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Truong KT, Wambaugh JF, Kapraun DF, Davidson-Fritz SE, Eytcheson S, Judson RS, Paul Friedman K. Interpretation of thyroid-relevant bioactivity data for comparison to in vivo exposures: A prioritization approach for putative chemical inhibitors of in vitro deiodinase activity. Toxicology 2025; 515:154157. [PMID: 40262668 DOI: 10.1016/j.tox.2025.154157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2025] [Revised: 04/16/2025] [Accepted: 04/17/2025] [Indexed: 04/24/2025]
Abstract
Many ToxCast assay endpoints can be mapped to molecular initiating events (MIEs) within the thyroid adverse outcome pathway (AOP) network. Herein, we provide a framework for interpretation of thyroid-relevant bioactivity data across MIEs. As a proof-of-concept, we used ToxCast data on the inhibition of deiodinase (DIO) enzymes, which convert thyroid hormones between active and inactive forms, and identified substances most likely to inhibit DIO enzymes. Data from 4 relevant cell-free in vitro assays are available for > 2000 chemicals in single concentration screening and 327 chemicals in multi-concentration screening. We filtered to identify chemicals that demonstrated inhibition for each DIO enzyme less likely to be confounded by assay interference, refining the list of putatively active chemicals from 523 to 135. In vitro bioactivity data were then used to estimate administered equivalent doses (AEDs) using a novel high-throughput toxicokinetic (HTTK) model for in vitro to in vivo extrapolation (IVIVE) of dose. To consider potential thyroid-disrupting activity in an appropriate life-stage and dose context, we extended an existing human maternal-fetal HTTK model to allow for simulations involving the first trimester of pregnancy. For many chemicals, using modeled fetal tissue concentrations produced lower AED estimates than using modeled maternal plasma concentrations alone, at least partially due to conservative assumptions in our HTTK model of complete gestation. This extensible approach for MIE groups of thyroid-related bioactivity data from ToxCast may inform further screening or analyses for potential adverse outcomes during pregnancy and development.
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Affiliation(s)
- K T Truong
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency (multiple locations), Washington, DC, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - J F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency (multiple locations), Washington, DC, USA
| | - D F Kapraun
- Center for Public Health and Environmental AssessmentUS EPA, Research Triangle Park, NC 27711, USA
| | - S E Davidson-Fritz
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency (multiple locations), Washington, DC, USA
| | - S Eytcheson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency (multiple locations), Washington, DC, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - R S Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency (multiple locations), Washington, DC, USA
| | - K Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency (multiple locations), Washington, DC, USA.
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2
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Charest N, Sinclair G, Eytcheson SA, Chang DT, Martin TM, Lowe CN, Paul Friedman K, Williams AJ. Combined In Vitro and In Silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity. J Chem Inf Model 2025; 65:4426-4441. [PMID: 40273369 DOI: 10.1021/acs.jcim.5c00713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2025]
Abstract
New approach methodologies (NAMs) are an increasing priority in the field of toxicology to fill data gaps and reduce time and resources in chemical safety assessment. We describe an NAMs workflow that integrates an in vitro high-throughput bioassay with an in silico computational model. In defining this workflow, we propose, as a crucial step of in silico development, the identification of explicit "purpose contexts": a priori definitions of the scope and intent of an in silico solution, which provide natural targets for the mechanistic interpretation, validation, and output design of the model. By inspecting data from an in vitro assay measuring the displacement of fluorescent probe 8-anilino-1-naphthalenesulfonic acid (ANSA) from the serum transport protein transthyretin (TTR) as a proxy for potential disruption of thyroxine (T4) binding, in collaboration with the experimenters, we developed three relevant purpose contexts for this in silico modeling effort: (1) examination and confirmation of the in vitro assay principle via orthogonal information, (2) immediate integration with the in vitro experimental cycle to reduce costs and enhance hit rates, and (3) ultimate replacement of the use of single-concentration screening as a prioritization strategy for bioactivity testing of bulk chemical libraries. From these purpose contexts, we derived the foundations of a robust and transparent quantitative structure-activity relationship (QSAR) model that is constructively fit for purpose, characterized by first-principles mechanistic analysis, strict data quality evaluation, contextually rigorous performance testing and, finally, delivery of a quantitative recommendation schedule to simultaneously improve in vitro hit rates and in silico model learning potential.
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Affiliation(s)
- Nathaniel Charest
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Gabriel Sinclair
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Stephanie A Eytcheson
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Daniel T Chang
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Todd M Martin
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Charles N Lowe
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Katie Paul Friedman
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
| | - Antony J Williams
- Office of Research and Development, Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, 109 TW Alexander Dr., Research Triangle Park, North Carolina 27711, United States
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3
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Paul Friedman K, Thomas RS, Wambaugh JF, Harrill JA, Judson RS, Shafer TJ, Williams AJ, Lee JYJ, Loo LH, Gagné M, Long AS, Barton-Maclaren TS, Whelan M, Bouhifd M, Rasenberg M, Simanainen U, Sobanski T. Integration of new approach methods for the assessment of data-poor chemicals. Toxicol Sci 2025; 205:74-105. [PMID: 39969258 DOI: 10.1093/toxsci/kfaf019] [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] [Indexed: 02/20/2025] Open
Abstract
The use of new approach methods (NAMs), including high-throughput, in vitro bioactivity data, in setting a point-of-departure (POD) will accelerate the pace of human health hazard assessments. Combining hazard and exposure predictions into a bioactivity:exposure ratio (BER) for use in risk-based prioritization and utilizing NAM-based bioactivity flags to indicate potential hazards of interest for further prediction or mechanism-based screening together comprise a prospective approach for management of substances with limited traditional toxicity testing data. In this work, we demonstrate a NAM-based assessment case study conducted via the Accelerating the Pace of Chemical Risk Assessment initiative, a consortium of international research and regulatory scientists. The primary objective was to develop a reusable and adaptable approach for addressing chemicals with limited traditional toxicity data using a NAM-based POD, BER, and bioactivity-based flags for indication of putative endocrine, developmental, neurological, and immunosuppressive effects via data generation and interpretation for 200 substances. Multiple data streams, including in silico and in vitro NAMs, were used. High-throughput transcriptomics and phenotypic profiling data, as well as targeted biochemical and cell-based assays, were combined with generic high-throughput toxicokinetic models parameterized with chemical-specific data to estimate dose for comparison to exposure predictions. This case study further enables regulatory scientists from different international purviews to utilize efficient approaches for prospective chemical management, addressing hazard and risk-based data needs, while reducing the need for animal studies. This work demonstrates the feasibility of using a battery of toxicodynamic and toxicokinetic NAMs to provide a NAM-based POD for screening-level assessment.
