, Ohio 45268, United States
| | - David E Meyer
- Office of Research and Development, National Risk Management Research Laboratory, U.S. Environmental Protection Agency , Cincinnati, Ohio 45268, United States
| | - Kathie L Dionisio
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Peter Egeghy
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Kristin K Isaacs
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Paul S Price
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Kelly A Scanlon
- AAAS Science & Technology Policy Fellow hosted by the U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Radiation and Indoor Air, Washington, DC 20460, United States
| | - Yu-Mei Tan
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Kent Thomas
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Daniel Vallero
- Office of Research and Development, National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Jane C Bare
- Office of Research and Development, National Risk Management Research Laboratory, U.S. Environmental Protection Agency , Cincinnati, Ohio 45268, United States
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Isaacs KK, Goldsmith MR, Egeghy P, Phillips K, Brooks R, Hong T, Wambaugh JF. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicol Rep 2016; 3:723-732. [PMID: 28959598 PMCID: PMC5616074 DOI: 10.1016/j.toxrep.2016.08.011] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 08/05/2016] [Accepted: 08/31/2016] [Indexed: 10/25/2022] Open
Abstract
Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization.
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Affiliation(s)
- Kristin K. Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Michael-Rock Goldsmith
- Chemical Computing Group, Suite 910, 1010 Sherbrooke Street West, Montreal, QC H3A 2R7, Canada
| | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Katherine Phillips
- Oak Ridge Institute for Science and Education, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Raina Brooks
- Student Services Contractor, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Tao Hong
- ICF International, 2635 Meridian Pkwy #200, Durham, NC 27713, United States
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
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Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith MR, Wambaugh JF, Judson RS, Buckley TJ. Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment. Environ Health Perspect 2016; 124:697-702. [PMID: 26545029 PMCID: PMC4892918 DOI: 10.1289/ehp.1509748] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 10/30/2015] [Indexed: 05/19/2023]
Abstract
BACKGROUND Computational exposure science represents a frontier of environmental science that is emerging and quickly evolving. OBJECTIVES In this commentary, we define this burgeoning discipline, describe a framework for implementation, and review some key ongoing research elements that are advancing the science with respect to exposure to chemicals in consumer products. DISCUSSION The fundamental elements of computational exposure science include the development of reliable, computationally efficient predictive exposure models; the identification, acquisition, and application of data to support and evaluate these models; and generation of improved methods for extrapolating across chemicals. We describe our efforts in each of these areas and provide examples that demonstrate both progress and potential. CONCLUSIONS Computational exposure science, linked with comparable efforts in toxicology, is ushering in a new era of risk assessment that greatly expands our ability to evaluate chemical safety and sustainability and to protect public health. CITATION Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith M-R, Wambaugh JF, Judson RS, Buckley TJ. 2016. Computational exposure science: an emerging discipline to support 21st-century risk assessment. Environ Health Perspect 124:697-702; http://dx.doi.org/10.1289/ehp.1509748.
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Affiliation(s)
- Peter P. Egeghy
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | | | - Kristin K. Isaacs
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | | | - Michael-Rock Goldsmith
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - John F. Wambaugh
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Richard S. Judson
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Timothy J. Buckley
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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Pleil JD, Isaacs KK. High-resolution mass spectrometry: basic principles for using exact mass and mass defect for discovery analysis of organic molecules in blood, breath, urine and environmental media. J Breath Res 2016; 10:012001. [DOI: 10.1088/1752-7155/10/1/012001] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM, Grulke CM, Wambaugh JF, Isaacs KK, Judson R, Williams AJ, Sobus JR. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ Int 2016; 88:269-280. [PMID: 26812473 DOI: 10.1016/j.envint.2015.12.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Revised: 12/03/2015] [Accepted: 12/09/2015] [Indexed: 05/18/2023]
Abstract
There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPA's ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPA's Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC-TOF/molecular feature data (match score≥90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.
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Affiliation(s)
- Julia E Rager
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Mark J Strynar
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Shuang Liang
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Rebecca L McMahen
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Christopher M Grulke
- Lockheed Martin, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Richard Judson
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States.
