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Brueck CL, Xin X, Lupolt SN, Kim BF, Santo RE, Lyu Q, Williams AJ, Nachman KE, Prasse C. (Non)targeted Chemical Analysis and Risk Assessment of Organic Contaminants in Darkibor Kale Grown at Rural and Urban Farms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:3690-3701. [PMID: 38350027 DOI: 10.1021/acs.est.3c09106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
This study investigated the presence and human hazards associated with pesticides and other anthropogenic chemicals identified in kale grown in urban and rural environments. Pesticides and related compounds (i.e., surfactants and metabolites) in kale samples were evaluated using a nontargeted data acquisition for targeted analysis method which utilized a pesticide mixture containing >1,000 compounds for suspect screening and quantification. We modeled population-level exposures and assessed noncancer hazards to DEET, piperonyl butoxide, prometon, secbumeton, terbumeton, and spinosyn A using nationally representative estimates of kale consumption across life stages in the US. Our findings indicate even sensitive populations (e.g., pregnant women and children) are not likely to experience hazards from these select compounds were they to consume kale from this study. However, a strictly nontargeted chemical analytical approach identified a total of 1,822 features across all samples, and principal component analysis revealed that the kale chemical composition may have been impacted by agricultural growing practices and environmental factors. Confidence level 2 compounds that were ≥5 times more abundant in the urban samples than in rural samples (p < 0.05) included chemicals categorized as "flavoring and nutrients" and "surfactants" in the EPA's Chemicals and Products Database. Using the US-EPA's Cheminformatics Hazard Module, we identified that many of the nontarget compounds have predicted toxicity scores of "very high" for several end points related to human health. These aspects would have been overlooked using traditional targeted analysis methods, although more information is needed to ascertain whether the compounds identified through nontargeted analysis are of environmental or human health concern. As such, our approach enabled the identification of potentially hazardous compounds that, based on their hazard assessment score, merit follow-up investigations.
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Affiliation(s)
- Christopher L Brueck
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Xiaoyue Xin
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Sara N Lupolt
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Risk Sciences and Public Policy Institute, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Brent F Kim
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Raychel E Santo
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Qinfan Lyu
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Washington, North Carolina 27711, United States
| | - Keeve E Nachman
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Risk Sciences and Public Policy Institute, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Center for a Livable Future, Johns Hopkins University, Baltimore, Maryland 21202, United States
| | - Carsten Prasse
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Risk Sciences and Public Policy Institute, Johns Hopkins University, Baltimore, Maryland 21205, United States
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2
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Han W, Wang Z, Xie Q, Chen X, Su L, Xie H, Chen J, Fu Z. Plastic protective nets: A significant but neglected "reservoir" for priority chemicals as revealed by composition analysis. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132905. [PMID: 37944235 DOI: 10.1016/j.jhazmat.2023.132905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Abstract
As chemical-intensive products, plastics are potential sources of emerging contaminants and pose risks to the ecosystem. However, knowledge on the inventory and emissions of chemicals in plastics remains scarce, prohibiting the lifecycle assessment of their environmental exposure. Herein, full compositions of plastic protective nets (PPNs, one globally used plastics) were analyzed via nontarget screening with mass spectrometry, optical emission spectrometry, infrared spectroscopy and thermogravimetric analysis. Nontarget screening identified 861 non-polymeric organic chemicals, which were classified by network-like similarity analysis into 9 communities, dominated by phthalates (PAEs), aliphatic/oxalic esters and branched alkanes. Notably, around 80.8% (696) of the chemicals were first observed in plastics, suggesting aplenty plastic additives have previously been overlooked. Quantification results indicated PPNs contained higher levels of priority chemicals, including detrimental lead (1.17 × 104 ng/g), benzotriazoles ultraviolet stabilizers (6.66 × 103 ng/g) and PAEs (1.87 × 104 ng/g) than other plastics commonly reported. Emission projections revealed that dibutyl phthalate in PPNs had an annual release (1.83 × 103 kg) comparable to that from greenhouse films in China. These findings suggest PPNs are a significant but neglected "reservoir" for priority chemicals, which could inform future research on resolving plastic compositions, so as to promote sound chemical management.
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Affiliation(s)
- Wenjing Han
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Qing Xie
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xi Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Lihao Su
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Huaijun Xie
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Zhiqiang Fu
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
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3
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Newmeyer MN, Quirós-Alcalá L, Kavi LK, Louis LM, Prasse C. Implementing a suspect screening method to assess occupational chemical exposures among US-based hairdressers serving an ethnically diverse clientele: a pilot study. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:566-574. [PMID: 36693958 PMCID: PMC10363568 DOI: 10.1038/s41370-023-00519-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND There are over 700,000 hairdressers in the United States, and it is estimated that >90% are female and 31% are Black or Hispanic/Latina. Racial and ethnic minorities in this workforce may be exposed to a unique mixture of potentially hazardous chemicals from products used and services provided. However, previous biomonitoring studies of hairdressers target a narrow list of compounds and few studies have investigated exposures among minority hairdressers. OBJECTIVE To assess occupational chemical exposures in a sample of US-based Black and Latina hairdressers serving an ethnically diverse clientele by analyzing urine specimens with a suspect screening method. METHODS Post-shift urine samples were collected from a sample of US female hairdressers (n = 23) and office workers (n = 17) and analyzed via reverse-phase liquid chromatography coupled to high-resolution mass spectrometry. Detected compounds were filtered based on peak area differences between groups and matching with a suspect screening list. When possible, compound identities were confirmed with reference standards. Possible exposure sources were evaluated for detected compounds. RESULTS The developed workflow allowed for the detection of 24 compounds with median peak areas ≥2x greater among hairdressers compared to office workers. Product use categories (PUCs) and harmonized functional uses were searched for these compounds, including confirmed compounds methylparaben, ethylparaben, propylparaben, and 2-naphthol. Most product use categories were associated with "personal use" and included 11 different "hair styling and care" product types (e.g., hair conditioner, hair relaxer). Functional uses for compounds without associated PUCs included fragrance, hair and skin conditioning, hair dyeing, and UV stabilizer. SIGNIFICANCE Our suspect screening approach detected several compounds not previously reported in biomonitoring studies of hairdressers. These results will help guide future studies to improve characterization of occupational chemical exposures in this workforce and inform exposure and risk mitigation strategies to reduce potential associated work-related health disparities.
