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Mansouri K, Moreira-Filho JT, Lowe CN, Charest N, Martin T, Tkachenko V, Judson R, Conway M, Kleinstreuer NC, Williams AJ. Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling. J Cheminform 2024; 16:19. [PMID: 38378618 PMCID: PMC10880251 DOI: 10.1186/s13321-024-00814-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/10/2024] [Indexed: 02/22/2024] Open
Abstract
The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.
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Affiliation(s)
- Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.
| | - José T Moreira-Filho
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Charles N Lowe
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Nathaniel Charest
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Todd Martin
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | | | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Mike Conway
- National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Nicole C Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
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Shirke AV, Radke EG, Lin C, Blain R, Vetter N, Lemeris C, Hartman P, Hubbard H, Angrish M, Arzuaga X, Congleton J, Davis A, Dishaw LV, Jones R, Judson R, Kaiser JP, Kraft A, Lizarraga L, Noyes PD, Patlewicz G, Taylor M, Williams AJ, Thayer KA, Carlson LM. Expanded Systematic Evidence Map for Hundreds of Per- and Polyfluoroalkyl Substances (PFAS) and Comprehensive PFAS Human Health Dashboard. Environ Health Perspect 2024; 132:26001. [PMID: 38319881 PMCID: PMC10846678 DOI: 10.1289/ehp13423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 02/08/2024]
Abstract
BACKGROUND Per- and polyfluoroalkyl substances (PFAS) encompass a class of chemically and structurally diverse compounds that are extensively used in industry and detected in the environment. The US Environmental Protection Agency (US EPA) 2021 PFAS Strategic Roadmap describes national research plans to address the challenge of PFAS. OBJECTIVES Systematic Evidence Map (SEM) methods were used to survey and summarize available epidemiological and mammalian bioassay evidence that could inform human health hazard identification for a set of 345 PFAS that were identified by the US EPA's Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing and through interagency discussions on PFAS of interest. This work builds from the 2022 evidence map that collated evidence on a separate set of ∼ 150 PFAS. Like our previous work, this SEM does not include PFAS that are the subject of ongoing or completed assessments at the US EPA. METHODS SEM methods were used to search, screen, and inventory mammalian bioassay and epidemiological literature from peer-reviewed and gray literature sources using manual review and machine-learning software. For each included study, study design details and health end points examined were summarized in interactive web-based literature inventories. Some included studies also underwent study evaluation and detailed extraction of health end point data. All underlying data is publicly available online as interactive visuals with downloadable metadata. RESULTS More than 13,000 studies were identified from scientific databases. Screening processes identified 121 mammalian bioassay and 111 epidemiological studies that met screening criteria. Epidemiological evidence (available for 12 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Mammalian bioassay evidence (available for 30 PFAS) commonly assessed effects in the reproductive, whole-body, nervous, and hepatic systems. Overall, 41 PFAS had evidence across mammalian bioassay and epidemiology data streams (roughly 11% of searched chemicals). DISCUSSION No epidemiological and/or mammalian bioassay evidence were identified for most of the PFAS included in our search. Results from this SEM, our 2022 SEM on ∼ 150 PFAS, and other PFAS assessment products from the US EPA are compiled into a comprehensive PFAS dashboard that provides researchers and regulators an overview of the current PFAS human health landscape including data gaps and can serve as a scoping tool to facilitate prioritization of PFAS-related research and/or risk assessment activities. https://doi.org/10.1289/EHP13423.
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Affiliation(s)
- Avanti V. Shirke
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | - Elizabeth G. Radke
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | | | | | | | | | | | | | | | - Xabier Arzuaga
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | - Johanna Congleton
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | - Allen Davis
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | | | - Ryan Jones
- Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division (HEEAD), US EPA, Durham, North Carolina, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure (CCTE), US EPA, Durham, North Carolina, USA
| | | | - Andrew Kraft
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | | | - Pamela D. Noyes
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), US Environmental Protection Agency (US EPA), Washington, DC, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), US EPA, Durham, North Carolina, USA
| | | | - Antony J. Williams
- Center for Computational Toxicology and Exposure (CCTE), US EPA, Durham, North Carolina, USA
| | | | - Laura M. Carlson
- Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division (HEEAD), US EPA, Durham, North Carolina, USA
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3
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Rogers JD, Leusch FD, Chambers B, Daniels KD, Everett LJ, Judson R, Maruya K, Mehinto AC, Neale PA, Paul-Friedman K, Thomas R, Snyder SA, Harrill J. High-Throughput Transcriptomics of Water Extracts Detects Reductions in Biological Activity with Water Treatment Processes. Environ Sci Technol 2024; 58:2027-2037. [PMID: 38235672 PMCID: PMC11003563 DOI: 10.1021/acs.est.3c07525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
The presence of numerous chemical contaminants from industrial, agricultural, and pharmaceutical sources in water supplies poses a potential risk to human and ecological health. Current chemical analyses suffer from limitations, including chemical coverage and high cost, and broad-coverage in vitro assays such as transcriptomics may further improve water quality monitoring by assessing a large range of possible effects. Here, we used high-throughput transcriptomics to assess the activity induced by field-derived water extracts in MCF7 breast carcinoma cells. Wastewater and surface water extracts induced the largest changes in expression among cell proliferation-related genes and neurological, estrogenic, and antibiotic pathways, whereas drinking and reclaimed water extracts that underwent advanced treatment showed substantially reduced bioactivity on both gene and pathway levels. Importantly, reclaimed water extracts induced fewer changes in gene expression than laboratory blanks, which reinforces previous conclusions based on targeted assays and improves confidence in bioassay-based monitoring of water quality.
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Affiliation(s)
- Jesse D. Rogers
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Frederic D.L. Leusch
- Australian Rivers Institute, School of Environment and Science, Griffith University, Southport Qld 4222, Australia
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | | | - Logan J. Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Keith Maruya
- Southern California Coastal Water Research Project Authority, 3535 Harbor Boulevard, Suite 110, Costa Mesa, CA 92626, USA
| | - Alvine C. Mehinto
- Southern California Coastal Water Research Project Authority, 3535 Harbor Boulevard, Suite 110, Costa Mesa, CA 92626, USA
| | - Peta A. Neale
- Australian Rivers Institute, School of Environment and Science, Griffith University, Southport Qld 4222, Australia
| | - Katie Paul-Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Russell Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shane A. Snyder
- Nanyang Environment & Water Research Institute (NEWRI), Nanyang Technological University, 1 Cleantech Loop, CleanTech One, #06-08, 637141, Singapore
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Carlson LM, Angrish M, Shirke AV, Radke EG, Schulz B, Kraft A, Judson R, Patlewicz G, Blain R, Lin C, Vetter N, Lemeris C, Hartman P, Hubbard H, Arzuaga X, Davis A, Dishaw LV, Druwe IL, Hollinger H, Jones R, Kaiser JP, Lizarraga L, Noyes PD, Taylor M, Shapiro AJ, Williams AJ, Thayer KA. Erratum: "Systematic Evidence Map for over One Hundred and Fifty Per- and Polyfluoroalkyl Substances (PFAS)". Environ Health Perspect 2024; 132:19001. [PMID: 38198380 PMCID: PMC10780484 DOI: 10.1289/ehp14191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024]
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Reardon AJF, Farmahin R, Williams A, Meier MJ, Addicks GC, Yauk CL, Matteo G, Atlas E, Harrill J, Everett LJ, Shah I, Judson R, Ramaiahgari S, Ferguson SS, Barton-Maclaren TS. From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Front Toxicol 2023; 5:1194895. [PMID: 37288009 PMCID: PMC10242042 DOI: 10.3389/ftox.2023.1194895] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/03/2023] [Indexed: 06/09/2023] Open
Abstract
The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.