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Affiliation(s)
- Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - Russell S Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - Timothy J Shafer
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC 27711, United States
| | - Jia-Ying Joey Lee
- Innovations in Food and Chemical Safety Programme and Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
| | - Lit-Hsin Loo
- Innovations in Food and Chemical Safety Programme and Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
| | - Matthew Gagné
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Alexandra S Long
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Tara S Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada
| | - Maurice Whelan
- Joint Research Centre (JRC), European Commission, Ispra (VA) 21047, Italy
| | - Mounir Bouhifd
- Directorate of Prioritisation and Integration, European Chemicals Agency (ECHA), Helsinki 00121, Finland
| | - Mike Rasenberg
- Directorate of Hazard Assessment, European Chemicals Agency (ECHA), Helsinki 00121, Finland
| | - Ulla Simanainen
- Directorate of Prioritisation and Integration, European Chemicals Agency (ECHA), Helsinki 00121, Finland
| | - Tomasz Sobanski
- Directorate of Prioritisation and Integration, European Chemicals Agency (ECHA), Helsinki 00121, Finland
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4
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Davidson-Fritz SE, Ring CL, Evans MV, Schacht CM, Chang X, Breen M, Honda GS, Kenyon E, Linakis MW, Meade A, Pearce RG, Sfeir MA, Sluka JP, Devito MJ, Wambaugh JF. Enabling transparent toxicokinetic modeling for public health risk assessment. PLoS One 2025; 20:e0321321. [PMID: 40238721 PMCID: PMC12002443 DOI: 10.1371/journal.pone.0321321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Accepted: 03/04/2025] [Indexed: 04/18/2025] Open
Abstract
Toxicokinetic modeling describes the absorption, distribution, metabolism, and elimination of chemicals by the body. Chemical-specific in vivo toxicokinetic data is often unavailable for the thousands of chemicals in commerce. However, predictions from generalized toxicokinetic models allow for extrapolation from in vitro toxicological data, obtained via new approach methods (NAMs), to predict in vivo human health outcomes and provide key information on chemicals for public health risk assessment. The httk R package provides an open-source software tool containing a suite of generalized toxicokinetic models covering various exposure scenarios, a library of chemical-specific data from peer-reviewed high-throughput toxicokinetic (HTTK) studies, and other utility functions to parameterize and evaluate toxicokinetic models. Generalized HTTK models in httk use the open-source language MCSim to describe the compartmental and physiologically based toxicokinetics (PBTK). New HTTK models may be integrated into httk with a model description code file (C script generated via MCSim) and a model documentation file (R script). httk provides a series of functionalities such as model parameterization, in vivo-derived data for evaluating model predictions, unit conversion, Monte Carlo simulations for uncertainty propagation and biological variability, and other model utilities. Here, we describe in detail how to add new HTTK models into the httk package to leverage its pre-existing data and functionality. As a demonstration, we describe the integration of a gas inhalation PBTK model. The intention of httk is to provide a transparent, open-source tool for toxicokinetics, bioinformatics, and public health risk assessment that makes use of publicly available data on more than one thousand chemicals.
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Affiliation(s)
- Sarah E. Davidson-Fritz
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Cincinnati, Ohio, United States of America
| | - Caroline L. Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | - Marina V. Evans
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | - Celia M. Schacht
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America
| | - Xiaoqing Chang
- Inotiv, Research Triangle Park, North Carolina, United States of America
| | - Miyuki Breen
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | - Gregory S. Honda
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America
| | - Elaina Kenyon
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | | | - Annabel Meade
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America
| | - Robert G. Pearce
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America
| | - Mark A. Sfeir
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, United States of America
| | - James P. Sluka
- Department of Intelligent Systems Engineering and Biocomplexity Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Michael J. Devito
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
| | - John F. Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
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5
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Rager JE, Koval LE, Hickman E, Ring C, Teitelbaum T, Cohen T, Fragola G, Zylka MJ, Engel LS, Lu K, Engel SM. The environmental neuroactive chemicals list of prioritized substances for human biomonitoring and neurotoxicity testing: A database and high-throughput toxicokinetics approach. ENVIRONMENTAL RESEARCH 2025; 266:120537. [PMID: 39638029 PMCID: PMC11753932 DOI: 10.1016/j.envres.2024.120537] [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: 05/03/2024] [Revised: 10/01/2024] [Accepted: 12/02/2024] [Indexed: 12/07/2024]
Abstract
There is a diversity of chemicals to which humans are potentially exposed. Few of these chemicals have linked human biomonitoring data, and most have very limited neurotoxicity testing. Of particular concern are environmental exposures impacting children, who constitute a population of heightened susceptibility due to rapid neural growth and plasticity, yet lack biomonitoring data compared to other age/population subgroups. This study set out to develop a prioritized list of neuroactive substances, titled the Environmental NeuRoactIve CHemicals (ENRICH) list, to be used as a defined screening library in the evaluation of human biological samples, with emphasis on early childhood-relevant environmental exposures. In silico database mining approaches were used to prioritize chemicals based upon likelihood of neuroactivity, human exposure, and feasible detection in biological samples. Evidence of neuroactivity was compiled across in vitro high-throughput screening, animal testing, and/or human epidemiological findings. Chemicals were considered for their likelihood of human exposure and detection presence in biological samples (including metabolites), with additional evidence indicating presence within child-relevant products. The resulting list of 1827 chemicals were ranked using a Chemical Prioritization Index. Manual inclusion/exclusion criteria were employed for the top-ranking chemical candidates to ensure that chemicals were within the study's scope (i.e., environmentally relevant) and, for the purposes of biomonitoring, had properties amenable to mass spectrometry methods. These elements were combined to produce the ENRICH list of 250 top-ranking chemicals, spanning pesticides and those used in home maintenance, personal care, cleaning products, vehicles, arts and crafts, and consumer electronics, among other sources. Chemicals were additionally evaluated for high-throughput toxicokinetics to predict how much of a chemical and/or its metabolite(s) may reach urine, as an example biological matrix for practical use in biomonitoring efforts. This novel study couples databases and in silico-based predictions to prioritize chemicals in the environment with potential neurological impacts for future study.