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Affiliation(s)
- Matthew N Newmeyer
- Department of Environmental Health & Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Lesliam Quirós-Alcalá
- Department of Environmental Health & Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Lucy K Kavi
- Maryland Institute of Applied Environmental Health, School of Public Health, University of Maryland, College Park, MD, 20742, USA
| | - Lydia M Louis
- Department of Environmental Health & Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Carsten Prasse
- Department of Environmental Health & Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.
- Risk Sciences and Public Policy Institute, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
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4
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Minucci JM, Purucker ST, Isaacs KK, Wambaugh JF, Phillips KA. A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:5947-5956. [PMID: 36995295 PMCID: PMC10100548 DOI: 10.1021/acs.est.2c08234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 06/19/2023]
Abstract
A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m-3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA's Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.
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Affiliation(s)
- Jeffrey M. Minucci
- Center
for Public Health and Environmental Assessment, Office of Research
and Development, US Environmental Protection
Agency, 109 TW Alexander Drive, Durham, North Carolina 27709, United States
| | - S. Thomas Purucker
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
| | - Kristin K. Isaacs
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
| | - John F. Wambaugh
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
| | - Katherine A. Phillips
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, 109 TW Alexander
Drive, Durham, North Carolina 27709, United States
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5
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Hernandez-Betancur JD, Ruiz-Mercado GJ, Martin M. Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023; 11:3594-3602. [PMID: 36911873 PMCID: PMC9993395 DOI: 10.1021/acssuschemeng.2c05662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.
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Affiliation(s)
| | - Gerardo J. Ruiz-Mercado
- Office
of Research & Development, US Environmental
Protection Agency, Cincinnati, Ohio 45268, United States
- Chemical
Engineering Graduate Program, Universidad
del Atlántico, Puerto Colombia 080007, Colombia
| | - Mariano Martin
- Department
of Chemical Engineering, University of Salamanca, Salamanca 37008, Spain
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6
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Wambaugh JF, Rager JE. Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:783-793. [PMID: 36347934 PMCID: PMC9742338 DOI: 10.1038/s41370-022-00492-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/21/2022] [Accepted: 10/21/2022] [Indexed: 05/10/2023]
Abstract
Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.
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Affiliation(s)
- John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, USA.
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Julia E Rager
- Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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7
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Wu S, Wu X, Li H, Li D, Zheng J, Lin Q, Nerín C, Zhong H, Dong B. The characterization and influence factors of semi-volatile compounds from mechanically recycled polyethylene terephthalate (rPET) by combining GC×GC-TOFMS and chemometrics. JOURNAL OF HAZARDOUS MATERIALS 2022; 439:129583. [PMID: 35872450 DOI: 10.1016/j.jhazmat.2022.129583] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/03/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
A non-targeted method was developed for screening the semi-volatile compounds of different mechanically recycled PET intended for food contact materials. The data was further analyzed by multiple chemometrics methods to obtain the difference level, and the potential influence factors were investigated. The results showed that total dissolution with comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry was more effective than other reported methods. Based on the difference level, 97 compounds were characterized into 4 levels. 1-Methyl-2-pyrrolidinone originating from organic solvent was recognized as level IV and could be determined as the primary difference indicator. The contaminant is mainly attributed to the residuum derived from non-food consumer products. The specific types of contaminants and process parameters of the recycling, such as moisture content, properties of rPET, and temperature, were the potential key factors affecting the presence of semi-volatile compounds of mechanically recycled rPET.
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Affiliation(s)
- Siliang Wu
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Xuefeng Wu
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Hanke Li
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Dan Li
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Jianguo Zheng
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Qinbao Lin
- Key Laboratory of Product Packaging and Logistics, Packaging Engineering Institute, Jinan University, Zhuhai 519070, China
| | - Cristina Nerín
- Department of Analytical Chemistry, GUIA Group, I3A, EINA, University of Zaragoza, María de Luna 3, 50018 Zaragoza, Spain
| | - Huaining Zhong
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China.
| | - Ben Dong
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China.
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El-Masri H, Paul Friedman K, Isaacs K, Wetmore BA. Advances in computational methods along the exposure to toxicological response paradigm. Toxicol Appl Pharmacol 2022; 450:116141. [PMID: 35777528 PMCID: PMC9619339 DOI: 10.1016/j.taap.2022.116141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/27/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.
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Affiliation(s)
- Hisham El-Masri
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kristin Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA
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9
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Thornton LL, Carlson DE, Wiesner MR. Predicting emerging chemical content in consumer products using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:154849. [PMID: 35405240 DOI: 10.1016/j.scitotenv.2022.154849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Chemical ingredients in consumer products are continually changing. To understand our exposure to chemicals and their consequent risk, we need to know their concentrations in products, or chemical weight fractions. Unfortunately, manufacturers rarely report comprehensive weight fraction data on product labels. The goal of this study was to evaluate the utility of machine learning strategies for predicting weight fractions when chemical constituent data are limited. A "data-poor" framework was developed and tested using a small dataset on consumer products containing engineered nanomaterials to represent emerging substances. A second, more traditional framework was applied to a "data-rich" product dataset comprised of bulk-scale organic chemicals for comparison purposes. Feature variables included chemical properties, functional use categories (e.g., antimicrobial), product categories (e.g., makeup), product matrix categories, and whether weight fractions were manufacturer-reported or experimentally obtained. Classification into three weight fraction bins was done using a random forest or nonlinear support vector classifier. An ablation study revealed that functional use data improved predictive performance when included alongside chemical property data, suggesting the utility of functional use categories in evaluating the safety and sustainability of emerging chemicals. Models could roughly stratify material-product observations into order of magnitude weight fractions with moderate success; the best of these achieved an average balanced accuracy of 73% on the nanomaterials product data. Framework comparisons also revealed a positive trend in sample size versus average balanced accuracy, suggesting great promise for machine learning approaches with continued investment in chemical data collection.