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Affiliation(s)
- Anthony J. F. Reardon
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Reza Farmahin
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Andrew Williams
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Matthew J. Meier
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Gregory C. Addicks
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Carole L. Yauk
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Geronimo Matteo
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | - Ella Atlas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
- Department of Biochemistry, University of Ottawa, Ottawa, ON, Canada
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Logan J. Everett
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Imran Shah
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Richard Judson
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States
| | - Sreenivasa Ramaiahgari
- Division of Translational Toxicology, Mechanistic Toxicology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, United States
| | - Stephen S. Ferguson
- Division of Translational Toxicology, Mechanistic Toxicology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, United States
| | - Tara S. Barton-Maclaren
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
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Aurisano N, Jolliet O, Chiu WA, Judson R, Jang S, Unnikrishnan A, Kosnik MB, Fantke P. Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals. Environ Health Perspect 2023; 131:37016. [PMID: 36989077 PMCID: PMC10056221 DOI: 10.1289/ehp11524] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 02/06/2023] [Accepted: 03/03/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals. OBJECTIVES Using in vivo experimental animal data from the U.S. Environmental Protection Agency's Toxicity Value Database, we developed a semiautomated approach to determine surrogate oral route PODs, and corresponding toxicity values where regulatory assessments are unavailable. METHODS We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses (BMDh). We hypothesized that a quantile of the BMDh distribution could serve as a surrogate POD and determined the appropriate quantile by calibration to regulatory PODs. Finally, we characterized uncertainties around the surrogate PODs from intra- and interstudy variability and derived probabilistic toxicity values using a standardized workflow. RESULTS The BMDh distribution for each chemical was adequately fit by a lognormal distribution, and the 25th percentile best predicted the available regulatory PODs [R2≥0.78, residual standard error (RSE)≤0.53 log10 units]. We derived surrogate PODs for 10,145 chemicals from the curated data set, differentiating between general noncancer and reproductive/developmental effects, with typical uncertainties (at 95% confidence) of a factor of 10 and 12, respectively. From these PODs, probabilistic reference doses (1% incidence at 95% confidence), as well as human population effect doses (10% incidence), were derived. DISCUSSION In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.
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Affiliation(s)
- Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Olivier Jolliet
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Richard Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Suji Jang
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Aswani Unnikrishnan
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Marissa B. Kosnik
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Lyngby, Denmark
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Nyffeler J, Willis C, Harris FR, Taylor LW, Judson R, Everett LJ, Harrill JA. Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example. Toxicol Appl Pharmacol 2022; 444:116032. [PMID: 35483669 PMCID: PMC10894461 DOI: 10.1016/j.taap.2022.116032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 10/18/2022]
Abstract
The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.
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Affiliation(s)
- Johanna 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
| | - Laura W Taylor
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard Judson
- 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
| | - 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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8
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Carlson LM, Angrish M, Shirke AV, Radke EG, Schulz B, Kraft A, Judson R, Patlewicz G, Blain R, Lin C, Vetter N, Lemeris C, Hartman P, Hubbard H, Arzuaga X, Davis A, Dishaw LV, Druwe IL, Hollinger H, Jones R, Kaiser JP, Lizarraga L, Noyes PD, Taylor M, Shapiro AJ, Williams AJ, Thayer KA. Systematic Evidence Map for Over One Hundred and Fifty Per- and Polyfluoroalkyl Substances (PFAS). Environ Health Perspect 2022; 130:56001. [PMID: 35580034 PMCID: PMC9113544 DOI: 10.1289/ehp10343] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 05/05/2023]
Abstract
BACKGROUND Per- and polyfluoroalkyl substances (PFAS) are a large class of synthetic (man-made) chemicals widely used in consumer products and industrial processes. Thousands of distinct PFAS exist in commerce. The 2019 U.S. Environmental Protection Agency (U.S. EPA) Per- and Polyfluoroalkyl Substances (PFAS) Action Plan outlines a multiprogram national research plan to address the challenge of PFAS. One component of this strategy involves the use of systematic evidence map (SEM) approaches to characterize the evidence base for hundreds of PFAS. OBJECTIVE SEM methods were used to summarize available epidemiological and animal bioassay evidence for a set of ∼ 150 PFAS that were prioritized in 2019 by the U.S. EPA's Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing. METHODS Systematic review methods were used to identify and screen literature using manual review and machine-learning software. The Populations, Exposures, Comparators, and Outcomes (PECO) criteria were kept broad to identify mammalian animal bioassay and epidemiological studies that could inform human hazard identification. A variety of supplemental content was also tracked, including information on in vitro model systems; exposure measurement-only studies in humans; and absorption, distribution, metabolism, and excretion (ADME). Animal bioassay and epidemiology studies meeting PECO criteria were summarized with respect to study design, and health system(s) were assessed. Because animal bioassay studies with ≥ 21 -d exposure duration (or reproductive/developmental study design) were most useful to CCTE analyses, these studies underwent study evaluation and detailed data extraction. All data extraction is publicly available online as interactive visuals with downloadable metadata. RESULTS More than 40,000 studies were identified from scientific databases. Screening processes identified 44 animal and 148 epidemiology studies from the peer-reviewed literature and 95 animal and 50 epidemiology studies from gray literature that met PECO criteria. Epidemiological evidence (available for 15 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Animal evidence (available for 40 PFAS) commonly assessed effects in the reproductive, developmental, urinary, immunological, and hepatic systems. Overall, 45 PFAS had evidence across animal and epidemiology data streams. DISCUSSION Many of the ∼ 150 PFAS were data poor. Epidemiological and animal evidence were lacking for most of the PFAS included in our search. By disseminating this information, we hope to facilitate additional assessment work by providing the initial scoping literature survey and identifying key research needs. Future research on data-poor PFAS will help support a more complete understanding of the potential health effects from PFAS exposures. https://doi.org/10.1289/EHP10343.
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Affiliation(s)
- Laura M Carlson
- Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division (HEEAD), U.S. Environmental Protection Agency (U.S. EPA), Durham, North Carolina, USA
| | - Michelle Angrish
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Durham, North Carolina, USA
| | - Avanti V Shirke
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Washington, District of Columbia, USA
| | - Elizabeth G Radke
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Washington, District of Columbia, USA
| | - Brittany Schulz
- Oak Ridge Associated Universities (ORAU), Oak Ridge, Tennessee, USA
| | - Andrew Kraft
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Washington, District of Columbia, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure (CCTE), U.S. EPA, Durham, North Carolina, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure (CCTE), U.S. EPA, Durham, North Carolina, USA
| | | | | | | | | | | | | | - Xabier Arzuaga
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Washington, District of Columbia, USA
| | - Allen Davis
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Washington, District of Columbia, USA
| | - Laura V Dishaw
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Durham, North Carolina, USA
| | - Ingrid L Druwe
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Durham, North Carolina, USA
| | - Hillary Hollinger
- Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division (HEEAD), U.S. Environmental Protection Agency (U.S. EPA), Durham, North Carolina, USA
| | - Ryan Jones
- Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division (HEEAD), U.S. Environmental Protection Agency (U.S. EPA), Durham, North Carolina, USA
| | - J Phillip Kaiser
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Cincinnati, Ohio, USA
| | - Lucina Lizarraga
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Cincinnati, Ohio, USA
| | - Pamela D Noyes
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Washington, District of Columbia, USA
| | - Michele Taylor
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Durham, North Carolina, USA
| | - Andrew J Shapiro
- Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division (HEEAD), U.S. Environmental Protection Agency (U.S. EPA), Durham, North Carolina, USA
| | - Antony J Williams
- Center for Computational Toxicology and Exposure (CCTE), U.S. EPA, Durham, North Carolina, USA
| | - Kristina A Thayer
- Center for Public Health and Environmental Assessment, Chemical & Pollutant Assessment Division (CPAD), U.S. EPA, Durham, North Carolina, USA
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9
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Bundy JL, Judson R, Williams AJ, Grulke C, Shah I, Everett LJ. Predicting molecular initiating events using chemical target annotations and gene expression. BioData Min 2022; 15:7. [PMID: 35246223 PMCID: PMC8895536 DOI: 10.1186/s13040-022-00292-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/10/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. RESULTS We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. CONCLUSIONS This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.
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Affiliation(s)
- Joseph L Bundy
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Richard Judson
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Antony J Williams
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Chris Grulke
- Chemical Characterization and Exposure Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Imran Shah
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA
| | - Logan J Everett
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Durham, NC, 27709, USA.