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Affiliation(s)
- Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Center for Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina at Chapel Hill, 116 Manning Drive, CB #7325, Chapel Hill, NC, 27599, USA; Curriculum in Toxicology and Environmental Medicine, School of Medicine the University of North Carolina at Chapel Hill, Chapel Hill, 116 Manning Drive, CB #7325, Chapel Hill, NC, 27599, USA.
| | - Lauren E Koval
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA
| | - Elise Hickman
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Center for Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, The University of North Carolina at Chapel Hill, 116 Manning Drive, CB #7325, Chapel Hill, NC, 27599, USA; Curriculum in Toxicology and Environmental Medicine, School of Medicine the University of North Carolina at Chapel Hill, Chapel Hill, 116 Manning Drive, CB #7325, Chapel Hill, NC, 27599, USA
| | - Caroline Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, United States Environmental Protection Agency, 109 T.W. Alexander Drive, Mail Drop D143-02, PO Box 12055, Research Triangle Park, NC, 27711, USA
| | - Taylor Teitelbaum
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA
| | - Todd Cohen
- Department of Neurology, School of Medicine, The University of North Carolina at Chapel Hill, 115 Mason Farm Road, CB #7250, Chapel Hill, NC, USA; Department of Cell Biology and Physiology, School of Medicine, The University of North Carolina at Chapel Hill, 111 Mason Farm Road, CB #7545, Chapel Hill, NC, USA; UNC Neuroscience Center, School of Medicine, The University of North Carolina at Chapel Hill, 116 Manning Drive, CB #7250, Chapel Hill, NC, USA
| | - Giulia Fragola
- Department of Neurology, School of Medicine, The University of North Carolina at Chapel Hill, 115 Mason Farm Road, CB #7250, Chapel Hill, NC, USA
| | - Mark J Zylka
- Department of Cell Biology and Physiology, School of Medicine, The University of North Carolina at Chapel Hill, 111 Mason Farm Road, CB #7545, Chapel Hill, NC, USA; UNC Neuroscience Center, School of Medicine, The University of North Carolina at Chapel Hill, 116 Manning Drive, CB #7250, Chapel Hill, NC, USA
| | - Lawrence S Engel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, CB #7435, Chapel Hill, NC, 27599, USA
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Institute for Environmental Health Solutions, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, 135 Dauer Drive, CB #7431, Chapel Hill, NC, 27599, USA; Curriculum in Toxicology and Environmental Medicine, School of Medicine the University of North Carolina at Chapel Hill, Chapel Hill, 116 Manning Drive, CB #7325, Chapel Hill, NC, 27599, USA
| | - Stephanie M Engel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, CB #7435, Chapel Hill, NC, 27599, USA
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6
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Lu EH, Rusyn I, Chiu WA. Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now! JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2025; 28:28-62. [PMID: 39390665 PMCID: PMC11614695 DOI: 10.1080/10937404.2024.2412571] [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] [Indexed: 10/12/2024]
Abstract
Regulatory dose-response assessments traditionally rely on in vivo data and default assumptions. New Approach Methods (NAMs) present considerable opportunities to both augment traditional dose-response assessments and accelerate the evaluation of new/data-poor chemicals. This review aimed to determine the potential utilization of NAMs through a unified conceptual framework that compartmentalizes derivation of toxicity values into five sequential Key Dose-response Modules (KDMs): (1) point-of-departure (POD) determination, (2) test system-to-human (e.g. inter-species) toxicokinetics and (3) toxicodynamics, (4) human population (intra-species) variability in toxicodynamics, and (5) toxicokinetics. After using several "traditional" dose-response assessments to illustrate this framework, a review is presented where existing NAMs, including in silico, in vitro, and in vivo approaches, might be applied across KDMs. Further, the false dichotomy between "traditional" and NAMs-derived data sources is broken down by organizing dose-response assessments into a matrix where each KDM has Tiers of increasing precision and confidence: Tier 0: Default/generic values, Tier 1: Computational predictions, Tier 2: Surrogate measurements, and Tier 3: Direct measurements. These findings demonstrated that although many publications promote the use of NAMs in KDMs (1) for POD determination and (5) for human population toxicokinetics, the proposed matrix of KDMs and Tiers reveals additional immediate opportunities for NAMs to be integrated across other KDMs. Further, critical needs were identified for developing NAMs to improve in vitro dosimetry and quantify test system and human population toxicodynamics. Overall, broadening the integration of NAMs across the steps of dose-response assessment promises to yield higher throughput, less animal-dependent, and more science-based toxicity values for protecting human health.
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Affiliation(s)
- En-Hsuan Lu
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, United States of America
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, United States of America
| | - Weihsueh A. Chiu
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843, United States of America
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7
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Jia T, Liu W, Keller AA, Gao L, Xu X, Wu W, Wang X, Yu Y, Zhao G, Li B, Deng J, Mao T, Chen C. Potential impact of organophosphate esters on thyroid eye disease based on machine learning and molecular docking. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 957:177835. [PMID: 39631328 DOI: 10.1016/j.scitotenv.2024.177835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 11/07/2024] [Accepted: 11/27/2024] [Indexed: 12/07/2024]
Abstract
Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in daily commodities and building materials. Some OPEs, acting as agonists of the thyroid-stimulating hormone receptor (TSHR), may contribute to the development of thyroid eye disease (TED). This study analyzes the serum and urine of patients and control groups, using machine learning and molecular docking to investigate the potential impact of OPEs on TED. Results indicate significantly higher concentrations of OPEs and di-OPEs of TED patients compared to controls (Mann-Whitney U test, p < 0.05). Aryl OPEs exhibit the strongest binding affinity with TSHR. We developed a predictive model for OPE-TSHR affinity to explore the impact of OPE structural features on TSHR activity and effectively capture the complex relationships between changes in OPE side chains and their effects on TSHR. Predictions from the USEPA's database indicate that 28 % of 1011 OPEs have a tendency to bind with TSHR. Furthermore, a high-accuracy classification model successfully identified key substructures associated with high affinity for TSHR. This study not only enhances our understanding of the complex relationship between the structural diversity of OPEs and their thyroid impact but also offers molecular design insights to prevent releasing OPEs with high thyroid harm potential into the environment.
