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Kalyva ME, Vist GE, Diemar MG, López-Soop G, Bozada TJ, Luechtefeld T, Roggen EL, Dirven H, Vinken M, Husøy T. Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: A systematic scoping review. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 352:124109. [PMID: 38718961 DOI: 10.1016/j.envpol.2024.124109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024]
Abstract
Exposure assessment is a crucial component of environmental health research, providing essential information on the potential risks associated with various chemicals. A systematic scoping review was conducted to acquire an overview of accessible human exposure assessment methods and computational tools to support and ultimately improve risk assessment. The systematic scoping review was performed in Sysrev, a web platform that introduces machine learning techniques into the review process aiming for increased accuracy and efficiency. Included publications were restricted to a publication date after the year 2000, where exposure methods were properly described. Exposure assessments methods were found to be used for a broad range of environmental chemicals including pesticides, metals, persistent chemicals, volatile organic compounds, and other chemical classes. Our results show that after the year 2000, for all the types of exposure routes, probabilistic analysis, and computational methods to calculate human exposure have increased. Sixty-three mathematical models and toolboxes were identified that have been developed in Europe, North America, and globally. However, only twelve occur frequently and their usefulness were associated with exposure route, chemical classes and input parameters used to estimate exposure. The outcome of the combined associations can function as a basis and/or guide for decision making for the selection of most appropriate method and tool to be used for environmental chemical human exposure assessments in Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment (ONTOX) project and elsewhere. Finally, the choice of input parameters used in each mathematical model and toolbox shown by our analysis can contribute to the harmonization process of the exposure models and tools increasing the prospect for comparison between studies and consistency in the regulatory process in the future.
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Affiliation(s)
- Maria E Kalyva
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway.
| | - Gunn E Vist
- Norwegian Institute of Public Health, Division for Health Services, Oslo, Norway
| | | | - Graciela López-Soop
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway
| | - T J Bozada
- Toxtrack LLC, Baltimore, MD, United States
| | - Thomas Luechtefeld
- Toxtrack LLC, Baltimore, MD, United States; Insilica LLC, Bethesda, MD, United States
| | - Erwin L Roggen
- 3Rs Management and Consulting ApS, Kongens Lyngby, Denmark
| | - Hubert Dirven
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway
| | - Mathieu Vinken
- Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Trine Husøy
- Norwegian Institute of Public Health, Division of Climate and Environmental Health, Oslo, Norway
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Phillips KA, Chao A, Church RL, Favela K, Garantziotis S, Isaacs KK, Meyer B, Rice A, Sayre R, Wetmore BA, Yau A, Wambaugh JF. Suspect Screening Analysis of Pooled Human Serum Samples Using GC × GC/TOF-MS. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1802-1812. [PMID: 38217501 DOI: 10.1021/acs.est.3c05092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.
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Affiliation(s)
- Katherine A Phillips
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Alex Chao
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Rebecca L Church
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Kristin Favela
- Southwest Research Institute, San Antonio, Texas 78238, United States
| | - Stavros Garantziotis
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Brian Meyer
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Annette Rice
- U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Environmental Health Sciences, Clinical Research Unit, Durham, North Carolina 27709, United States
| | - Risa Sayre
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Barbara A Wetmore
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
| | - Alice Yau
- Southwest Research Institute, San Antonio, Texas 78238, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina 27711, United States
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Isaacs KK, Wall JT, Paul Friedman K, Franzosa JA, Goeden H, Williams AJ, Dionisio KL, Lambert JC, Linnenbrink M, Singh A, Wambaugh JF, Bogdan AR, Greene C. Screening for drinking water contaminants of concern using an automated exposure-focused workflow. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024; 34:136-147. [PMID: 37193773 PMCID: PMC11131037 DOI: 10.1038/s41370-023-00552-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The number of chemicals present in the environment exceeds the capacity of government bodies to characterize risk. Therefore, data-informed and reproducible processes are needed for identifying chemicals for further assessment. The Minnesota Department of Health (MDH), under its Contaminants of Emerging Concern (CEC) initiative, uses a standardized process to screen potential drinking water contaminants based on toxicity and exposure potential. OBJECTIVE Recently, MDH partnered with the U.S. Environmental Protection Agency (EPA) Office of Research and Development (ORD) to accelerate the screening process via development of an automated workflow accessing relevant exposure data, including exposure new approach methodologies (NAMs) from ORD's ExpoCast project. METHODS The workflow incorporated information from 27 data sources related to persistence and fate, release potential, water occurrence, and exposure potential, making use of ORD tools for harmonization of chemical names and identifiers. The workflow also incorporated data and criteria specific to Minnesota and MDH's regulatory authority. The collected data were used to score chemicals using quantitative algorithms developed by MDH. The workflow was applied to 1867 case study chemicals, including 82 chemicals that were previously manually evaluated by MDH. RESULTS Evaluation of the automated and manual results for these 82 chemicals indicated reasonable agreement between the scores although agreement depended on data availability; automated scores were lower than manual scores for chemicals with fewer available data. Case study chemicals with high exposure scores included disinfection by-products, pharmaceuticals, consumer product chemicals, per- and polyfluoroalkyl substances, pesticides, and metals. Scores were integrated with in vitro bioactivity data to assess the feasibility of using NAMs for further risk prioritization. SIGNIFICANCE This workflow will allow MDH to accelerate exposure screening and expand the number of chemicals examined, freeing resources for in-depth assessments. The workflow will be useful in screening large libraries of chemicals for candidates for the CEC program.