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10
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Sheffield T, Brown J, Davidson S, Friedman KP, Judson R. tcplfit2: an R-language general purpose concentration-response modeling package. Bioinformatics 2022; 38:1157-1158. [PMID: 34791027 DOI: 10.1093/bioinformatics/btab779] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/14/2021] [Accepted: 11/09/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Many applications of chemical screening are performed in concentration or dose-response mode, and it is necessary to extract appropriate parameters, including whether the chemical/assay pair is active and if so, what are concentrations where activity is seen. Typically, multiple mathematical models or curve shapes are tested against the data to assess the best fit. There are several commercial programs used for this purpose as well as open-source libraries. A widely used system for managing high-throughput screening (HTS) concentration-response data is tcpl (ToxCast Pipeline). The current implementation of tcpl has the concentration-response modeling code tightly integrated with the data management and databasing aspects of HTS data processing. Tcplfit2 is a stand-alone version of the curve-fitting and hitcalling core of tcpl that has been extended to include a large number of standard curve classes and to use benchmark dose modeling. This package will be useful for HTS concentration-response data such as high-throughput whole genome transcriptomics. AVAILABILITY AND IMPLEMENTATION tcplfit2 is written in R and is available from CRAN.
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Affiliation(s)
- Thomas Sheffield
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - Jason Brown
- US Environmental Protection Agency, RTP NC USA
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11
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Aurisano N, Huang L, Jang S, Chiu W, Judson R, Jolliet O, Fantke P. Broadening the chemical coverage to derive human toxicity dose-response factors for non-cancer endpoints. Toxicol Lett 2021. [DOI: 10.1016/s0378-4274(21)00799-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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12
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Harrill J, Everett L, Nyffeler J, Willis C, Brockway R, Freidman K, Shah I, Judson R. Strategic Use of High-Throughput Transcriptomics and Phenotypic Profiling Data in Support of Regulatory Decisions. Toxicol Lett 2021. [DOI: 10.1016/s0378-4274(21)00358-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Pradeep P, Judson R, DeMarini DM, Keshava N, Martin TM, Dean J, Gibbons CF, Simha A, Warren SH, Gwinn MR, Patlewicz G. Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset. ACTA ACUST UNITED AC 2021; 18. [PMID: 34504984 DOI: 10.1016/j.comtox.2021.100167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Regulatory agencies world-wide face the challenge of performing risk-based prioritization of thousands of substances in commerce. In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk +/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The 'best' consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity.
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Affiliation(s)
- Prachi Pradeep
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - David M DeMarini
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Nagalakshmi Keshava
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Todd M Martin
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Jeffry Dean
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Catherine F Gibbons
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Washington, District of Columbia, USA
| | - Anita Simha
- ORAU, contractor to U.S. Environmental Protection Agency through the National Student Services Contract, Research Triangle Park, North Carolina, USA
| | - Sarah H Warren
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Maureen R Gwinn
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
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14
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Fantke P, Chiu WA, Aylward L, Judson R, Huang L, Jang S, Gouin T, Rhomberg L, Aurisano N, McKone T, Jolliet O. Exposure and Toxicity Characterization of Chemical Emissions and Chemicals in Products: Global Recommendations and Implementation in USEtox. Int J Life Cycle Assess 2021; 26:899-915. [PMID: 34140756 PMCID: PMC8208704 DOI: 10.1007/s11367-021-01889-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 03/11/2021] [Indexed: 05/24/2023]
Abstract
PURPOSE Reducing chemical pressure on human and environmental health is an integral part of the global sustainability agenda. Guidelines for deriving globally applicable, life cycle based indicators are required to consistently quantify toxicity impacts from chemical emissions as well as from chemicals in consumer products. In response, we elaborate the methodological framework and present recommendations for advancing near-field/far-field exposure and toxicity characterization, and for implementing these recommendations in the scientific consensus model USEtox. METHODS An expert taskforce was convened by the Life Cycle Initiative hosted by UN Environment to expand existing guidance for evaluating human toxicity impacts from exposure to chemical substances. This taskforce evaluated advances since the original release of USEtox. Based on these advances, the taskforce identified two major aspects that required refinement, namely integrating near-field and far-field exposure and improving human dose-response modeling. Dedicated efforts have led to a set of recommendations to address these aspects in an update of USEtox, while ensuring consistency with the boundary conditions for characterizing life cycle toxicity impacts and being aligned with recommendations from agencies that regulate chemical exposure. The proposed framework was finally tested in an illustrative rice production and consumption case study. RESULTS AND DISCUSSION On the exposure side, a matrix system is proposed and recommended to integrate far-field exposure from environmental emissions with near-field exposure from chemicals in various consumer product types. Consumer exposure is addressed via submodels for each product type to account for product characteristics and exposure settings. Case study results illustrate that product-use related exposure dominates overall life cycle exposure. On the effect side, a probabilistic dose-response approach combined with a decision tree for identifying reliable points of departure is proposed for non-cancer effects, following recent guidance from the World Health Organization. This approach allows for explicitly considering both uncertainty and human variability in effect factors. Factors reflecting disease severity are proposed to distinguish cancer from non-cancer effects, and within the latter discriminate reproductive/developmental and other non-cancer effects. All proposed aspects have been consistently implemented into the original USEtox framework. CONCLUSIONS The recommended methodological advancements address several key limitations in earlier approaches. Next steps are to test the new characterization framework in additional case studies and to close remaining research gaps. Our framework is applicable for evaluating chemical emissions and product-related exposure in life cycle assessment, chemical alternatives assessment and chemical substitution, consumer exposure and risk screening, and high-throughput chemical prioritization.
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Affiliation(s)
- Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Lesa Aylward
- Queensland Alliance for Environmental Health Sciences, University of Queensland, Brisbane, Australia
| | - Richard Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Lei Huang
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Suji Jang
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Todd Gouin
- TG Environmental Research, Sharnbrook, MK44 1PL, UK
| | | | - Nicolò Aurisano
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet 424, 2800 Kgs. Lyngby, Denmark
| | - Thomas McKone
- School of Public Health, University of California, Berkeley, California 94720, USA
| | - Olivier Jolliet
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA
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15
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Emara Y, Fantke P, Judson R, Chang X, Pradeep P, Lehmann A, Siegert MW, Finkbeiner M. Integrating endocrine-related health effects into comparative human toxicity characterization. Sci Total Environ 2021; 762:143874. [PMID: 33401053 DOI: 10.1016/j.scitotenv.2020.143874] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 06/12/2023]
Abstract
Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.
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Affiliation(s)
- Yasmine Emara
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
| | - Richard Judson
- Office of Research and Development, Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, LLC., Morrisville, NC 27560, United States.
| | - Prachi Pradeep
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
| | - Annekatrin Lehmann
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Marc-William Siegert
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Matthias Finkbeiner
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
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16
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Pradeep P, Patlewicz G, Pearce R, Wambaugh J, Wetmore B, Judson R. Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment. ACTA ACUST UNITED AC 2020; 16. [PMID: 34124416 DOI: 10.1016/j.comtox.2020.100136] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Clint (classification and regression) using a dataset of 1487 chemicals; utilization of predicted TK parameters to estimate uncertainty in steady-state plasma concentration (Css); and subsequent in vitro-in vivo extrapolation analyses to derive bioactivity-exposure ratio (BER) plot to compare human oral equivalent doses and exposure predictions using androgen and estrogen receptor activity data for 233 chemicals as an example dataset. The results demonstrate that fub is structurally more predictable than Clint. The model with the highest observed performance for fub had an external test set RMSE/σ=0.62 and R2=0.61, for Clint classification had an external test set accuracy = 65.9%, and for intrinsic clearance regression had an external test set RMSE/σ=0.90 and R2=0.20. This relatively low performance is in part due to the large uncertainty in the underlying Clint data. We show that Css is relatively insensitive to uncertainty in Clint. The models were benchmarked against the ADMET Predictor software. Finally, the BER analysis allowed identification of 14 out of 136 chemicals for further risk assessment demonstrating the utility of these models in aiding risk-based chemical prioritization.