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Affiliation(s)
- Luka Lila Thornton
- Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708, USA; Center for the Environmental Implications of NanoTechnology (CEINT), USA.
| | - David E Carlson
- Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708, USA; Duke University, Department of Biostatistics and Bioinformatics, Duke University Medical Center, 2424 Erwin Road, Suite 1102 Hock Plaza, Durham, NC 27710, USA
| | - Mark R Wiesner
- Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708, USA; Center for the Environmental Implications of NanoTechnology (CEINT), USA
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10
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Isaacs KK, Wall JT, Williams AR, Hobbie KA, Sobus JR, Ulrich E, Lyons D, Dionisio KL, Williams AJ, Grulke C, Foster CA, McCoy J, Bevington C. A harmonized chemical monitoring database for support of exposure assessments. Sci Data 2022; 9:314. [PMID: 35710792 PMCID: PMC9203490 DOI: 10.1038/s41597-022-01365-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 05/05/2022] [Indexed: 11/09/2022] Open
Abstract
Direct monitoring of chemical concentrations in different environmental and biological media is critical to understanding the mechanisms by which human and ecological receptors are exposed to exogenous chemicals. Monitoring data provides evidence of chemical occurrence in different media and can be used to inform exposure assessments. Monitoring data provide required information for parameterization and evaluation of predictive models based on chemical uses, fate and transport, and release or emission processes. Finally, these data are useful in supporting regulatory chemical assessment and decision-making. There are a wide variety of public monitoring data available from existing government programs, historical efforts, public data repositories, and peer-reviewed literature databases. However, these data are difficult to access and analyze in a coordinated manner. Here, data from 20 individual public monitoring data sources were extracted, curated for chemical and medium, and harmonized into a sustainable machine-readable data format for support of exposure assessments.
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Affiliation(s)
- Kristin K Isaacs
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA.
| | - Jonathan T Wall
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | | | - Kevin A Hobbie
- ICF International, 2635 Meridian Pkwy #200, Durham, NC, 27713, USA
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Elin Ulrich
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - David Lyons
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Kathie L Dionisio
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Antony J Williams
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | - Christopher Grulke
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27709, USA
| | | | - Josiah McCoy
- ICF International, 2635 Meridian Pkwy #200, Durham, NC, 27713, USA
| | - Charles Bevington
- U.S. Consumer Product Safety Commission 5 Research Place Rockville, Rockville, MD, 20850, USA
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11
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Syeda SR, Khan EA, Padungwatanaroj O, Kuprasertwong N, Tula AK. A perspective on hazardous chemical substitution in consumer products. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100748] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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12
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Li L, Sangion A, Wania F, Armitage JM, Toose L, Hughes L, Arnot JA. Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk. ENVIRONMENTAL HEALTH PERSPECTIVES 2021; 129:127006. [PMID: 34882502 PMCID: PMC8658982 DOI: 10.1289/ehp9372] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
BACKGROUND Large numbers of chemicals require evaluation to determine if their production and use pose potential risks to ecological and human health. For most chemicals, the inadequacy and uncertainty of chemical-specific data severely limit the application of exposure- and risk-based methods for screening-level assessments, priority setting, and effective management. OBJECTIVE We developed and evaluated a holistic, mechanistic modeling framework for ecological and human health assessments to support the safe and sustainable production, use, and disposal of organic chemicals. METHODS We consolidated various models for simulating the PROduction-To-EXposure (PROTEX) continuum with empirical data sets and models for predicting chemical property and use function information to enable high-throughput (HT) exposure and risk estimation. The new PROTEX-HT framework calculates exposure and risk by integrating mechanistic computational modules describing chemical behavior and fate in the socioeconomic system (i.e., life cycle emissions), natural and indoor environments, various ecological receptors, and humans. PROTEX-HT requires only molecular structure and chemical tonnage (i.e., annual production or consumption volume) as input information. We evaluated the PROTEX-HT framework using 95 organic chemicals commercialized in the United States and demonstrated its application in various exposure and risk assessment contexts. RESULTS Seventy-nine percent and 97% of the PROTEX-HT human exposure predictions were within one and two orders of magnitude, respectively, of independent human exposure estimates inferred from biomonitoring data. PROTEX-HT supported screening and ranking chemicals based on various exposure and risk metrics, setting chemical-specific maximum allowable tonnage based on user-defined toxicological thresholds, and identifying the most relevant emission sources, environmental media, and exposure routes of concern in the PROTEX continuum. The case study shows that high chemical tonnage did not necessarily result in high exposure or health risks. CONCLUSION Requiring only two chemical-specific pieces of information, PROTEX-HT enables efficient screening-level evaluations of existing and premanufacture chemicals in various exposure- and risk-based contexts. https://doi.org/10.1289/EHP9372.