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Isaacs KK, Glen WG, Egeghy P, Goldsmith MR, Smith L, Vallero D, Brooks R, Grulke CM, Özkaynak H. SHEDS-HT: an integrated probabilistic exposure model for prioritizing exposures to chemicals with near-field and dietary sources. Environ Sci Technol 2014; 48:12750-9. [PMID: 25222184 DOI: 10.1021/es502513w] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
United States Environmental Protection Agency (USEPA) researchers are developing a strategy for high-throughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologically relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Based on probabilistic methods and algorithms developed for The Stochastic Human Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM), a new mechanistic modeling approach has been developed to accommodate high-throughput (HT) assessment of exposure potential. In this SHEDS-HT model, the residential and dietary modules of SHEDS-MM have been operationally modified to reduce the user burden, input data demands, and run times of the higher-tier model, while maintaining critical features and inputs that influence exposure. The model has been implemented in R; the modeling framework links chemicals to consumer product categories or food groups (and thus exposure scenarios) to predict HT exposures and intake doses. Initially, SHEDS-HT has been applied to 2507 organic chemicals associated with consumer products and agricultural pesticides. These evaluations employ data from recent USEPA efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. In modeling indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based module to estimate concentrations in indoor environmental media. The concentration estimates, along with relevant exposure factors and human activity data, are then used by the model to rapidly generate probabilistic population distributions of near-field indirect exposures via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific estimates of near-field direct exposures from consumer products are also modeled. Population dietary exposures for a variety of chemicals found in foods are combined with the corresponding chemical-specific near-field exposure predictions to produce aggregate population exposure estimates. The estimated intake dose rates (mg/kg/day) for the 2507 chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully reproduced the pathway-specific exposure results of the higher-tier SHEDS-MM for a case-study pesticide and produced median intake doses significantly correlated (p<0.0001, R2=0.39) with medians inferred using biomonitoring data for 39 chemicals from the National Health and Nutrition Examination Survey (NHANES). Based on the favorable performance of SHEDS-HT with respect to these initial evaluations, we believe this new tool will be useful for HT prediction of chemical exposure potential.
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Affiliation(s)
- Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27709, United States
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32
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Breen MS, Long TC, Schultz BD, Crooks J, Breen M, Langstaff JE, Isaacs KK, Tan YM, Williams RW, Cao Y, Geller AM, Devlin RB, Batterman SA, Buckley TJ. GPS-based microenvironment tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation in central North Carolina. J Expo Sci Environ Epidemiol 2014; 24:412-20. [PMID: 24619294 PMCID: PMC4269558 DOI: 10.1038/jes.2014.13] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2013] [Accepted: 12/19/2013] [Indexed: 05/22/2023]
Abstract
A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.
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Affiliation(s)
- Michael S. Breen
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Thomas C. Long
- National Center for Environmental Assessment, US EPA, Research Triangle Park, NC, USA
| | - Bradley D. Schultz
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - James Crooks
- National Health and Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Miyuki Breen
- National Health and Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - John E. Langstaff
- Office of Air Quality Planning and Standards, US EPA, Research Triangle Park, NC, USA
| | - Kristin K. Isaacs
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Yu-Mei Tan
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Ronald W. Williams
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | - Ye Cao
- National Center for Environmental Assessment, US EPA, Research Triangle Park, NC, USA
| | - Andrew M. Geller
- Immediate Office of the Assistant Administrator, US EPA, Research Triangle Park, NC, USA
| | - Robert B. Devlin
- National Health and Environmental Effects Research Laboratory, US EPA, Research Triangle Park, NC, USA
| | | | - Timothy J. Buckley
- National Exposure Research Laboratory, US EPA, Research Triangle Park, NC, USA
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Isaacs KK, Schlesinger RB, Martonen TB. Three-Dimensional Computational Fluid Dynamics Simulations of Particle Deposition in the Tracheobronchial Tree. ACTA ACUST UNITED AC 2006; 19:344-52. [PMID: 17034309 DOI: 10.1089/jam.2006.19.344] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Simulation of the dynamics and disposition of inhaled particles within human lungs is an invaluable tool in both the development of inhaled pharmacologic drugs and the risk assessment of environmental particulate matter (PM). The goal of the present focused study was to assess the utility of three-dimensional computational fluid dynamics (CFD) models in studying the local deposition patterns of PM in respiratory airways. CFD models were validated using data from published experimental studies in human lung casts. The ability of CFD to appropriately simulate trends in deposition patterns due to changing ventilatory conditions was specifically addressed. CFD simulations of airflow and particle motion were performed in a model of the trachea and main bronchi using Fluent Inc.'s FIDAP CFD software. Particle diameters of 8 microm were considered for input flow rates of 15 and 60 L/min. CFD was able to reproduce the observed spatial heterogeneities of deposition within the modeled bifurcations, and correctly predicted the "hot-spots" of particle deposition on carinal ridges. The CFD methods also predicted observed differences in deposition for high-versus-low flow rates. CFD models may provide an efficient means of studying the complex effects of airway geometry, particle characteristics, and ventilatory parameters on particle deposition and therefore aid in the design of human subject experiments.
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Affiliation(s)
- Kristin K Isaacs
- University of North Carolina at Chapel Hill, Department of Environmental Sciences and Engineering, Chapel Hill, North Carolina, USA.