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Affiliation(s)
- Prachi Pradeep
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Robert Pearce
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - John Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Barbara Wetmore
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
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17
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Abstract
Human health risk assessment for environmental chemical exposure is limited by a vast majority of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models based on chemical structure information, can predict hazard in the absence of experimental data. Risk assessment requires identification of a quantitative point-of-departure (POD) value, the point on the dose-response curve that marks the beginning of a low-dose extrapolation. This study presents two sets of QSAR models to predict POD values (PODQSAR) for repeat dose toxicity. For training and validation, a publicly available in vivo toxicity dataset for 3592 chemicals was compiled using the U.S. Environmental Protection Agency's Toxicity Value database (ToxValDB). The first set of QSAR models predict point-estimates of POD values (PODQSAR) using structural and physicochemical descriptors for repeat dose study types and species combinations. A random forest QSAR model using study type and species as descriptors showed the best performance, with an external test set root mean square error (RMSE) of 0.71 log10-mg/kg/day and coefficient of determination (R2) of 0.53. The second set of QSAR models predict the 95% confidence intervals for PODQSAR using a constructed POD distribution with a mean equal to the median POD value and a standard deviation of 0.5 log10-mg/kg/day, based on previously published typical study-to-study variability that may lead to uncertainty in model predictions. Bootstrap resampling of the pre-generated POD distribution was used to derive point-estimates and 95% confidence intervals for each POD prediction. Enrichment analysis to evaluate the accuracy of PODQSAR showed that 80% of the 5% most potent chemicals were found in the top 20% of the most potent chemical predictions, suggesting that the repeat dose POD QSAR models presented here may help inform screening level human health risk assessments in the absence of other data.
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Affiliation(s)
- Prachi Pradeep
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
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18
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Nyffeler J, Haggard DE, Willis C, Setzer RW, Judson R, Paul-Friedman K, Everett LJ, Harrill JA. Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data. SLAS Discov 2020; 26:292-308. [PMID: 32862757 DOI: 10.1177/2472555220950245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.
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Affiliation(s)
- Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Derik E Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Clinton Willis
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, USA
| | - R Woodrow Setzer
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Katie Paul-Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
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19
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Judson R, Houck K, Paul Friedman K, Brown J, Browne P, Johnston PA, Close DA, Mansouri K, Kleinstreuer N. Selecting a minimal set of androgen receptor assays for screening chemicals. Regul Toxicol Pharmacol 2020; 117:104764. [PMID: 32798611 PMCID: PMC8356084 DOI: 10.1016/j.yrtph.2020.104764] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/03/2020] [Accepted: 08/07/2020] [Indexed: 01/01/2023]
Abstract
Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modeling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. Agonist batteries of as few as six assays and antagonist batteries of as few as five assays can yield balanced accuracies of 95% or better relative to the full model. Balanced accuracy for predicting reference chemicals is 100%. An approach is outlined for researchers to develop their own subset batteries to accurately detect AR activity using assays that map to the pathway of key molecular and cellular events involved in chemical-mediated AR activation and transcriptional activity. This work indicates in vitro bioactivity and in silico predictions that map to the AR pathway could be used in an integrated approach to testing and assessment for identifying chemicals that interact directly with the mammalian AR.
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Affiliation(s)
| | - Keith Houck
- U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Jason Brown
- U.S. Environmental Protection Agency, RTP, NC, USA
| | | | - Paul A Johnston
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Close
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kamel Mansouri
- Integrated Laboratory Systems, Inc., Morrisville, NC, USA
| | - Nicole Kleinstreuer
- NTP Interagency Center for the Evaluation of Alternative Toxicological Methods, RTP, NC, USA
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20
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Ly Pham L, Watford S, Pradeep P, Martin MT, Thomas R, Judson R, Setzer RW, Paul Friedman K. Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels. ACTA ACUST UNITED AC 2020; 15:1-100126. [PMID: 33426408 DOI: 10.1016/j.comtox.2020.100126] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log10-mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20-0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log10-mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.
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Affiliation(s)
- Ly Ly Pham
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.,Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN 37830
| | - Sean Watford
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.,ORAU, contractor to U.S. Environmental Protection Agency through the National Student Services Contract, 100 ORAU Way, Oak Ridge, TN 37830
| | - Prachi Pradeep
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.,Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN 37830
| | - Matthew T Martin
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA.,Currently at Global Investigative Toxicology, Drug Safety Research and Development, Pfizer Inc. 445 Eastern Point Road, Groton, CT 06340
| | - Russell Thomas
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
| | - R Woodrow Setzer
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
| | - Katie Paul Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
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21
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Wittholz K, Fetterplace K, Clode M, George ES, MacIsaac CM, Judson R, Presneill JJ, Deane AM. Measuring nutrition-related outcomes in a cohort of multi-trauma patients following intensive care unit discharge. J Hum Nutr Diet 2019; 33:414-422. [PMID: 31788891 DOI: 10.1111/jhn.12719] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Functional recovery is an important outcome for those who survive critical illness. The present study aimed to assess nutrition provision and nutrition-related outcomes in a multi-trauma cohort following intensive care unit (ICU) discharge. METHODS The present study investigated a prospective cohort of patients discharged from an ICU, who had been admitted because of major trauma and required mechanical ventilation for at least 48 h. Nutrition-related outcomes, including body weight, quadriceps muscle layer thickness (QMLT), handgrip strength and subjective global assessment, were recorded on ICU discharge, days 5-7 post-ICU discharge and then weekly until hospital discharge. Nutrition intake was recorded for 5 days post-ICU discharge. Unless otherwise stated, data are presented as the mean (SD). RESULTS Twenty-eight patients [75% males, 55 (22.5) years] were included. Intake met 64% (28%) of estimated energy and 72% (32%) of protein requirements over the 5 days post-ICU discharge, which was similar to over the ICU admission. From ICU admission to hospital discharge, the mean reduction in weight was 4.2 kg (95% confidence interval = 2.2-6.3, P < 0.001) and after ICU discharge, the mean reduction in weight and QMLT was 2.6 kg (95% confidence interval = 1.0-4.2, P = 0.004) and 0.23 cm (95% confidence interval = 0.06-0.4, P = 0.01), respectively. CONCLUSIONS Patients received less energy and protein than estimated requirements after ICU discharge. Weight loss and reduction in QMLT also occurred during this period.
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Affiliation(s)
- K Wittholz
- Department of Allied Health (Clinical Nutrition), The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - K Fetterplace
- Department of Allied Health (Clinical Nutrition), The Royal Melbourne Hospital, Melbourne, VIC, Australia.,Department of Medicine and Radiology, Royal Melbourne Hospital, The University of Melbourne, Melbourne Medical School, Parkville, VIC, Australia
| | - M Clode
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Science, Deakin University, Geelong, VIC, Australia
| | - E S George
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Science, Deakin University, Geelong, VIC, Australia
| | - C M MacIsaac
- Department of Medicine and Radiology, Royal Melbourne Hospital, The University of Melbourne, Melbourne Medical School, Parkville, VIC, Australia.,Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - R Judson
- Department of Trauma, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - J J Presneill
- Department of Medicine and Radiology, Royal Melbourne Hospital, The University of Melbourne, Melbourne Medical School, Parkville, VIC, Australia.,Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - A M Deane
- Department of Medicine and Radiology, Royal Melbourne Hospital, The University of Melbourne, Melbourne Medical School, Parkville, VIC, Australia.,Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, VIC, Australia
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22
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Rivetti C, Allen TEH, Brown JB, Butler E, Carmichael PL, Colbourne JK, Dent M, Falciani F, Gunnarsson L, Gutsell S, Harrill JA, Hodges G, Jennings P, Judson R, Kienzler A, Margiotta-Casaluci L, Muller I, Owen SF, Rendal C, Russell PJ, Scott S, Sewell F, Shah I, Sorrel I, Viant MR, Westmoreland C, White A, Campos B. Vision of a near future: Bridging the human health-environment divide. Toward an integrated strategy to understand mechanisms across species for chemical safety assessment. Toxicol In Vitro 2019; 62:104692. [PMID: 31669395 DOI: 10.1016/j.tiv.2019.104692] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/25/2019] [Accepted: 10/14/2019] [Indexed: 12/31/2022]
Abstract
There is a growing recognition that application of mechanistic approaches to understand cross-species shared molecular targets and pathway conservation in the context of hazard characterization, provide significant opportunities in risk assessment (RA) for both human health and environmental safety. Specifically, it has been recognized that a more comprehensive and reliable understanding of similarities and differences in biological pathways across a variety of species will better enable cross-species extrapolation of potential adverse toxicological effects. Ultimately, this would also advance the generation and use of mechanistic data for both human health and environmental RA. A workshop brought together representatives from industry, academia and government to discuss how to improve the use of existing data, and to generate new NAMs data to derive better mechanistic understanding between humans and environmentally-relevant species, ultimately resulting in holistic chemical safety decisions. Thanks to a thorough dialogue among all participants, key challenges, current gaps and research needs were identified, and potential solutions proposed. This discussion highlighted the common objective to progress toward more predictive, mechanistically based, data-driven and animal-free chemical safety assessments. Overall, the participants recognized that there is no single approach which would provide all the answers for bridging the gap between mechanism-based human health and environmental RA, but acknowledged we now have the incentive, tools and data availability to address this concept, maximizing the potential for improvements in both human health and environmental RA.