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Affiliation(s)
- Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA.
| | - Jonathan T Wall
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Jill A Franzosa
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Helen Goeden
- Minnesota Department of Health, 625 Robert St. N, St. Paul, MN, 55155, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Kathie L Dionisio
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Jason C Lambert
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Monica Linnenbrink
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Amar Singh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 TW Alexander Drive, Research Triangle Park, Durham, NC, 27711, USA
| | - Alexander R Bogdan
- Minnesota Department of Health, 625 Robert St. N, St. Paul, MN, 55155, USA
| | - Christopher Greene
- Minnesota Department of Health, 625 Robert St. N, St. Paul, MN, 55155, USA
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Muncke J, Andersson AM, Backhaus T, Belcher SM, Boucher JM, Carney Almroth B, Collins TJ, Geueke B, Groh KJ, Heindel JJ, von Hippel FA, Legler J, Maffini MV, Martin OV, Peterson Myers J, Nadal A, Nerin C, Soto AM, Trasande L, Vandenberg LN, Wagner M, Zimmermann L, Thomas Zoeller R, Scheringer M. A vision for safer food contact materials: Public health concerns as drivers for improved testing. ENVIRONMENT INTERNATIONAL 2023; 180:108161. [PMID: 37758599 DOI: 10.1016/j.envint.2023.108161] [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: 04/14/2023] [Revised: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 09/29/2023]
Abstract
Food contact materials (FCMs) and food contact articles are ubiquitous in today's globalized food system. Chemicals migrate from FCMs into foodstuffs, so called food contact chemicals (FCCs), but current regulatory requirements do not sufficiently protect public health from hazardous FCCs because only individual substances used to make FCMs are tested and mostly only for genotoxicity while endocrine disruption and other hazard properties are disregarded. Indeed, FCMs are a known source of a wide range of hazardous chemicals, and they likely contribute to highly prevalent non-communicable diseases. FCMs can also include non-intentionally added substances (NIAS), which often are unknown and therefore not subject to risk assessment. To address these important shortcomings, we outline how the safety of FCMs may be improved by (1) testing the overall migrate, including (unknown) NIAS, of finished food contact articles, and (2) expanding toxicological testing beyond genotoxicity to multiple endpoints associated with non-communicable diseases relevant to human health. To identify mechanistic endpoints for testing, we group chronic health outcomes associated with chemical exposure into Six Clusters of Disease (SCOD) and we propose that finished food contact articles should be tested for their impacts on these SCOD. Research should focus on developing robust, relevant, and sensitive in-vitro assays based on mechanistic information linked to the SCOD, e.g., through Adverse Outcome Pathways (AOPs) or Key Characteristics of Toxicants. Implementing this vision will improve prevention of chronic diseases that are associated with hazardous chemical exposures, including from FCMs.
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Affiliation(s)
- Jane Muncke
- Food Packaging Forum Foundation, Zurich, Switzerland.
| | - Anna-Maria Andersson
- Dept. of Growth and Reproduction, Rigshospitalet and Centre for Research and Research Training in Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Thomas Backhaus
- Dept of Biological and Environmental Sciences, University of Gothenburg, Sweden
| | - Scott M Belcher
- Dept. of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | | | | | | | - Birgit Geueke
- Food Packaging Forum Foundation, Zurich, Switzerland
| | - Ksenia J Groh
- Department of Environmental Toxicology, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - Jerrold J Heindel
- Healthy Environment and Endocrine Disruptor Strategies, Durham, NC, USA
| | - Frank A von Hippel
- Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Juliette Legler
- Dept. of Population Health Sciences, Faculty of Veterinary Medicine, University of Utrecht, Netherlands
| | | | - Olwenn V Martin
- Plastic Waste Innovation Hub, Department of Arts and Science, University College London, UK
| | - John Peterson Myers
- Dept. of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA; Environmental Health Sciences, Charlottesville, VA, USA
| | - Angel Nadal
- IDiBE and CIBERDEM, Miguel Hernández University of Elche, Alicante, Spain
| | - Cristina Nerin
- Dept. of Analytical Chemistry, I3A, University of Zaragoza, Zaragoza, Spain
| | - Ana M Soto
- Department of Immunology, Tufts University School of Medicine, Boston, MA, USA; Centre Cavaillès, Ecole Normale Supérieure, Paris, France
| | - Leonardo Trasande
- College of Global Public Health and Grossman School of Medicine and Wagner School of Public Service, New York University, New York, NY, USA
| | - Laura N Vandenberg
- Department of Environmental Health Sciences, School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Martin Wagner
- Dept. of Biology, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - R Thomas Zoeller
- Department of Environmental Health Sciences, School of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Martin Scheringer
- RECETOX, Masaryk University, Brno, Czech Republic; Department of Environmental Systems Science, ETH Zurich, Switzerland.