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Affiliation(s)
- Tianqi Jia
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China; Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, USA
| | - Wenbin Liu
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China; Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China..
| | - Arturo A Keller
- Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, USA.
| | - Lirong Gao
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China; Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Xiaotian Xu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Wenqi Wu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Xiaoxia Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yang Yu
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Guang Zhao
- Department of Clinical Laboratory, 989th Hospital of the Joint Logistic Support Force of the PLA, Luoyang 471031, China
| | - Baohui Li
- Department of Clinical Laboratory, 989th Hospital of the Joint Logistic Support Force of the PLA, Luoyang 471031, China
| | - Jinglin Deng
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Tianao Mao
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Chunci Chen
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
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8
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Agea MI, Čmelo I, Dehaen W, Chen Y, Kirchmair J, Sedlák D, Bartůněk P, Šícho M, Svozil D. Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library. Mol Inform 2024; 43:e202300316. [PMID: 38979783 DOI: 10.1002/minf.202300316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 07/10/2024]
Abstract
Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.
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Affiliation(s)
- M Isabel Agea
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Ivan Čmelo
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Wim Dehaen
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
- Department of Organic Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146, Hamburg, Germany
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090, Vienna, Austria
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146, Hamburg, Germany
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090, Vienna, Austria
| | - David Sedlák
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, 14220, Czech Republic
| | - Petr Bartůněk
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, 14220, Czech Republic
| | - Martin Šícho
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
| | - Daniel Svozil
- Department of Informatics and Chemistry & CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, 16628, Czech Republic
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, 14220, Czech Republic
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9
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Bianchi E, Costa E, Harrill J, Deford P, LaRocca J, Chen W, Sutake Z, Lehman A, Pappas-Garton A, Sherer E, Moreillon C, Sriram S, Dhroso A, Johnson K. Discovery Phase Agrochemical Predictive Safety Assessment Using High Content In Vitro Data to Estimate an In Vivo Toxicity Point of Departure. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024. [PMID: 39033510 DOI: 10.1021/acs.jafc.4c03094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.
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Affiliation(s)
- Enrica Bianchi
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | | | - Joshua Harrill
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, Research Triangle Park ,North Carolina 27709, United States
| | - Paul Deford
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Jessica LaRocca
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Wei Chen
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Zachary Sutake
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Audrey Lehman
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | | | - Eric Sherer
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | | | | | - Andi Dhroso
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
| | - Kamin Johnson
- Corteva Agriscience, Indianapolis ,Indiana 46268, United States
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10
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Cao H, Peng J, Zhou Z, Yang Z, Wang L, Sun Y, Wang Y, Liang Y. Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17762-17773. [PMID: 36282672 DOI: 10.1021/acs.est.2c04400] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
More than 7000 per- and polyfluorinated alkyl substances (PFAS) have been documented in the U.S. Environmental Protection Agency's CompTox Chemicals database. These PFAS can be used in a broad range of industrial and consumer applications but may pose potential environmental issues and health risks. However, little is known about emerging PFAS bioaccumulation to assess their chemical safety. This study focuses specifically on the large and high-quality data set of fluorochemicals from the related environmental and pharmaceutical chemicals databases, and machine learning (ML) models were developed for the classification prediction of the unbound fraction of compounds in plasma. A comprehensive evaluation of the ML models shows that the best blending model yields an accuracy of 0.901 for the test set. The predictions suggest that most PFAS (∼92%) have a high binding fraction in plasma. Introduction of alkaline amino groups is likely to reduce the binding affinities of PFAS with plasma proteins. Molecular dynamics simulations indicate a clear distinction between the high and low binding fractions of PFAS. These computational workflows can be used to predict the bioaccumulation of emerging PFAS and are also helpful for the molecular design of PFAS to prevent the release of high-bioaccumulation compounds into the environment.
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Affiliation(s)
- Huiming Cao
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Jianhua Peng
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zhen Zhou
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Zeguo Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ling Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yuzhen Sun
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yawei Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
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11
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Nyffeler J, Willis C, Harris FR, Foster MJ, Chambers B, Culbreth M, Brockway RE, Davidson-Fritz S, Dawson D, Shah I, Friedman KP, Chang D, Everett LJ, Wambaugh JF, Patlewicz G, Harrill JA. Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments. Toxicol Appl Pharmacol 2023; 468:116513. [PMID: 37044265 PMCID: PMC11917499 DOI: 10.1016/j.taap.2023.116513] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/03/2023] [Accepted: 04/08/2023] [Indexed: 04/14/2023]
Abstract
'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ∼100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.
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Affiliation(s)
- Jo Nyffeler
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, TN 37831, United States of America
| | - Clinton Willis
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Felix R Harris
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - M J Foster
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - Bryant Chambers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Megan Culbreth
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard E Brockway
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - Sarah Davidson-Fritz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Daniel Dawson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Katie Paul Friedman
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Dan Chang
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Logan J Everett
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - John F Wambaugh
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Grace Patlewicz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Joshua A Harrill
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
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12
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Kreutz A, Clifton MS, Henderson WM, Smeltz MG, Phillips M, Wambaugh JF, Wetmore BA. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. TOXICS 2023; 11:toxics11050463. [PMID: 37235277 DOI: 10.3390/toxics11050463] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023]
Abstract
Concern over per- and polyfluoroalkyl substances (PFAS) has increased as more is learned about their environmental presence, persistence, and bioaccumulative potential. The limited monitoring, toxicokinetic (TK), and toxicologic data available are inadequate to inform risk across this diverse domain. Here, 73 PFAS were selected for in vitro TK evaluation to expand knowledge across lesser-studied PFAS alcohols, amides, and acrylates. Targeted methods developed using gas chromatography-tandem mass spectrometry (GC-MS/MS) were used to measure human plasma protein binding and hepatocyte clearance. Forty-three PFAS were successfully evaluated in plasma, with fraction unbound (fup) values ranging from 0.004 to 1. With a median fup of 0.09 (i.e., 91% bound), these PFAS are highly bound but exhibit 10-fold lower binding than legacy perfluoroalkyl acids recently evaluated. Thirty PFAS evaluated in the hepatocyte clearance assay showed abiotic loss, with many exceeding 60% loss within 60 min. Metabolic clearance was noted for 11 of the 13 that were successfully evaluated, with rates up to 49.9 μL/(min × million cells). The chemical transformation simulator revealed potential (bio)transformation products to consider. This effort provides critical information to evaluate PFAS for which volatility, metabolism, and other routes of transformation are likely to modulate their environmental fates.