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Affiliation(s)
- Claudia Rivetti
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - James B Brown
- Department of Genome Dynamics Lawrence Berkeley National Laboratory, University of California Berkeley, Berkeley, California 94720, USA
| | - Emma Butler
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Paul L Carmichael
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - John K Colbourne
- School of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Matthew Dent
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Francesco Falciani
- Institute for Integrative Biology, University of Liverpool, L69 7ZB Liverpool, United Kingdom
| | - Lina Gunnarsson
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Geoffrey Pope, Stocker Road, Exeter, Devon EX4 4QD, United Kingdom
| | - Steve Gutsell
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Joshua A Harrill
- National Center for Computational Toxicology, Office of Research & Development, U.S. Environmental Protection Agency, Mail Code B205-01, Research Triangle Park, Durham, North Carolina 27711, USA
| | - Geoff Hodges
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Paul Jennings
- Division of Molecular and Computational Toxicology, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Richard Judson
- National Center for Computational Toxicology, Office of Research & Development, U.S. Environmental Protection Agency, Mail Code B205-01, Research Triangle Park, Durham, North Carolina 27711, USA
| | - Aude Kienzler
- European Commission, Joint Research Centre (JRC), Ispra, VA, Italy
| | | | - Iris Muller
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Stewart F Owen
- AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TF, United Kingdom
| | - Cecilie Rendal
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Paul J Russell
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Sharon Scott
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Fiona Sewell
- NC3Rs, Gibbs Building, 215 Euston Road, London NW1 2BE, United Kingdom
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research & Development, U.S. Environmental Protection Agency, Mail Code B205-01, Research Triangle Park, Durham, North Carolina 27711, USA
| | - Ian Sorrel
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Carl Westmoreland
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Andrew White
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Bruno Campos
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom.
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23
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Harrill J, Shah I, Setzer RW, Haggard D, Auerbach S, Judson R, Thomas RS. Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions. Curr Opin Toxicol 2019; 15:64-75. [PMID: 31501805 DOI: 10.1016/j.cotox.2019.05.004] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Recently, numerous organizations, including governmental regulatory agencies in the U.S. and abroad, have proposed using data from New Approach Methodologies (NAMs) for augmenting and increasing the pace of chemical assessments. NAMs are broadly defined as any technology, methodology, approach or combination thereof that can be used to provide information on chemical hazard and risk assessment that avoids the use of intact animals. High-throughput transcriptomics (HTTr) is a type of NAM that uses gene expression profiling as an endpoint for rapidly evaluating the effects of large numbers of chemicals on in vitro cell culture systems. As compared to targeted high-throughput screening (HTS) approaches that measure the effect of chemical X on target Y, HTTr is a non-targeted approach that allows researchers to more broadly characterize the integrated response of an intact biological system to chemicals that may affect a specific biological target or many biological targets under a defined set of treatment conditions (time, concentration, etc.). HTTr screening performed in concentration-response mode can provide potency estimates for the concentrations of chemicals that produce perturbations in cellular response pathways. Here, we discuss study design considerations for HTTr concentration-response screening and present a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach. The framework involves concentration-response modeling of HTTr data, mapping gene level responses to biological pathways, determination of BPACs, in vitro-to-in vivo extrapolation (IVIVE) and comparison to human exposure predictions.
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Affiliation(s)
- Joshua Harrill
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Derik Haggard
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Scott Auerbach
- National Toxicology Program, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, NC, USA
| | - Richard Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Russell S Thomas
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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24
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Ho SM, Lewis JD, Mayer EA, Bernstein CN, Plevy SE, Chuang E, Rappaport SM, Croitoru K, Korzenik JR, Krischer J, Hyams JS, Judson R, Kellis M, Jerrett M, Miller GW, Grant ML, Shtraizent N, Honig G, Hurtado-Lorenzo A, Wu GD. Challenges in IBD Research: Environmental Triggers. Inflamm Bowel Dis 2019; 25:S13-S23. [PMID: 31095702 PMCID: PMC6787673 DOI: 10.1093/ibd/izz076] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Indexed: 02/07/2023]
Abstract
Environmental triggers is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, novel technologies, precision medicine and pragmatic clinical research. The Challenges in IBD research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the environmental triggers section is focused on the main research gaps in elucidating causality of environmental factors in IBD. Research gaps were identified in: 1) epidemiology of exposures; 2) identification of signatures of biological response to exposures; and 3) mechanisms of how environmental exposures drive IBD. To address these gaps, the implementation of longitudinal prospective studies to determine disease evolution and identify sub-clinical changes in response to exposures is proposed. This can help define critical windows of vulnerability and risk prediction. In addition, systems biology analysis and in silico modeling were proposed as approaches to integrate the IBD exposome for the identification of biological signatures of response to exposures, and to develop prediction models of the effects of environmental factors in driving disease activity and response to therapy. This research could lead to identification of biomarkers of exposures and new modalities for therapeutic intervention. Finally, hypothesis-driven mechanistic studies to understand gene-environment interactions and to validate causality of priority factors should be performed to determine how environment influences clinical outcomes.
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Affiliation(s)
| | - James D Lewis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Emeran A Mayer
- University of California Los Angeles, Los Angeles, California
| | | | | | | | | | | | | | | | - Jeffrey S Hyams
- Connecticut Children’s Medical Center, Hartford, Connecticut
| | - Richard Judson
- United States Environmental Protection Agency, Washington, District of Columbia
| | - Manolis Kellis
- Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Michael Jerrett
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Melanie L Grant
- Children’s National Health System, Washington, District of Columbia
| | | | - Gerard Honig
- Crohn’s & Colitis Foundation, New York, New York
| | - Andrés Hurtado-Lorenzo
- Crohn’s & Colitis Foundation, New York, New York,Address correspondence to: Andrés Hurtado-Lorenzo, PhD, 733 3rd Ave Suite 510, New York, NY USA 10017 ()
| | - Gary D Wu
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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25
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Vinggaard AM, Judson R. Editorial overview: Risk assessment in toxicology. Current Opinion in Toxicology 2019. [DOI: 10.1016/j.cotox.2019.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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26
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Fantke P, Aylward L, Bare J, Chiu WA, Dodson R, Dwyer R, Ernstoff A, Howard B, Jantunen M, Jolliet O, Judson R, Kirchhübel N, Li D, Miller A, Paoli G, Price P, Rhomberg L, Shen B, Shin HM, Teeguarden J, Vallero D, Wambaugh J, Wetmore BA, Zaleski R, McKone TE. Advancements in Life Cycle Human Exposure and Toxicity Characterization. Environ Health Perspect 2018; 126:125001. [PMID: 30540492 PMCID: PMC6371687 DOI: 10.1289/ehp3871] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 11/06/2018] [Accepted: 11/15/2018] [Indexed: 05/06/2023]
Abstract
BACKGROUND The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA). OBJECTIVES The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose–response modeling. DISCUSSION The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose–response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose–response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential. CONCLUSIONS Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks. https://doi.org/10.1289/EHP3871.