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Affiliation(s)
- Li Li
- School of Public Health, University of Nevada, Reno, Reno, Nevada, USA
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Alessandro Sangion
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
| | - Frank Wania
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | | | - Liisa Toose
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
| | - Lauren Hughes
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
| | - Jon A. Arnot
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- ARC Arnot Research and Consulting, Toronto, Ontario, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
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13
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Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182212105. [PMID: 34831860 PMCID: PMC8622387 DOI: 10.3390/ijerph182212105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/02/2021] [Accepted: 11/16/2021] [Indexed: 12/24/2022]
Abstract
The prevalence of allergic diseases is regarded as one of the key challenges in health worldwide. Although the precise mechanisms underlying this rapid increase in prevalence are unknown, emerging evidence suggests that genetic and environmental factors play a significant role. The immune system, microbiota, viruses, and bacteria have all been linked to the onset of allergy disorders in recent years. Avoiding allergen exposure is the best treatment option; however, steroids, antihistamines, and other symptom-relieving drugs are also used. Allergen bioinformatics encompasses both computational tools/methods and allergen-related data resources for managing, archiving, and analyzing allergological data. This study highlights allergy-promoting mechanisms, algorithms, and concepts in allergen bioinformatics, as well as major areas for future research in the field of allergology.
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Tornero-Velez R, Isaacs K, Dionisio K, Prince S, Laws H, Nye M, Price PS, Buckley TJ. Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2021; 41:1716-1735. [PMID: 33331033 PMCID: PMC8734486 DOI: 10.1111/risa.13650] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 10/20/2020] [Accepted: 11/15/2020] [Indexed: 05/08/2023]
Abstract
The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.
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Affiliation(s)
- Rogelio Tornero-Velez
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - Kristen 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
| | - 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
| | - Steven Prince
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - Hanna Laws
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
| | - Michael Nye
- U.S. Environmental Protection Agency, Region 8 Denver, 1595 Wynkoop Street, Denver, CO 80202
| | - 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
| | - Timothy J Buckley
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709
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15
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 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] [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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16
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Sharma N, Patiyal S, Dhall A, Devi NL, Raghava GPS. ChAlPred: A web server for prediction of allergenicity of chemical compounds. Comput Biol Med 2021; 136:104746. [PMID: 34388468 DOI: 10.1016/j.compbiomed.2021.104746] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/04/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Allergy is the abrupt reaction of the immune system that may occur after the exposure to allergens such as proteins, peptides, or chemicals. In the past, various methods have been generated for predicting allergenicity of proteins and peptides. In contrast, there is no method that can predict allergenic potential of chemicals. In this paper, we described a method ChAlPred developed for predicting chemical allergens as well as for designing chemical analogs with desired allergenicity. METHOD In this study, we have used 403 allergenic and 1074 non-allergenic chemical compounds obtained from IEDB database. The PaDEL software was used to compute the molecular descriptors of the chemical compounds to develop different prediction models. All the models were trained and tested on the 80% training data and evaluated on the 20% validation data using the 2D, 3D and FP descriptors. RESULTS In this study, we have developed different prediction models using several machine learning approaches. It was observed that the Random Forest based model developed using hybrid descriptors performed the best, and achieved the maximum accuracy of 83.39% and AUC of 0.93 on validation dataset. The fingerprint analysis of the dataset indicates that certain chemical fingerprints are more abundant in allergens that include PubChemFP129 and GraphFP1014. We have also predicted allergenicity potential of FDA-approved drugs using our best model and identified the drugs causing allergic symptoms (e.g., Cefuroxime, Spironolactone, Tioconazole). Our results agreed with allergenicity of these drugs reported in literature. CONCLUSIONS To aid the research community, we developed a smart-device compatible web server ChAlPred (https://webs.iiitd.edu.in/raghava/chalpred/) that allows to predict and design the chemicals with allergenic properties.
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Affiliation(s)
- Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Naorem Leimarembi Devi
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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17
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Aurisano N, Huang L, Milà I Canals L, Jolliet O, Fantke P. Chemicals of concern in plastic toys. ENVIRONMENT INTERNATIONAL 2021; 146:106194. [PMID: 33115697 DOI: 10.1016/j.envint.2020.106194] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/04/2020] [Accepted: 10/05/2020] [Indexed: 05/24/2023]
Abstract
We present a list of Chemicals of Concern (CoCs) in plastic toys. We started from available studies reporting chemical composition of toys to group plastic materials, as well as to gather mass fractions and function of chemicals in these materials. Chemical emissions from plastic toys and subsequent human exposures were then estimated using a series of models and a coupled near-field and far-field exposure assessment framework. Comparing human doses with reference doses shows high Hazard Quotients of up to 387 and cancer risk calculated using cancer slope factors of up to 0.0005. Plasticizers in soft plastic materials show the highest risk, with 31 out of the 126 chemicals identified as CoCs, with sum of Hazard Quotients >1 or child cancer risk >10-6. Our results indicate that a relevant amount of chemicals used in plastic toy materials may pose a non-negligible health risk to children, calling for more refined investigations and more human- and eco-friendly alternatives. The 126 chemicals identified as CoCs were compared with other existing regulatory prioritization lists. While some of our chemicals appear in other lists, we also identified additional priority chemicals that are not yet covered elsewhere and thus require further attention. We finally derive for all considered chemicals the maximum Acceptable Chemical Content (ACC) in the grouped toy plastic materials as powerful green chemistry tool to check whether chemical alternatives could create substantial risks.
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Affiliation(s)
- Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark
| | - Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Llorenç Milà I Canals
- Economy Division, United Nations Environment Programme, 1 Rue de Miollis, 75015 Paris, France
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark.