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Affiliation(s)
- Anna Kreutz
- Oak Ridge Institute for Science and Education, 1299 Bethel Valley Road, Oak Ridge, TN 37830, USA
| | - Matthew S Clifton
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Research Triangle Park, NC 27711, USA
| | - W Matthew Henderson
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Athens, GA 30605, USA
| | - Marci G Smeltz
- US Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Research Triangle Park, NC 27711, USA
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC 27711, USA
| | - Matthew Phillips
- Oak Ridge Associated Universities, 100 ORAU Way, Oak Ridge, TN 37830, USA
| | - John F Wambaugh
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC 27711, USA
| | - Barbara A Wetmore
- US Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Research Triangle Park, NC 27711, USA
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13
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Bluemlein K, Nowak N, Ellinghusen B, Gerling S, Badorrek P, Hansen T, Hohlfeld JM, Paul R, Schuchardt S. Occupational exposure to veterinary antibiotics: Pharmacokinetics of enrofloxacin in humans after dermal, inhalation and oral uptake - A clinical study. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2023; 100:104139. [PMID: 37142072 DOI: 10.1016/j.etap.2023.104139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 04/24/2023] [Accepted: 04/29/2023] [Indexed: 05/06/2023]
Abstract
Occupational exposure to veterinary antibiotics in hen houses at poultry feeding farms was demonstrated by biomonitoring campaigns in the past. The objective of this study was to investigate pharmacokinetics of three uptake routes: dermal, oral and inhaled. In an open-label cross-over study, six healthy volunteers were exposed to single occupational relevant doses of enrofloxacin. Plasma and urine samples were analysed for enrofloxacin and ciprofloxacin. Physiologically based pharmacokinetic (PBPK) modelling based on bioanalysis data showed underestimation for the elimination rate in comparison to experimental data pointing towards a lack of sufficient ADME information and limitations of available physico-chemical properties of the parent drug. The data obtained in this study indicate that oral uptake with its various sources, e.g. airborne enrofloxacin, direct hand-mouth contact, is the major source for occupational exposure to enrofloxacin in hen houses. Dermal exposure was considered negligible.
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Affiliation(s)
- Katharina Bluemlein
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany
| | - Norman Nowak
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany
| | - Birthe Ellinghusen
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany
| | - Susanne Gerling
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany
| | - Philipp Badorrek
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany
| | - Tanja Hansen
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany
| | - Jens M Hohlfeld
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany; Department of Respiratory Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany; German Centre of Lung Research (DZL-BREATH), Hannover, Germany
| | - Roland Paul
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin Gruppe 4.2 - Medizinischer Arbeitsschutz, Biomonitoring, Nöldnerstraße 40/42, 10317 Berlin, Germany
| | - Sven Schuchardt
- Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, Nikolai-Fuchs-Str. 1, 30625 Hannover, Germany.
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14
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LaLone CA, Blatz DJ, Jensen MA, Vliet SM, Mayasich S, Mattingly KZ, Transue TR, Melendez W, Wilkinson A, Simmons CW, Ng C, Zhang C, Zhang Y. From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2023; 42:463-474. [PMID: 36524855 PMCID: PMC11265300 DOI: 10.1002/etc.5537] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Computational screening for potentially bioactive molecules using advanced molecular modeling approaches including molecular docking and molecular dynamic simulation is mainstream in certain fields like drug discovery. Significant advances in computationally predicting protein structures from sequence information have also expanded the availability of structures for nonmodel species. Therefore, the objective of the present study was to develop an analysis pipeline to harness the power of these bioinformatics approaches for cross-species extrapolation for evaluating chemical safety. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool compares protein-sequence similarity across species for conservation of known chemical targets, providing an initial line of evidence for extrapolation of toxicity knowledge. However, with the development of structural models from tools like the Iterative Threading ASSEmbly Refinement (ITASSER), analyses of protein structural conservation can be included to add further lines of evidence and generate protein models across species. Models generated through such a pipeline could then be used for advanced molecular modeling approaches in the context of species extrapolation. Two case examples illustrating this pipeline from SeqAPASS sequences to I-TASSER-generated protein structures were created for human liver fatty acid-binding protein (LFABP) and androgen receptor (AR). Ninety-nine LFABP and 268 AR protein models representing diverse species were generated and analyzed for conservation using template modeling (TM)-align. The results from the structural comparisons were in line with the sequence-based SeqAPASS workflow, adding further evidence of LFABL and AR conservation across vertebrate species. The present study lays the foundation for expanding the capabilities of the web-based SeqAPASS tool to include structural comparisons for species extrapolation, facilitating more rapid and efficient toxicological assessments among species with limited or no existing toxicity data. Environ Toxicol Chem 2023;42:463-474. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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Affiliation(s)
- Carlie A. LaLone
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Donovan J. Blatz
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- Oak Ridge Institute for Science and Education, Duluth, Minnesota, USA
| | - Marissa A. Jensen
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- University of Minnesota Duluth, Swenson College of Science and Engineering, Department of Biology, Duluth, Minnesota, USA
| | - Sara M.F. Vliet
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Duluth, Minnesota, USA
| | - Sally Mayasich
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- University of Wisconsin-Madison Aquatic Sciences Center at U.S. EPA Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Kali Z. Mattingly
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
- SpecPro Professional Services, Contractor to U.S. EPA Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, USA
| | - Thomas R. Transue
- Congruence Therapeutics, Chapel Hill, NC, USA
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Wilson Melendez
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Audrey Wilkinson
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Cody W. Simmons
- General Dynamics Information Technology, Research Triangle Park, North Carolina, USA
| | - Carla Ng
- Departments of Civil & Environmental Engineering and Environmental and Occupational Health, University of Pittsburgh
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109 USA
- Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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15
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Chou WC, Lin Z. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicol Sci 2023; 191:1-14. [PMID: 36156156 PMCID: PMC9887681 DOI: 10.1093/toxsci/kfac101] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.