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Affiliation(s)
- Peter Fantke
- Quantitative Sustainability Assessment Division, Department of Management Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lesa Aylward
- National Centre for Environmental Toxicology, University of Queensland, Brisbane, Australia
| | - Jane Bare
- U.S. EPA (Environmental Protection Agency), Cincinnati, Ohio, USA
| | - Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA
| | - Robin Dodson
- Silent Spring Institute, Newton, Massachusetts, USA
| | - Robert Dwyer
- International Copper Association, New York, New York, USA
| | | | | | - Matti Jantunen
- Department of Environmental Health, National Institute for Health and Welfare, Kuopio, Finland
| | - Olivier Jolliet
- School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Nienke Kirchhübel
- Quantitative Sustainability Assessment Division, Department of Management Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Dingsheng Li
- School of Community Health Sciences, University of Nevada, Reno, Nevada, USA
| | - Aubrey Miller
- National Institute of Environmental Health Sciences, Bethesda, Maryland, USA
| | - Greg Paoli
- Risk Sciences International, Ottawa, Ontario, Canada
| | - Paul Price
- U.S. EPA, Research Triangle Park, North Carolina, USA
| | | | - Beverly Shen
- School of Public Health, University of California, Berkeley, California, USA
| | | | - Justin Teeguarden
- Health Effects and Exposure Science, Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - John Wambaugh
- U.S. EPA, Research Triangle Park, North Carolina, USA
| | | | - Rosemary Zaleski
- ExxonMobil Biomedical Sciences, Inc., Annandale, New Jersey, USA
| | - Thomas E McKone
- School of Public Health, University of California, Berkeley, California, USA
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27
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Kleinstreuer NC, Browne P, Chang X, Judson R, Casey W, Ceger P, Deisenroth C, Baker N, Markey K, Thomas RS. Evaluation of androgen assay results using a curated Hershberger database. Reprod Toxicol 2018; 81:272-280. [PMID: 30205137 PMCID: PMC7171594 DOI: 10.1016/j.reprotox.2018.08.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 07/25/2018] [Accepted: 08/23/2018] [Indexed: 12/18/2022]
Abstract
A set of 39 reference chemicals with reproducible androgen pathway effects in vivo, identified in the companion manuscript [1], were used to interrogate the performance of the ToxCast/Tox 21 androgen receptor (AR) model based on 11 high throughput assays. Cytotoxicity data and specificity confirmation assays were used to distinguish assay loss-of-function from true antagonistic signaling suppression. Overall agreement was 66% (19/29), with ten additional inconclusive chemicals. Most discrepancies were explained using in vitro to in vivo extrapolation to estimate equivalent administered doses. The AR model had 100% positive predictive value for the in vivo response, i.e. there were no false positives, and chemicals with conclusive AR model results (agonist or antagonist) were consistently positive in vivo. Considering the lack of reproducibility of the in vivo Hershberger assay, the in vitro AR model may better predict specific AR interaction and can rapidly and cost-effectively screen thousands of chemicals without using animals.
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28
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Nguyen J, Maier A, Ovesen J, Kleinstreuer N, Judson R, Krishan M. A proof-of-concept study: evaluating the applicability of high-throughput screening data and read-across tools for food relevant chemicals. Toxicol Lett 2018. [DOI: 10.1016/j.toxlet.2018.06.725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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29
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Judson R. Rapid toxicity screening of chemicals combining in vitro high-throughput transcriptomics, toxicokinetics and exposure estimates. Toxicol Lett 2018. [DOI: 10.1016/j.toxlet.2018.06.1125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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30
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Abstract
Background Globally, populations are ageing, creating challenges for trauma system design. Despite this, little is known about causes of injury and long-term outcomes in older injured patients. This study aims to describe temporal trends in the incidence, causes and functional outcomes of major trauma in older adults. Methods The population-based Victorian State Trauma Registry was used to identify patients with major trauma aged 65 years and older with a date of injury between 1 January 2007 and 31 December 2016. Temporal trends in population-based incidence rates were evaluated. Functional outcome was measured using the Glasgow Outcome Scale - Extended. Results There were 9250 older adults with major trauma during the study period. Low falls were the most common mechanism of injury (62·5 per cent), followed by transport-related events (22·2 per cent) and high falls (9·5 per cent). The number of patients with major trauma aged 65 years and older more than doubled from 2007 to 2016, and the incidence increased by 4·3 per cent per year (incidence rate ratio 1·043, 95 per cent c.i. 1·035 to 1·050; P < 0·001). At 12 months after injury, 41·8 per cent of older adults with major trauma had died, and 52·2 per cent of those who survived to hospital discharge were not living independently. Conclusions The number and proportion of older adults with major trauma are increasing rapidly and this will impact on trauma system design. Given the poor long-term outcomes, there needs to be greater emphasis on ensuring that appropriate interventions are targeted to the right patients and enhanced efforts in primary prevention.
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Affiliation(s)
- B Beck
- Department of Epidemiology and Preventive Medicine, Monash University Melbourne, Victoria Australia
| | - P Cameron
- Department of Epidemiology and Preventive Medicine, Monash University Melbourne, Victoria Australia.,Emergency and Trauma Centre The Alfred Melbourne, Victoria Australia
| | - J Lowthian
- Department of Epidemiology and Preventive Medicine, Monash University Melbourne, Victoria Australia.,Bolton Clarke Research Institute, Bolton Clarke Melbourne, Victoria Australia
| | - M Fitzgerald
- Trauma Service The Alfred Melbourne, Victoria Australia.,National Trauma Research Institute, Royal Melbourne Hospital Melbourne, Victoria Australia
| | - R Judson
- Department of General Surgery, Royal Melbourne Hospital Melbourne, Victoria Australia.,Department of Surgery University of Melbourne Melbourne, Victoria Australia
| | - B J Gabbe
- Department of Epidemiology and Preventive Medicine, Monash University Melbourne, Victoria Australia.,Farr Institute Swansea University Medical School Swansea UK
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Torres R, Lang U, Shelton S, Yeniay Y, Joseph N, Shain A, Yeh I, Oldham M, Wei M, Bastian B, Judson R. 1220 MicroRNA signature distinguishing nevi from primary melanoma. J Invest Dermatol 2018. [DOI: 10.1016/j.jid.2018.03.1235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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32
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Truong L, Ouedraogo G, Pham L, Clouzeau J, Loisel-Joubert S, Blanchet D, Noçairi H, Setzer W, Judson R, Grulke C, Mansouri K, Martin M. Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates. Arch Toxicol 2018; 92:587-600. [PMID: 29075892 PMCID: PMC5818596 DOI: 10.1007/s00204-017-2067-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/18/2017] [Indexed: 11/29/2022]
Abstract
In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log10 mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for over 30,000 chemicals.
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Affiliation(s)
- Lisa Truong
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
- Currently at Oregon State University, Corvallis, USA
| | - Gladys Ouedraogo
- L'Oréal Safety Research Department, 1 Avenue E. Schueller, 93600, Aulnay-Sous-Bois, France
| | - LyLy Pham
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Jacques Clouzeau
- L'Oréal Safety Research Department, 1 Avenue E. Schueller, 93600, Aulnay-Sous-Bois, France
| | - Sophie Loisel-Joubert
- L'Oréal Safety Research Department, 1 Avenue E. Schueller, 93600, Aulnay-Sous-Bois, France
| | - Delphine Blanchet
- L'Oréal Safety Research Department, 1 Avenue E. Schueller, 93600, Aulnay-Sous-Bois, France
| | - Hicham Noçairi
- L'Oréal Safety Research Department, 1 Avenue E. Schueller, 93600, Aulnay-Sous-Bois, France
| | - Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Richard Judson
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Chris Grulke
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
- Currently at Scitovation LLC, Research Triangle Park, NC, USA
| | - Matthew Martin
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
- Currently at Pfizer, Inc, Drug Safety Research and Development, 445 Eastern Point Road, MS 8274-1224, Groton, CT, 06340, USA.
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Pradeep P, Mansouri K, Patlewicz G, Judson R. A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols. ACTA ACUST UNITED AC 2017; 4:22-30. [PMID: 30057968 DOI: 10.1016/j.comtox.2017.09.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions.
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Affiliation(s)
- Prachi Pradeep
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.,National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Grace Patlewicz
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
| | - Richard Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
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Trisciuzzi D, Alberga D, Mansouri K, Judson R, Novellino E, Mangiatordi GF, Nicolotti O. Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals. J Chem Inf Model 2017; 57:2874-2884. [PMID: 29022712 DOI: 10.1021/acs.jcim.7b00420] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.