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18
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Sy MM, Garcia-Hidalgo E, Jung C, Lindtner O, von Goetz N, Greiner M. Analysis of consumer behavior for the estimation of the exposure to chemicals in personal care products. Food Chem Toxicol 2020; 140:111320. [PMID: 32302718 DOI: 10.1016/j.fct.2020.111320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 04/01/2020] [Accepted: 04/02/2020] [Indexed: 02/06/2023]
Abstract
In this study, the main objective was to implement an integrative modelling framework in order to support the prioritization and screening of chemicals present in personal care products (PCPs) regarding their potential to expose users across multiple possible pathways. Here, we implemented an exposure-based framework based on product intake fractions (PiFs) calculated using a two-compartment model reproducing the skin uptake and the competing volatilization of chemicals applied on skin during PCP use. The implemented framework enabled to simultaneously and comprehensively accommodate coupled chemical specific parameters (i.e. physical and chemical properties of the candidate chemicals), exposure information specific for product-chemical combinations, and survey data informing on consumer behavior. A case-study, based on the usage pattern data of 22 PCPs investigated among Swiss individuals (Garcia-Hidalgo et al., 2017a) and 113 candidate chemicals chosen for their suspected presence in the PCP categories of interest was defined to evaluate the applicability of the framework. Nonnegative matrix factorization (NMF) and hierarchical clustering were subsequently applied to identify chemicals with the highest exposure potential and to highlight most relevant mixtures of chemicals on the basis of the specific usage patterns of the considered survey individuals.
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Affiliation(s)
- Mouhamadou M Sy
- German Federal Institute for Risk Assessment (BfR), Exposure Department, Max-Dohrn Str. 8-10, 10589, Berlin, Germany.
| | | | - Christian Jung
- German Federal Institute for Risk Assessment (BfR), Exposure Department, Max-Dohrn Str. 8-10, 10589, Berlin, Germany
| | - Oliver Lindtner
- German Federal Institute for Risk Assessment (BfR), Exposure Department, Max-Dohrn Str. 8-10, 10589, Berlin, Germany
| | - Natalie von Goetz
- Swiss Federal Institute of Technology (ETH) Zurich, 8093, Zurich, Switzerland
| | - Matthias Greiner
- German Federal Institute for Risk Assessment (BfR), Exposure Department, Max-Dohrn Str. 8-10, 10589, Berlin, Germany
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19
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Meyer DE, Bailin SC, Vallero D, Egeghy PP, Liu SV, Cohen Hubal EA. Enhancing life cycle chemical exposure assessment through ontology modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 712:136263. [PMID: 32050401 PMCID: PMC7453614 DOI: 10.1016/j.scitotenv.2019.136263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/18/2019] [Accepted: 12/20/2019] [Indexed: 05/22/2023]
Abstract
In its 2014 report, A Framework Guide for the Selection of Chemical Alternatives, the National Academy of Sciences placed increased emphasis on comparative exposure assessment throughout the life cycle (i.e., from manufacturing to end-of-life) of a chemical. The inclusion of the full life cycle greatly increases the data demands for exposure assessments, including both the quantity and type of data. High throughput tools for exposure estimation add to this challenge by requiring rapid accessibility to data. In this work, ontology modeling was used to bridge the domains of exposure modeling and life cycle inventory modeling to facilitate data sharing and integration. The exposure ontology, ExO, is extended to describe human exposure to consumer products, while an inventory modeling ontology, LciO, is formulated to support automated data mining. The core ontology pieces are connected using a bridging ontology and discussed through a theoretical example to demonstrate how data from LCA can be leveraged to support rapid exposure modeling within a life cycle context.
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Affiliation(s)
- David E Meyer
- U.S. Environmental Protection Agency, Center for Environmental Solutions and Emergency Response, 26 West Martin Luther King Drive, Cincinnati, OH 45268, United States.
| | - Sidney C Bailin
- Knowledge Evolution, Inc., 1748 Seaton Street NW, Washington, DC 20009, United States
| | - Daniel Vallero
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Drive, Durham, NC 27709, United States
| | - Peter P Egeghy
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Drive, Durham, NC 27709, United States
| | - Shi V Liu
- U.S. Environmental Protection Agency, Center for Public Health and Environmental Assessment, 109 TW Alexander Drive, Durham, NC 27709, United States
| | - Elaine A Cohen Hubal
- U.S. Environmental Protection Agency, Center for Public Health and Environmental Assessment, 109 TW Alexander Drive, Durham, NC 27709, United States
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20
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Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, Wambaugh JF, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch Toxicol 2019; 94:1-58. [DOI: 10.1007/s00204-019-02613-4] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022]
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21
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Tickner J, Jacobs M, Malloy T, Buck T, Stone A, Blake A, Edwards S. Advancing alternatives assessment for safer chemical substitution: A research and practice agenda. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2019; 15:855-866. [PMID: 30117284 DOI: 10.1002/ieam.4094] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 05/29/2018] [Accepted: 08/13/2018] [Indexed: 06/08/2023]
Abstract
Alternatives assessment has emerged as a science policy field that supports the evaluation and adoption of safer chemistries in manufacturing processes and consumer products. The recent surge in the development and practice of alternatives assessment has revealed notable methodological challenges. Spurred by this need, we convened an informal community of practice comprising industry experts, academics, and scientists within government and nongovernmental organizations to prioritize a research and practice agenda for the next 5 years that, if implemented, would significantly advance the field of alternatives assessment. With input from over 40 experts, the agenda outlines specific needs to advance methods, tools, and guidance in 5 critical areas: hazard assessment, comparative exposure characterization, life cycle considerations, decision making, and professional practice. Fifteen research and practice needs were identified, ranging from relatively simple efforts to define a minimum hazard data set to the development of more complex performance and decision-analytic methods and data integration tools. Some research needs involve adapting existing approaches to the alternatives assessment context, while others will require the development of entirely new methods and tools. The proposed research and practice agenda is ambitious. Implementing it will require expanding the current network of researchers from academia, government, and industry, as well as increased funding for methodological, application, and evaluation research. Integr Environ Assess Manag 2018;00:000-000. © 2018 SETAC.