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Affiliation(s)
- Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA
| | - Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA
- Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32608, USA
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16
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Dawson DE, Lau C, Pradeep P, Sayre RR, Judson RS, Tornero-Velez R, Wambaugh JF. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. TOXICS 2023; 11:98. [PMID: 36850973 PMCID: PMC9962572 DOI: 10.3390/toxics11020098] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/09/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.
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Affiliation(s)
- Daniel E. Dawson
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Christopher Lau
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, 109 T.W. Alexander Drive, Research Triangle Park, NC 277011, USA
| | - Prachi Pradeep
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Oak Ridge Institutes for Science and Education, Oak Ridge, TN 37830, USA
| | - Risa R. Sayre
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Richard S. Judson
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Rogelio Tornero-Velez
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
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17
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Boyce M, Favela KA, Bonzo JA, Chao A, Lizarraga LE, Moody LR, Owens EO, Patlewicz G, Shah I, Sobus JR, Thomas RS, Williams AJ, Yau A, Wambaugh JF. Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis. FRONTIERS IN TOXICOLOGY 2023; 5:1051483. [PMID: 36742129 PMCID: PMC9889941 DOI: 10.3389/ftox.2023.1051483] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/03/2023] [Indexed: 01/19/2023] Open
Abstract
Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.
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Affiliation(s)
- Matthew Boyce
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | | | - Jessica A. Bonzo
- Thermo Fisher Scientific, South San Francisco, CA, United States
| | - Alex Chao
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Lucina E. Lizarraga
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH, United States
| | - Laura R. Moody
- Thermo Fisher Scientific, South San Francisco, CA, United States
| | - Elizabeth O. Owens
- Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH, United States
| | - Grace Patlewicz
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Imran Shah
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Jon R. Sobus
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Russell S. Thomas
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Antony J. Williams
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States
| | - Alice Yau
- Southwest Research Institute, San Antonio, TX, United States
| | - John F. Wambaugh
- Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States,*Correspondence: John F. Wambaugh,
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18
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Sabbioni G, Castaño A, Esteban López M, Göen T, Mol H, Riou M, Tagne-Fotso R. Literature review and evaluation of biomarkers, matrices and analytical methods for chemicals selected in the research program Human Biomonitoring for the European Union (HBM4EU). ENVIRONMENT INTERNATIONAL 2022; 169:107458. [PMID: 36179646 DOI: 10.1016/j.envint.2022.107458] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
Humans are potentially exposed to a large amount of chemicals present in the environment and in the workplace. In the European Human Biomonitoring initiative (Human Biomonitoring for the European Union = HBM4EU), acrylamide, mycotoxins (aflatoxin B1, deoxynivalenol, fumonisin B1), diisocyanates (4,4'-methylenediphenyl diisocyanate, 2,4- and 2,6-toluene diisocyanate), and pyrethroids were included among the prioritized chemicals of concern for human health. For the present literature review, the analytical methods used in worldwide biomonitoring studies for these compounds were collected and presented in comprehensive tables, including the following parameter: determined biomarker, matrix, sample amount, work-up procedure, available laboratory quality assurance and quality assessment information, analytical techniques, and limit of detection. Based on the data presented in these tables, the most suitable methods were recommended. According to the paradigm of biomonitoring, the information about two different biomarkers of exposure was evaluated: a) internal dose = parent compounds and metabolites in urine and blood; and b) the biologically effective = dose measured as blood protein adducts. Urine was the preferred matrix used for deoxynivalenol, fumonisin B1, and pyrethroids (biomarkers of internal dose). Markers of the biological effective dose were determined as hemoglobin adducts for diisocyanates and acrylamide, and as serum-albumin-adducts of aflatoxin B1 and diisocyanates. The analyses and quantitation of the protein adducts in blood or the metabolites in urine were mostly performed with LC-MS/MS or GC-MS in the presence of isotope-labeled internal standards. This review also addresses the critical aspects of the application, use and selection of biomarkers. For future biomonitoring studies, a more comprehensive approach is discussed to broaden the selection of compounds.
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Affiliation(s)
- Gabriele Sabbioni
- Università della Svizzera Italiana (USI), Research and Transfer Service, Lugano, Switzerland; Institute of Environmental and Occupational Toxicology, Airolo, Switzerland; Walther-Straub-Institute for Pharmacology and Toxicology, Ludwig-Maximilians-University Munich, Munich, Germany.
| | - Argelia Castaño
- National Centre for Environmental Health, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain.
| | - Marta Esteban López
- National Centre for Environmental Health, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain.
| | - Thomas Göen
- Institute and Outpatient Clinic of Occupational, Social and Environmental Medicine, Friedrich-Alexander Universität Erlangen-Nürnberg (IPASUM), Erlangen, Germany.
| | - Hans Mol
- Wageningen Food Safety Research, Part of Wageningen University & Research, Wageningen, the Netherlands.
| | - Margaux Riou
- Department of Environmental and Occupational Health, Santé publique France, The National Public Health Agency, Saint-Maurice, France.
| | - Romuald Tagne-Fotso
- Department of Environmental and Occupational Health, Santé publique France, The National Public Health Agency, Saint-Maurice, France.
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19
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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: 22] [Impact Index Per Article: 7.3] [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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20
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Nicolas CI, Linakis MW, Minto MS, Mansouri K, Clewell RA, Yoon M, Wambaugh JF, Patlewicz G, McMullen PD, Andersen ME, Clewell III HJ. Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods. Front Pharmacol 2022; 13:980747. [PMID: 36278238 PMCID: PMC9586287 DOI: 10.3389/fphar.2022.980747] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA’s ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA’s ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.
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Affiliation(s)
- Chantel I. Nicolas
- Office of Chemical Safety and Pollution Prevention, US EPA, Washington, DC, United States
| | | | | | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, United States
| | | | | | - John F. Wambaugh
- Center for Computational Toxicology and Exposure Office of Research and Development, US EPA, Research Triangle Park, NC, United States
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure Office of Research and Development, US EPA, Research Triangle Park, NC, United States
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21
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Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF. Evaluation of a rapid, generic human gestational dose model. Reprod Toxicol 2022; 113:172-188. [PMID: 36122840 PMCID: PMC9761697 DOI: 10.1016/j.reprotox.2022.09.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 08/30/2022] [Accepted: 09/14/2022] [Indexed: 10/14/2022]
Abstract
Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.