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Abstract
A mathematical model of inhaled aerosol particle deposition for children is presented and validated with data from two published experimental studies. The model accurately predicts deposition fraction (DF) in children as a function of particle size for particles in the size range 1-3 microns for both sedentary and exercise breathing conditions. When the experimental data are grouped according to age, the model is able to predict age-dependent trends in DF at the studied particle sizes under sedentary breathing conditions. The model predicts that when ventilatory conditions are held constant, age-dependent changes in morphology result in decreasing DF with age; however, under realistic conditions these changes may be masked by age-dependent changes in ventilation. Despite the fact that mean DF differs significantly from adult values only in children younger than 9, the model predicted that dose-per-surface area may still be greater in children due to smaller lung sizes.
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Affiliation(s)
- Kristin K Isaacs
- Experimental Toxicology Division, National Health and Environmental Effects Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
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Martonen TB, Smyth HD, Isaacs KK, Burton RT. Issues in drug delivery: concepts and practice. Respir Care 2005; 50:1228-52. [PMID: 16163810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Understanding the transport and deposition of inhaled aerosols is of fundamental importance to inhalation therapy. Herein we address issues that affect drug delivery from experimental and theoretical perspectives. Accordingly, we shall limit our comments to a focused review of laboratory work (ie, an in vitro perspective) and the development of a computer-based 3-dimensional (3D) oral morphology with related computational fluid dynamics (CFD) and particle deposition studies (ie, an "in silico" perspective). To describe the oral region, morphometric data from the literature were employed. With Maya Unlimited, a third-party animation software package, coronal images were used to create initial spline curves, which served as the foundation of a nonuniform rational B-spline surface, representing a 3D morphology. To the best of our knowledge, this study is the first medical application of Maya Unlimited. We have demonstrated that the code can be employed to construct 3D biological structures and perform 3D CFD simulations of aerosols from dry powder inhalers and metered-dose inhalers. A study was also conducted using Fluent, a commercially available software package that has been used extensively in our laboratory for 3D CFD computations. The Maya Unlimited software can generate physiologically realistic oral structures; it has great potential for use in the medical arena, because it requires neither advance technical training nor substantial peripheral ( eg, hardware) support, it allows for the introduction of medical devices ( eg, dry powder inhalers) into simulations, and it predicts 3D CFDpatterns consistent with experimental observations and results of more rigorous software ( Fluent). In the in vitro perspective we considered numerous salient topics, including the performances of dry powder inhalers and metered-dose inhalers, their respective operating characteristics, and relevance to in vivo data. We advocate that 3D CFD software be employed in a complementary manner, in real time, with aerosol therapy protocols in the medical arena, to promote the targeted delivery of inhaled drugs and thereby enhance their efficacies.
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Affiliation(s)
- Ted B Martonen
- Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA.
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Abstract
A physiologically based pharmacokinetic (PBPK) model incorporating mixed enzyme inhibition was used to determine the mechanism of metabolic interactions occurring during simultaneous exposures to the organic solvents chloroform and trichloroethylene (TCE). Visualization-based sensitivity and identifiability analyses of the model were performed to determine the conditions under which four inhibitory parameters describing inhibitor binding could be estimated. The sensitivity methods were used to reduce the 4-parameter estimation problem into two distinct 2-parameter problems. The inhibitory parameters were then estimated from multiple closed-chamber gas-uptake experiments using graphical methods. The estimated values of the four inhibitory parameters predicted that chloroform and TCE interact in a competitive manner. Based on the model analysis, we present recommendations for the design of experiments for determination of inhibition mechanism in binary chemical mixtures. We assert that a thorough analysis of the parameter-dependent sensitivity and identifiability characteristics can be used to plan efficient experimental protocols for the quantitative analysis of inhalation pharmacokinetics.
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Affiliation(s)
- Kristin K Isaacs
- Vanderbilt University, Department of Biomedical Engineering, Station B, Nashville, TN 37235, USA.
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Burton RT, Isaacs KK, Fleming JS, Martonen TB. Computer reconstruction of a human lung boundary model from magnetic resonance images. Respir Care 2004; 49:180-5. [PMID: 14964316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
A mathematical description of the morphology of the lung is necessary for modeling and analyzing the deposition of inhaled aerosols A model of the lung boundary was generated from magnetic resonance images, with the goal of creating a framework for anatomically realistic morphological models of the human airway network. We used data visualization and analysis software to reconstruct the lung volume from a series of transverse magnetic resonance images collected at many vertical locations in the lung, ranging from apex to base. The lung model was then built using isosurface extraction techniques. These modeling methods may facilitate the creation of customized morphological models for individual subjects, resulting in improved interpretation of aerosol distribution data from single-photon-emission computed tomography (SPECT). Such customized models could be developed for children and for patients with respiratory diseases, thus aiding in the study of inhaled medications and environmental aerosols in these populations.
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Affiliation(s)
- Ray T Burton
- Science Applications International Corporation, United States Environmental Protection Agency Scientific Visualization Laboratory, Research Triangle, North Carolina 27711, USA
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