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Affiliation(s)
- Joel Tickner
- University of Massachusetts Lowell, Department of Public Health, Lowell, Massachusetts, USA
- Lowell Center for Sustainable Production, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Molly Jacobs
- University of Massachusetts Lowell, Department of Public Health, Lowell, Massachusetts, USA
- Lowell Center for Sustainable Production, University of Massachusetts Lowell, Lowell, Massachusetts, USA
| | - Tim Malloy
- University of California, Los Angeles, School of Law, Los Angeles, California, USA
| | - Topher Buck
- Northeast Waste Management Officials' Association, Interstate Chemicals Clearinghouse, Boston, Massachusetts, USA
| | - Alex Stone
- Washington Department of Ecology, Lacey, Washington, USA
| | - Ann Blake
- Environmental and Public Health Consulting, Alameda, California, USA
| | - Sally Edwards
- Lowell Center for Sustainable Production, University of Massachusetts Lowell, Lowell, Massachusetts, USA
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22
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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] [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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Li D, Suh S. Health risks of chemicals in consumer products: A review. ENVIRONMENT INTERNATIONAL 2019; 123:580-587. [PMID: 30622082 DOI: 10.1016/j.envint.2018.12.033] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/01/2018] [Accepted: 12/15/2018] [Indexed: 06/09/2023]
Abstract
Increasingly diverse chemicals are used in consumer products, while our understanding of their exposure pathways and associated human health risks still lags behind. This paper aims to identify the dominant patterns of exposure pathways and associated health risks of chemicals used in consumer products reported in the peer-reviewed literature. We analyzed 342 articles covering 202 unique chemicals, and distilled the information on the functional uses, product applications, exposure routes, exposure pathways, toxicity endpoints and their combinations. We found that the volume of the literature addressing human health risks of chemicals in consumer products is increasing. Among others, phthalates, bisphenol-A, and polybrominated diphenyl ethers were the most frequently discussed chemical groups in the literature reviewed. Emerged from our review were a number of frequently reported functional use/product application combinations, including plasticizers, polymers/monomers, and flame retardants used in food contact products, personal care products, cosmetics, furniture, flooring, and electronics. We also observed a strong tendency that the number of publications on a chemical surges following major regulatory changes or exposure incidents associated with the chemical. We highlight the need to develop the capacity and the mechanism through which human health risks of chemicals in consumer products can be identified prior to their releases.
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Affiliation(s)
- Dingsheng Li
- Bren School of Environmental Science & Management, University of California Santa Barbara, Santa Barbara, CA, United States; School of Community Health Sciences, University of Nevada, Reno, NV, United States
| | - Sangwon Suh
- Bren School of Environmental Science & Management, University of California Santa Barbara, Santa Barbara, CA, United States.
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Bolinius DJ, Sobek A, Löf MF, Undeman E. Evaluating the consumption of chemical products and articles as proxies for diffuse emissions to the environment. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:1427-1440. [PMID: 30207349 DOI: 10.1039/c8em00270c] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this study we have evaluated the use of consumption of manufactured products (chemical products and articles) in the EU as proxies for diffuse emissions of chemicals to the environment. The content of chemical products is relatively well known. However, the content of articles (products defined by their shape rather than their composition) is less known and currently has to be estimated from chemicals that are known to occur in a small set of materials, such as plastics, that are part of the articles. Using trade and production data from Eurostat in combination with product composition data from a database on chemical content in materials (the Commodity Guide), we were able to calculate trends in the apparent consumption and in-use stocks for 768 chemicals in the EU for the period 2003-2016. The results showed that changes in the apparent consumption of these chemicals over time are smaller than in the consumption of corresponding products in which the chemicals are present. In general, our results suggest that little change in chemical consumption has occurred over the timespan studied, partly due to the financial crisis in 2008 which led to a sudden drop in the consumption, and partly due to the fact that each of the chemicals studied is present in a wide variety of products. Estimated in-use stocks of chemicals show an increasing trend over time, indicating that the mass of chemicals in articles in the EU, that could potentially be released to the environment, is increasing. The quantitative results from this study are associated with large uncertainties due to limitations of the available data. These limitations are highlighted in this study and further underline the current lack of transparency on chemicals in articles. Recommendations on how to address these limitations are also discussed.
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Affiliation(s)
- Damien J Bolinius
- Baltic Sea Centre, Stockholm University, SE-106 91 Stockholm, Sweden.
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26
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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. THE SCIENCE OF THE TOTAL ENVIRONMENT 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] [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. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2018; 28:411-426. [PMID: 29288256 PMCID: PMC6661898 DOI: 10.1038/s41370-017-0012-y] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [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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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] [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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Isaacs KK, Phillips KA, Biryol D, Dionisio KL, Price PS. Consumer product chemical weight fractions from ingredient lists. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 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] [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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30
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 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] [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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31
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Tao M, Li D, Song R, Suh S, Keller AA. OrganoRelease - A framework for modeling the release of organic chemicals from the use and post-use of consumer products. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 234:751-761. [PMID: 29245149 DOI: 10.1016/j.envpol.2017.11.058] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 11/15/2017] [Accepted: 11/16/2017] [Indexed: 05/03/2023]
Abstract
Chemicals in consumer products have become the focus of recent regulatory developments including California's Safer Consumer Products Act. However, quantifying the amount of chemicals released during the use and post-use phases of consumer products is challenging, limiting the ability to understand their impacts. Here we present a comprehensive framework, OrganoRelease, for estimating the release of organic chemicals from the use and post-use of consumer products given limited information. First, a novel Chemical Functional Use Classifier estimates functional uses based on chemical structure. Second, the quantity of chemicals entering different product streams is estimated based on market share data of the chemical functional uses. Third, chemical releases are estimated based on either chemical product categories or functional uses by using the Specific Environmental Release Categories and EU Technological Guidance Documents. OrganoRelease connects 19 unique functional uses and 14 product categories across 4 data sources and provides multiple pathways for chemical release estimation. Available user information can be incorporated in the framework at various stages. The Chemical Functional Use Classifier achieved an average accuracy above 84% for nine functional uses, which enables the OrganoRelease to provide release estimates for the chemical, mostly using only the molecular structure. The results can be can be used as input for methods estimating environmental fate and exposure.