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Affiliation(s)
- Dustin F Kapraun
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Mark Sfeir
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Robert G Pearce
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Sarah E Davidson-Fritz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Annie Lumen
- National Center for Toxicological Research, US Food and Drug Administration, USA
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
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22
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Najjar A, Punt A, Wambaugh J, Paini A, Ellison C, Fragki S, Bianchi E, Zhang F, Westerhout J, Mueller D, Li H, Shi Q, Gant TW, Botham P, Bars R, Piersma A, van Ravenzwaay B, Kramer NI. Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment. Arch Toxicol 2022; 96:3407-3419. [PMID: 36063173 PMCID: PMC9584981 DOI: 10.1007/s00204-022-03356-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/03/2022] [Indexed: 11/28/2022]
Abstract
With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.
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Affiliation(s)
| | - Ans Punt
- Wageningen Food Safety Research, Wageningen, The Netherlands
| | - John Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC USA
| | | | | | - Styliani Fragki
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | | | - Joost Westerhout
- The Netherlands Organisation for Applied Scientific Research TNO, Utrecht, The Netherlands
| | - Dennis Mueller
- Research and Development, Crop Science, Bayer AG, Monheim, Germany
| | - Hequn Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire UK
| | - Quan Shi
- Shell Global Solutions International B.V, The Hague, The Netherlands
| | - Timothy W. Gant
- School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Phil Botham
- Syngenta, Jealott’s Hill, Bracknell, Berkshire UK
| | - Rémi Bars
- Crop Science Division, Bayer S.A.S., Sophia Antipolis, France
| | - Aldert Piersma
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - Nynke I. Kramer
- Toxicology Division, Wageningen University, PO Box 8000, 6700 EA Wageningen, The Netherlands
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23
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Jeong J, Choi J. Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7532-7543. [PMID: 35666838 DOI: 10.1021/acs.est.1c07413] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.
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Affiliation(s)
- Jaeseong Jeong
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, South Korea
| | - Jinhee Choi
- School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, South Korea
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24
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Chang X, Tan YM, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, Kleinstreuer NC, Lumen A, Matheson J, Paini A, Pangburn HA, Petersen EJ, Reinke EN, Ribeiro AJS, Sipes N, Sweeney LM, Wambaugh JF, Wange R, Wetmore BA, Mumtaz M. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. TOXICS 2022; 10:232. [PMID: 35622645 PMCID: PMC9143724 DOI: 10.3390/toxics10050232] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/24/2022] [Indexed: 02/04/2023]
Abstract
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.
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Affiliation(s)
- Xiaoqing Chang
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, 109 T.W. Alexander Drive, Durham, NC 27709, USA;
| | - David G. Allen
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Shannon Bell
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Paul C. Brown
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Lauren Browning
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Patricia Ceger
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Jeffery Gearhart
- The Henry M. Jackson Foundation, Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Pertti J. Hakkinen
- National Library of Medicine, National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA;
| | - Shruti V. Kabadi
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Additive Safety, 5001 Campus Drive, HFS-275, College Park, MD 20740, USA;
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC 27709, USA;
| | - Annie Lumen
- U.S. Food and Drug Administration, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA;
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, Division of Toxicology and Risk Assessment, 5 Research Place, Rockville, MD 20850, USA;
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy;
| | - Heather A. Pangburn
- Air Force Research Laboratory, 711 Human Performance Wing, 2729 R Street, Area B, Building 837, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Elijah J. Petersen
- U.S. Department of Commerce, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA;
| | - Emily N. Reinke
- U.S. Army Public Health Center, 8252 Blackhawk Rd., Aberdeen Proving Ground, MD 21010, USA;
| | - Alexandre J. S. Ribeiro
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Nisha Sipes
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Lisa M. Sweeney
- UES, Inc., 4401 Dayton-Xenia Road, Beavercreek, OH 45432, Assigned to Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Ronald Wange
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Barbara A. Wetmore
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, Office of the Associate Director for Science, 1600 Clifton Road, S102-2, Atlanta, GA 30333, USA
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Moreau M, Mallick P, Smeltz M, Haider S, Nicolas CI, Pendse SN, Leonard JA, Linakis MW, McMullen PD, Clewell RA, Clewell HJ, Yoon M. Considerations for Improving Metabolism Predictions for In Vitro to In Vivo Extrapolation. FRONTIERS IN TOXICOLOGY 2022; 4:894569. [PMID: 35573278 PMCID: PMC9099212 DOI: 10.3389/ftox.2022.894569] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/13/2022] [Indexed: 12/14/2022] Open
Abstract
High-throughput (HT) in vitro to in vivo extrapolation (IVIVE) is an integral component in new approach method (NAM)-based risk assessment paradigms, for rapidly translating in vitro toxicity assay results into the context of in vivo exposure. When coupled with rapid exposure predictions, HT-IVIVE supports the use of HT in vitro assays for risk-based chemical prioritization. However, the reliability of prioritization based on HT bioactivity data and HT-IVIVE can be limited as the domain of applicability of current HT-IVIVE is generally restricted to intrinsic clearance measured primarily in pharmaceutical compounds. Further, current approaches only consider parent chemical toxicity. These limitations occur because current state-of-the-art HT prediction tools for clearance and metabolite kinetics do not provide reliable data to support HT-IVIVE. This paper discusses current challenges in implementation of IVIVE for prioritization and risk assessment and recommends a path forward for addressing the most pressing needs and expanding the utility of IVIVE.