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Affiliation(s)
- Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy.,Centro Ricerche TIRES, Università degli Studi di Bari "Aldo Moro" , Via Amendola 173, I-70126 Bari, Italy
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education , Oak Ridge, Tennessee 37830, United States.,National Center for Computational Toxicology, U.S. Environmental Protection Agency , 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27711, United States.,ScitoVation LLC , 6 Davis Drive, Research Triangle Park, North Carolina 27709, United States
| | - Richard Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency , 109 T.W. Alexander Drive, Research Triangle Park, North Carolina 27711, United States
| | - Ettore Novellino
- Dipartimento di Farmacia, Università degli Studi di Napoli "Federico II" , Via D. Montesano 49, 80131 Napoli, Italy
| | - Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy.,Centro Ricerche TIRES, Università degli Studi di Bari "Aldo Moro" , Via Amendola 173, I-70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro" , Via E. Orabona 4, I-70126 Bari, Italy.,Centro Ricerche TIRES, Università degli Studi di Bari "Aldo Moro" , Via Amendola 173, I-70126 Bari, Italy
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Judson R. Linking in vivo toxicity data to ToxCast/Tox21 in vitro assay data. Toxicol Lett 2017. [DOI: 10.1016/j.toxlet.2017.07.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Malloy T, Zaunbrecher V, Beryt E, Judson R, Tice R, Allard P, Blake A, Cote I, Godwin H, Heine L, Kerzic P, Kostal J, Marchant G, McPartland J, Moran K, Nel A, Ogunseitan O, Rossi M, Thayer K, Tickner J, Whittaker M, Zarker K. Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes. Integr Environ Assess Manag 2017; 13:915-925. [PMID: 28247928 DOI: 10.1002/ieam.1923] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 09/26/2016] [Accepted: 02/07/2017] [Indexed: 05/20/2023]
Abstract
Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC.
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Affiliation(s)
- Timothy Malloy
- School of Law, University of California Los Angeles (UCLA), Los Angeles, California, USA
- Fielding School of Public Health, UCLA, Los Angeles, California, USA
- UC Center for the Environmental Implications of Nanotechnology, UCLA, Los Angeles, California, USA
| | - Virginia Zaunbrecher
- School of Law, University of California Los Angeles (UCLA), Los Angeles, California, USA
- Fielding School of Public Health, UCLA, Los Angeles, California, USA
| | - Elizabeth Beryt
- UC Center for the Environmental Implications of Nanotechnology, UCLA, Los Angeles, California, USA
| | - Richard Judson
- National Center for Computational Toxicology, Research Triangle Park, North Carolina, USA
| | - Raymond Tice
- National Toxicology Program, Durham, North Carolina, USA
| | - Patrick Allard
- Fielding School of Public Health, UCLA, Los Angeles, California, USA
- Institute for Society & Genetics, UCLA, Los Angeles, California, USA
| | - Ann Blake
- Environmental and Public Health Consulting, Alameda, California, USA
| | - Ila Cote
- US Environmental Protection Agency, Washington, DC
| | - Hilary Godwin
- Fielding School of Public Health, UCLA, Los Angeles, California, USA
- UC Center for the Environmental Implications of Nanotechnology, UCLA, Los Angeles, California, USA
| | | | - Patrick Kerzic
- California Department of Toxic Substances Control, Chatsworth, California, USA
| | - Jakub Kostal
- Computational Biology Institute at the George Washington University, Ashburn, Virginia, USA
| | - Gary Marchant
- Sandra Day O'Connor School of Law, Arizona State University, Tempe, Arizona, USA
| | | | - Kelly Moran
- TDC Environmental, San Mateo, California, USA
| | - Andre Nel
- UC Center for the Environmental Implications of Nanotechnology, UCLA, Los Angeles, California, USA
| | - Oladele Ogunseitan
- School of Public Health, University of California Irvine (UCI), Irvine, California, USA
| | - Mark Rossi
- Clean Production Action, Somerville, Massachusetts, USA
| | | | - Joel Tickner
- University of Massachusetts, Lowell, Massachusetts, USA
| | | | - Ken Zarker
- Washington State Department of Ecology, Olympia,, Washington,, USA
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Blackwell BR, Ankley GT, Corsi SR, DeCicco LA, Houck K, Judson R, Li S, Martin M, Murphy E, Schroeder AL, Smith ET, Swintek J, Villeneuve DL. An "EAR" on Environmental Surveillance and Monitoring: A Case Study on the Use of Exposure-Activity Ratios (EARs) to Prioritize Sites, Chemicals, and Bioactivities of Concern in Great Lakes Waters. Environ Sci Technol 2017; 51:8713-8724. [PMID: 28671818 PMCID: PMC6132252 DOI: 10.1021/acs.est.7b01613] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on concentration alone, it can be difficult to identify which compounds may be of toxicological concern and should be prioritized for further monitoring, in-depth testing, or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high-throughput screening (HTS) data, such as the ToxCast database, which contains information for over 9000 compounds screened through up to 1100 bioassays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast effects database were used to calculate exposure-activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Prioritized bioactivities from the EAR analysis were linked to discrete adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts.
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Affiliation(s)
- Brett R. Blackwell
- US EPA, Mid-Continent Ecology Division, 6201 Congdon Blvd, Duluth, MN, USA 55804
- Corresponding author: 6201 Congdon Blvd, Duluth, MN 55804; ; T: (218) 529-5078; Fax: (218) 529-5003
| | - Gerald T. Ankley
- US EPA, Mid-Continent Ecology Division, 6201 Congdon Blvd, Duluth, MN, USA 55804
| | - Steve R. Corsi
- US Geological Survey, Wisconsin Water Science Center, 8505 Research Way, Middleton, WI, USA 53562
| | - Laura A. DeCicco
- US Geological Survey, Wisconsin Water Science Center, 8505 Research Way, Middleton, WI, USA 53562
| | - Keith Houck
- US EPA, National Center for Computational Toxicology, 109 T.W. Alexander Dr, Research Triangle Park, NC, USA 27711
| | - Richard Judson
- US EPA, National Center for Computational Toxicology, 109 T.W. Alexander Dr, Research Triangle Park, NC, USA 27711
| | - Shibin Li
- US EPA, Mid-Continent Ecology Division, 6201 Congdon Blvd, Duluth, MN, USA 55804
- National Research Council, US EPA, 6201 Congdon Blvd, Duluth, MN, USA 55804
| | - Matt Martin
- US EPA, National Center for Computational Toxicology, 109 T.W. Alexander Dr, Research Triangle Park, NC, USA 27711
| | - Elizabeth Murphy
- US EPA, Great Lakes National Program Office, 77 West Jackson Blvd, Chicago, IL, USA 60604
| | - Anthony L. Schroeder
- University of Minnesota Crookston, Math, Science, and Technology Department, 2900 University Ave, Crookston, MN, USA 56716
| | - Edwin T. Smith
- US EPA, Great Lakes National Program Office, 77 West Jackson Blvd, Chicago, IL, USA 60604
| | - Joe Swintek
- Badger Technical Services, 6201 Congdon Blvd, Duluth, MN, USA 55804
| | - Daniel L. Villeneuve
- US EPA, Mid-Continent Ecology Division, 6201 Congdon Blvd, Duluth, MN, USA 55804
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Tee M, Lang U, Durieux E, Jorapur A, Shain A, Haddad V, Pissaloux D, Chen X, Cerroni L, Judson R, LeBoit P, McCalmont T, Bastian B, de la Fouchardiere A. 148 Combined activation of MAP kinase and beta-catenin signaling define deep penetrating nevi. J Invest Dermatol 2017. [DOI: 10.1016/j.jid.2017.02.162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Kleinstreuer NC, Ceger P, Watt ED, Martin M, Houck K, Browne P, Thomas RS, Casey WM, Dix DJ, Allen D, Sakamuru S, Xia M, Huang R, Judson R. Development and Validation of a Computational Model for Androgen Receptor Activity. Chem Res Toxicol 2016; 30:946-964. [PMID: 27933809 PMCID: PMC5396026 DOI: 10.1021/acs.chemrestox.6b00347] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 μM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity.