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Affiliation(s)
- Mengya Tao
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, 93106, United States.
| | - Dingsheng Li
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, 93106, United States.
| | - Runsheng Song
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, 93106, United States.
| | - Sangwon Suh
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, 93106, United States.
| | - Arturo A Keller
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, 93106, United States.
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Fritsche E, Grandjean P, Crofton KM, Aschner M, Goldberg A, Heinonen T, Hessel EVS, Hogberg HT, Bennekou SH, Lein PJ, Leist M, Mundy WR, Paparella M, Piersma AH, Sachana M, Schmuck G, Solecki R, Terron A, Monnet-Tschudi F, Wilks MF, Witters H, Zurich MG, Bal-Price A. Consensus statement on the need for innovation, transition and implementation of developmental neurotoxicity (DNT) testing for regulatory purposes. Toxicol Appl Pharmacol 2018; 354:3-6. [PMID: 29447839 PMCID: PMC6097873 DOI: 10.1016/j.taap.2018.02.004] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 02/09/2018] [Accepted: 02/10/2018] [Indexed: 01/15/2023]
Abstract
This consensus statement voices the agreement of scientific stakeholders from regulatory agencies, academia and industry that a new framework needs adopting for assessment of chemicals with the potential to disrupt brain development. An increased prevalence of neurodevelopmental disorders in children has been observed that cannot solely be explained by genetics and recently pre- and postnatal exposure to environmental chemicals has been suspected as a causal factor. There is only very limited information on neurodevelopmental toxicity, leaving thousands of chemicals, that are present in the environment, with high uncertainty concerning their developmental neurotoxicity (DNT) potential. Closing this data gap with the current test guideline approach is not feasible, because the in vivo bioassays are far too resource-intensive concerning time, money and number of animals. A variety of in vitro methods are now available, that have the potential to close this data gap by permitting mode-of-action-based DNT testing employing human stem cells-derived neuronal/glial models. In vitro DNT data together with in silico approaches will in the future allow development of predictive models for DNT effects. The ultimate application goals of these new approach methods for DNT testing are their usage for different regulatory purposes. An increased prevalence of neurodevelopmental disorders in children is observed. There is very limited information on neurodevelopmental toxicity (DNT) induced by environmental chemicals. A new framework is required for assessment of chemicals with the potential to disrupt brain development. In vitro DNT data together with in silico approaches should be used for regulatory purposes.
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Affiliation(s)
- Ellen Fritsche
- IUF - Leibniz Research Institute for Environmental Medicine, Duesseldorf, Germany
| | - Philippe Grandjean
- University of Southern Denmark, Harvard T.H. Chan School of Public Health, USA
| | | | | | - Alan Goldberg
- Bloomberg School of Public Health, Founding Director (Emeritus) of Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, USA; Global Food Ethics, Johns Hopkins University, Baltimore, USA
| | - Tuula Heinonen
- Finnish Centre for Alternative Methods (FICAM), University of Tampere, Tampere, Finland
| | - Ellen V S Hessel
- National Institute for Public Health and the Environment, RIVM Center for Health Protection, Bilthoven, Netherlands
| | - Helena T Hogberg
- Centre for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Baltimore, USA
| | | | - Pamela J Lein
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, USA
| | - Marcel Leist
- CAAT - Centre for Alternatives to Animal Testing, University of Konstanz, Konstanz, Germany
| | | | | | - Aldert H Piersma
- RIVM Center for Health Protection, Bilthoven and Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands
| | - Magdalini Sachana
- Organisation for Economic Co-operation and Development (OECD), Paris, France
| | | | - Roland Solecki
- Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | | | | | - Martin F Wilks
- SCAHT - Swiss Centre for Applied Human Toxicology, University of Basel, Basel, Switzerland
| | - Hilda Witters
- VITO, Flemish Institute for Technological Research, Unit Environmental Risk and Health, Belgium
| | | | - Anna Bal-Price
- European Commission -DG Joint Research Centre (JRC), Ispra, Italy.
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Williams AJ, Grulke CM, Edwards J, McEachran AD, Mansouri K, Baker NC, Patlewicz G, Shah I, Wambaugh JF, Judson RS, Richard AM. The CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J Cheminform 2017; 9:61. [PMID: 29185060 PMCID: PMC5705535 DOI: 10.1186/s13321-017-0247-6] [Citation(s) in RCA: 533] [Impact Index Per Article: 76.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Accepted: 11/18/2017] [Indexed: 11/10/2022] Open
Abstract
Despite an abundance of online databases providing access to chemical data, there is increasing demand for high-quality, structure-curated, open data to meet the various needs of the environmental sciences and computational toxicology communities. The U.S. Environmental Protection Agency's (EPA) web-based CompTox Chemistry Dashboard is addressing these needs by integrating diverse types of relevant domain data through a cheminformatics layer, built upon a database of curated substances linked to chemical structures. These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. Batch searching allows for direct chemical identifier (ID) mapping and downloading of multiple data streams in several different formats. This facilitates fast access to available structure, property, toxicity, and bioassay data for collections of chemicals (hundreds to thousands at a time). Advanced search capabilities are available to support, for example, non-targeted analysis and identification of chemicals using mass spectrometry. The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA's DSSTox project. DSSTox chemical content is subject to strict quality controls to enforce consistency among chemical substance-structure identifiers, as well as list curation review to ensure accurate linkages of DSSTox substances to chemical lists and associated data. The Dashboard, publicly launched in April 2016, has expanded considerably in content and user traffic over the past year. It is continuously evolving with the growth of DSSTox into high-interest or data-rich domains of interest to EPA, such as chemicals on the Toxic Substances Control Act listing, while providing the user community with a flexible and dynamic web-based platform for integration, processing, visualization and delivery of data and resources. The Dashboard provides support for a broad array of research and regulatory programs across the worldwide community of toxicologists and environmental scientists.