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5
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Buckley TJ, Egeghy PP, Isaacs K, Richard AM, Ring C, Sayre RR, Sobus JR, Thomas RS, Ulrich EM, Wambaugh JF, Williams AJ. Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency. ENVIRONMENT INTERNATIONAL 2023; 178:108097. [PMID: 37478680 PMCID: PMC10588682 DOI: 10.1016/j.envint.2023.108097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.
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Affiliation(s)
- Timothy J Buckley
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States.
| | - Peter P Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Kristin Isaacs
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Caroline Ring
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Risa R Sayre
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Russell S Thomas
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
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Wu L, Chen S, Guo L, Shpyleva S, Harris K, Fahmi T, Flanigan T, Tong W, Xu J, Ren Z. Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review. Regul Toxicol Pharmacol 2022; 137:105287. [PMID: 36372266 DOI: 10.1016/j.yrtph.2022.105287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/18/2022] [Accepted: 11/06/2022] [Indexed: 11/13/2022]
Abstract
In the field of regulatory science, reviewing literature is an essential and important step, which most of the time is conducted by manually reading hundreds of articles. Although this process is highly time-consuming and labor-intensive, most output of this process is not well transformed into machine-readable format. The limited availability of data has largely constrained the artificial intelligence (AI) system development to facilitate this literature reviewing in the regulatory process. In the past decade, AI has revolutionized the area of text mining as many deep learning approaches have been developed to search, annotate, and classify relevant documents. After the great advancement of AI algorithms, a lack of high-quality data instead of the algorithms has recently become the bottleneck of AI system development. Herein, we constructed two large benchmark datasets, Chlorine Efficacy dataset (CHE) and Chlorine Safety dataset (CHS), under a regulatory scenario that sought to assess the antiseptic efficacy and toxicity of chlorine. For each dataset, ∼10,000 scientific articles were initially collected, manually reviewed, and their relevance to the review task were labeled. To ensure high data quality, each paper was labeled by a consensus among multiple experienced reviewers. The overall relevance rate was 27.21% (2,663 of 9,788) for CHE and 7.50% (761 of 10,153) for CHS, respectively. Furthermore, the relevant articles were categorized into five subgroups based on the focus of their content. Next, we developed an attention-based classification language model using these two datasets. The proposed classification model yielded 0.857 and 0.908 of Area Under the Curve (AUC) for CHE and CHS dataset, respectively. This performance was significantly better than permutation test (p < 10E-9), demonstrating that the labeling processes were valid. To conclude, our datasets can be used as benchmark to develop AI systems, which can further facilitate the literature review process in regulatory science.
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Affiliation(s)
- Leihong Wu
- Division of Bioinformatics and Biostatics, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA.
| | - Si Chen
- Division of Biochemical Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Lei Guo
- Division of Biochemical Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Svitlana Shpyleva
- Division of Biochemical Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Kelly Harris
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Tariq Fahmi
- Office of Scientific Coordination, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Timothy Flanigan
- Division of Neurotoxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatics, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Joshua Xu
- Division of Bioinformatics and Biostatics, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA
| | - Zhen Ren
- Division of Biochemical Toxicology, National Center for Toxicological Research, U.S. FDA, Jefferson, AR, 72079, USA.
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7
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Isaacs KK, Egeghy P, Dionisio KL, Phillips KA, Zidek A, Ring C, Sobus JR, Ulrich EM, Wetmore BA, Williams AJ, Wambaugh JF. The chemical landscape of high-throughput new approach methodologies for exposure. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2022; 32:820-832. [PMID: 36435938 PMCID: PMC9882966 DOI: 10.1038/s41370-022-00496-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 05/25/2023]
Abstract
The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.
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Affiliation(s)
- Kristin K Isaacs
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Peter Egeghy
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Kathie L Dionisio
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Katherine A Phillips
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Angelika Zidek
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Caroline Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jon R Sobus
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Elin M Ulrich
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Barbara A Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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