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Affiliation(s)
- Marjory Moreau
- ScitoVation, LLC, Durham, NC, United States
- *Correspondence: Marjory Moreau,
| | | | | | | | | | | | - Jeremy A. Leonard
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, United States
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EFSA Panel on Plant Protection Products and their Residues (EFSA PPR Panel), Hernandez‐Jerez AF, Adriaanse P, Aldrich A, Berny P, Coja T, Duquesne S, Focks A, Marinovich M, Millet M, Pelkonen O, Pieper S, Tiktak A, Topping CJ, Widenfalk A, Wilks M, Wolterink G, Gundert‐Remy U, Louisse J, Rudaz S, Testai E, Lostia A, Dorne J, Parra Morte JM. Scientific Opinion of the Scientific Panel on Plant Protection Products and their Residues (PPR Panel) on testing and interpretation of comparative in vitro metabolism studies. EFSA J 2021; 19:e06970. [PMID: 34987623 PMCID: PMC8696562 DOI: 10.2903/j.efsa.2021.6970] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
EFSA asked the Panel on Plant Protection Products and their residues to deliver a Scientific Opinion on testing and interpretation of comparative in vitro metabolism studies for both new active substances and existing ones. The main aim of comparative in vitro metabolism studies of pesticide active substances is to evaluate whether all significant metabolites formed in the human in vitro test system, as a surrogate of the in vivo situation, are also present at comparable level in animal species tested in toxicological studies and, therefore, if their potential toxicity has been appropriately covered by animal studies. The studies may also help to decide which animal model, with regard to a particular compound, is the most relevant for humans. In the experimental strategy, primary hepatocytes in suspension or culture are recommended since hepatocytes are considered the most representative in vitro system for prediction of in vivo metabolites. The experimental design of 3 × 3 × 3 (concentrations, time points, technical replicates, on pooled hepatocytes) will maximise the chance to identify unique (UHM) and disproportionate (DHM) human metabolites. When DHM and UHM are being assessed, test item-related radioactivity recovery and metabolite profile are the most important parameters. Subsequently, structural characterisation of the assigned metabolites is performed with appropriate analytical techniques. In toxicological assessment of metabolites, the uncertainty factor approach is the first alternative to testing option, followed by new approach methodologies (QSAR, read-across, in vitro methods), and only if these fail, in vivo animal toxicity studies may be performed. Knowledge of in vitro metabolites in human and animal hepatocytes would enable toxicological evaluation of all metabolites of concern, and, furthermore, add useful pieces of information for detection and evaluation of metabolites in different matrices (crops, livestock, environment), improve biomonitoring efforts via better toxicokinetic understanding, and ultimately, develop regulatory schemes employing physiologically based or physiology-mimicking in silico and/or in vitro test systems to anticipate the exposure of humans to potentially hazardous substances in plant protection products.
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Abstract
Chemicals are measured regularly in air, food, the environment, and the workplace. Biomonitoring of chemicals in biological fluids is a tool to determine the individual exposure. Blood protein adducts of xenobiotics are a marker of both exposure and the biologically effective dose. Urinary metabolites and blood metabolites are short term exposure markers. Stable hemoglobin adducts are exposure markers of up to 120 days. Blood protein adducts are formed with many xenobiotics at different sites of the blood proteins. Newer methods apply the techniques developed in the field of proteomics. Larger adducted peptides with 20 amino acids are used for quantitation. Unfortunately, at present the methods do not reach the limits of detection obtained with the methods looking at single amino acid adducts or at chemically cleaved adducts. Therefore, to progress in the field new approaches are needed.
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28
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Tan YM, Barton HA, Boobis A, Brunner R, Clewell H, Cope R, Dawson J, Domoradzki J, Egeghy P, Gulati P, Ingle B, Kleinstreuer N, Lowe K, Lowit A, Mendez E, Miller D, Minucci J, Nguyen J, Paini A, Perron M, Phillips K, Qian H, Ramanarayanan T, Sewell F, Villanueva P, Wambaugh J, Embry M. Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment. Regul Toxicol Pharmacol 2021; 127:105070. [PMID: 34718074 DOI: 10.1016/j.yrtph.2021.105070] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/18/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023]
Abstract
Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.
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Affiliation(s)
- Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | | | | | - Rachel Brunner
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | | | - Rhian Cope
- Australian Pesticides and Veterinary Medicines Authority, Sydney, NSW, Australia
| | - Jeffrey Dawson
- U.S. Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC, USA
| | | | - Peter Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Pankaj Gulati
- Australian Pesticides and Veterinary Medicines Authority, Sydney, NSW, Australia
| | - Brandall Ingle
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Durham, NC, USA
| | - Nicole Kleinstreuer
- National Toxicology Program, Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, NC, USA
| | - Kelly Lowe
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Anna Lowit
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Elizabeth Mendez
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - David Miller
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Jeffrey Minucci
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - James Nguyen
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Alicia Paini
- European Commission, Joint Research Centre, Ispra, Italy
| | - Monique Perron
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - Katherine Phillips
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Hua Qian
- ExxonMobil Biomedical Sciences, Inc., Annandale, NJ, USA
| | | | - Fiona Sewell
- National Centre for the Replacement, Refinement, and Reduction of Animals in Research, London, UK
| | - Philip Villanueva
- U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, USA
| | - John Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Durham, NC, USA
| | - Michelle Embry
- Health and Environmental Sciences Institute, Washington DC, USA.
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29
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Matsuzaka Y, Totoki S, Handa K, Shiota T, Kurosaki K, Uesawa Y. Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System. Int J Mol Sci 2021; 22:10821. [PMID: 34639159 PMCID: PMC8509615 DOI: 10.3390/ijms221910821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/11/2022] Open
Abstract
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.
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Affiliation(s)
- Yasunari Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
- Center for Gene and Cell Therapy, Division of Molecular and Medical Genetics, The Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
| | - Shin Totoki
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Kentaro Handa
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Tetsuyoshi Shiota
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Kota Kurosaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
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30
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Breen M, Ring CL, Kreutz A, Goldsmith MR, Wambaugh JF. High-throughput PBTK models for in vitro to in vivo extrapolation. Expert Opin Drug Metab Toxicol 2021; 17:903-921. [PMID: 34056988 PMCID: PMC9703392 DOI: 10.1080/17425255.2021.1935867] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals. AREAS COVERED This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data. EXPERT OPINION HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.
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Affiliation(s)
- Miyuki Breen
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Caroline L Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Anna Kreutz
- Oak Ridge Institute for Science and Education (ORISE) fellow at the Center for Computational Toxicology and Exposure, Office of Research and Development, Research Triangle Park, NC, USA
| | - Michael-Rock Goldsmith
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA
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