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Affiliation(s)
- Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | - Christopher M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | - Jeff Edwards
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | | | - Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN USA
- ScitoVation LLC, Research Triangle Park, NC USA
| | | | - Grace Patlewicz
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | - John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
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Ernstoff AS, Fantke P, Huang L, Jolliet O. High-throughput migration modelling for estimating exposure to chemicals in food packaging in screening and prioritization tools. Food Chem Toxicol 2017; 109:428-438. [DOI: 10.1016/j.fct.2017.09.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 06/09/2017] [Accepted: 09/14/2017] [Indexed: 11/29/2022]
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Biryol D, Nicolas CI, Wambaugh J, Phillips K, Isaacs K. High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization. ENVIRONMENT INTERNATIONAL 2017; 108:185-194. [PMID: 28865378 PMCID: PMC5894819 DOI: 10.1016/j.envint.2017.08.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 08/07/2017] [Accepted: 08/08/2017] [Indexed: 05/21/2023]
Abstract
Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C0) and chemical properties. The most predictive variables in the resulting model were C0, molecular weight, log Kow, and food type (R2=0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C0 based on the functional role of chemicals in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R2=0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority-setting.
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Affiliation(s)
- Derya Biryol
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 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, NC 27709, United States
| | - Chantel I Nicolas
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States; 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
| | - John 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
| | - 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, United States
| | - Kristin 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.
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Moreau M, Leonard J, Phillips KA, Campbell J, Pendse SN, Nicolas C, Phillips M, Yoon M, Tan YM, Smith S, Pudukodu H, Isaacs K, Clewell H. Using exposure prediction tools to link exposure and dosimetry for risk-based decisions: A case study with phthalates. CHEMOSPHERE 2017; 184:1194-1201. [PMID: 28672700 PMCID: PMC6084441 DOI: 10.1016/j.chemosphere.2017.06.098] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 06/15/2017] [Accepted: 06/23/2017] [Indexed: 05/22/2023]
Abstract
A few different exposure prediction tools were evaluated for use in the new in vitro-based safety assessment paradigm using di-2-ethylhexyl phthalate (DEHP) and dibutyl phthalate (DnBP) as case compounds. Daily intake of each phthalate was estimated using both high-throughput (HT) prediction models such as the HT Stochastic Human Exposure and Dose Simulation model (SHEDS-HT) and the ExpoCast heuristic model and non-HT approaches based on chemical specific exposure estimations in the environment in conjunction with human exposure factors. Reverse dosimetry was performed using a published physiologically based pharmacokinetic (PBPK) model for phthalates and their metabolites to provide a comparison point. Daily intakes of DEHP and DnBP were estimated based on the urinary concentrations of their respective monoesters, mono-2-ethylhexyl phthalate (MEHP) and monobutyl phthalate (MnBP), reported in NHANES (2011-2012). The PBPK-reverse dosimetry estimated daily intakes at the 50th and 95th percentiles were 0.68 and 9.58 μg/kg/d and 0.089 and 0.68 μg/kg/d for DEHP and DnBP, respectively. For DEHP, the estimated median from PBPK-reverse dosimetry was about 3.6-fold higher than the ExpoCast estimate (0.68 and 0.18 μg/kg/d, respectively). For DnBP, the estimated median was similar to that predicted by ExpoCast (0.089 and 0.094 μg/kg/d, respectively). The SHEDS-HT prediction of DnBP intake from consumer product pathways alone was higher at 0.67 μg/kg/d. The PBPK-reverse dosimetry-estimated median intake of DEHP and DnBP was comparable to values previously reported for US populations. These comparisons provide insights into establishing criteria for selecting appropriate exposure prediction tools for use in an integrated modeling platform to link exposure to health effects.
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Affiliation(s)
- Marjory Moreau
- Scitovation, 6 Davis Drive, Durham, NC 27709, United States
| | - Jeremy Leonard
- Oak Ridge Institute for Science and Education, 1299 Bethel Valley Rd, Oak Ridge, TN 37830, United States
| | - Katherine A Phillips
- National Exposure Research Laboratory, US Environmental Protection Agency, 109 TW Alexander Dr, Durham, NC 27709, United States
| | - Jerry Campbell
- Ramboll Environ, 6 Davis Drive, Durham, NC 27709, United States
| | - Salil N Pendse
- Scitovation, 6 Davis Drive, Durham, NC 27709, United States
| | | | | | - Miyoung Yoon
- Scitovation, 6 Davis Drive, Durham, NC 27709, United States.
| | - Yu-Mei Tan
- National Exposure Research Laboratory, US Environmental Protection Agency, 109 TW Alexander Dr, Durham, NC 27709, United States.
| | - Sherrie Smith
- North Carolina State University, Raleigh, NC 27695, United States
| | - Harish Pudukodu
- North Carolina State University, Raleigh, NC 27695, United States
| | - Kristin Isaacs
- National Exposure Research Laboratory, US Environmental Protection Agency, 109 TW Alexander Dr, Durham, NC 27709, United States
| | - Harvey Clewell
- Scitovation, 6 Davis Drive, Durham, NC 27709, United States
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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 CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2017; 19:1063-1074. [PMID: 30505234 PMCID: PMC6260937 DOI: 10.1039/c6gc02744j] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [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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