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Affiliation(s)
- Nicole C Kleinstreuer
- NIH/NIEHS/DNTP/The NTP Interagency Center for the Evaluation of Alternative Toxicological Methods , Research Triangle Park, North Carolina 27713, United States
| | - Patricia Ceger
- Integrated Laboratory Systems, Inc. , Research Triangle Park, North Carolina 27560, United States
| | - Eric D Watt
- EPA/ORD/National Center for Computational Toxicology , Research Triangle Park, North Carolina 27711, United States
| | - Matthew Martin
- EPA/ORD/National Center for Computational Toxicology , Research Triangle Park, North Carolina 27711, United States
| | - Keith Houck
- EPA/ORD/National Center for Computational Toxicology , Research Triangle Park, North Carolina 27711, United States
| | - Patience Browne
- OECD Environment Directorate, Environment Health and Safety Division , Paris 75775, France
| | - Russell S Thomas
- EPA/ORD/National Center for Computational Toxicology , Research Triangle Park, North Carolina 27711, United States
| | - Warren M Casey
- NIH/NIEHS/DNTP/The NTP Interagency Center for the Evaluation of Alternative Toxicological Methods , Research Triangle Park, North Carolina 27713, United States
| | - David J Dix
- EPA/OCSPP/Office of Science Coordination and Policy , Washington, DC, 20460, United States
| | - David Allen
- Integrated Laboratory Systems, Inc. , Research Triangle Park, North Carolina 27560, United States
| | - Srilatha Sakamuru
- NIH/National Center for Advancing Translational Sciences , Bethesda, Maryland 20892, United States
| | - Menghang Xia
- NIH/National Center for Advancing Translational Sciences , Bethesda, Maryland 20892, United States
| | - Ruili Huang
- NIH/National Center for Advancing Translational Sciences , Bethesda, Maryland 20892, United States
| | - Richard Judson
- EPA/ORD/National Center for Computational Toxicology , Research Triangle Park, North Carolina 27711, United States
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Hill T, Nelms MD, Edwards SW, Martin M, Judson R, Corton JC, Wood CE. Editor’s Highlight: Negative Predictors of Carcinogenicity for Environmental Chemicals. Toxicol Sci 2016; 155:157-169. [DOI: 10.1093/toxsci/kfw195] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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Judson R, Houck K, Martin M, Richard AM, Knudsen TB, Shah I, Little S, Wambaugh J, Setzer RW, Kothiya P, Phuong J, Filer D, Smith D, Reif D, Rotroff D, Kleinstreuer N, Sipes N, Xia M, Huang R, Crofton K, Thomas RS. Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. Toxicol Sci 2016; 153:409. [PMID: 27605417 PMCID: PMC7297301 DOI: 10.1093/toxsci/kfw148] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Richarz A, Bois F, Exner T, Judson R, Mahony C, Ouedraogo G, Paini A, White A, Worth A, Berggren E. Applied chemical safety assessment case study for repeated dose toxicity integrating non-animal methods. Toxicol Lett 2016. [DOI: 10.1016/j.toxlet.2016.06.1807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Rosenberg S, Nikolov N, Dybdahl M, Simmons S, Crofton K, Watt E, Friedmann KP, Judson R, Wedebye E. Development of a QSAR Model for thyroperoxidase inhibition and screening of 72,526 REACH substances. Toxicol Lett 2016. [DOI: 10.1016/j.toxlet.2016.06.1475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Abstract
The US Environmental Protection Agency's (EPA) Endocrine Disruptor Screening Program (EDSP) is using in vitro data generated from ToxCast/Tox21 high-throughput screening assays to assess the endocrine activity of environmental chemicals. Considering that in vitro assays may have limited metabolic capacity, inactive chemicals that are biotransformed into metabolites with endocrine bioactivity may be missed for further screening and testing. Therefore, there is a value in developing novel approaches to account for metabolism and endocrine activity of both parent chemicals and their associated metabolites. We used commercially available software to predict metabolites of 50 parent compounds, out of which 38 chemicals are known to have estrogenic metabolites, and 12 compounds and their metabolites are negative for estrogenic activity. Three ER QSAR models were used to determine potential estrogen bioactivity of the parent compounds and predicted metabolites, the outputs of the models were averaged, and the chemicals were then ranked based on the total estrogenicity of the parent chemical and metabolites. The metabolite prediction software correctly identified known estrogenic metabolites for 26 out of 27 parent chemicals with associated metabolite data, and 39 out of 46 estrogenic metabolites were predicted as potential biotransformation products derived from the parent chemical. The QSAR models estimated stronger estrogenic activity for the majority of the known estrogenic metabolites compared to their parent chemicals. Finally, the three models identified a similar set of parent compounds as top ranked chemicals based on the estrogenicity of putative metabolites. This proposed in silico approach is an inexpensive and rapid strategy for the detection of chemicals with estrogenic metabolites and may reduce potential false negative results from in vitro assays.
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Affiliation(s)
- Caroline L Pinto
- Office of Chemical Safety and Pollution Prevention, US Environmental Protection Agency , 1200 Pennsylvania Avenue, N.W., Washington, DC 20460, United States.,Oak Ridge Institute for Science and Education , MC-100-44, P.O. Box 117, Oak Ridge, Tennessee 37831-0117, United States
| | - Kamel Mansouri
- Oak Ridge Institute for Science and Education , MC-100-44, P.O. Box 117, Oak Ridge, Tennessee 37831-0117, United States.,Office of Research and Development, US Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Richard Judson
- Office of Research and Development, US Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States
| | - Patience Browne
- Office of Chemical Safety and Pollution Prevention, US Environmental Protection Agency , 1200 Pennsylvania Avenue, N.W., Washington, DC 20460, United States
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Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J, Srinivasan S, Svoboda D, Judson R, Bucher JR, Thayer KA. Prioritizing Environmental Chemicals for Obesity and Diabetes Outcomes Research: A Screening Approach Using ToxCast™ High-Throughput Data. Environ Health Perspect 2016; 124:1141-54. [PMID: 26978842 PMCID: PMC4977057 DOI: 10.1289/ehp.1510456] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 10/09/2015] [Accepted: 02/08/2016] [Indexed: 05/23/2023]
Abstract
BACKGROUND Diabetes and obesity are major threats to public health in the United States and abroad. Understanding the role that chemicals in our environment play in the development of these conditions is an emerging issue in environmental health, although identifying and prioritizing chemicals for testing beyond those already implicated in the literature is challenging. This review is intended to help researchers generate hypotheses about chemicals that may contribute to diabetes and to obesity-related health outcomes by summarizing relevant findings from the U.S. Environmental Protection Agency (EPA) ToxCast™ high-throughput screening (HTS) program. OBJECTIVES Our aim was to develop new hypotheses around environmental chemicals of potential interest for diabetes- or obesity-related outcomes using high-throughput screening data. METHODS We identified ToxCast™ assay targets relevant to several biological processes related to diabetes and obesity (insulin sensitivity in peripheral tissue, pancreatic islet and β cell function, adipocyte differentiation, and feeding behavior) and presented chemical screening data against those assay targets to identify chemicals of potential interest. DISCUSSION The results of this screening-level analysis suggest that the spectrum of environmental chemicals to consider in research related to diabetes and obesity is much broader than indicated by research papers and reviews published in the peer-reviewed literature. Testing hypotheses based on ToxCast™ data will also help assess the predictive utility of this HTS platform. CONCLUSIONS More research is required to put these screening-level analyses into context, but the information presented in this review should facilitate the development of new hypotheses. CITATION Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J, Srinivasan S, Svoboda D, Judson R, Bucher JR, Thayer KA. 2016. Prioritizing environmental chemicals for obesity and diabetes outcomes research: a screening approach using ToxCast™ high-throughput data. Environ Health Perspect 124:1141-1154; http://dx.doi.org/10.1289/ehp.1510456.
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Affiliation(s)
- Scott Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
| | - Dayne Filer
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - David Reif
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Vickie Walker
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
| | - Alison C. Holloway
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada
| | - Jennifer Schlezinger
- Department of Environmental Health, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Supriya Srinivasan
- Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California, USA
| | - Daniel Svoboda
- SciOme, LLC, Research Triangle Park, North Carolina, USA
| | - Richard Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - John R. Bucher
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
| | - Kristina A. Thayer
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
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46
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Judson R, Houck K, Martin M, Richard AM, Knudsen TB, Shah I, Little S, Wambaugh J, Woodrow Setzer R, Kothiya P, Phuong J, Filer D, Smith D, Reif D, Rotroff D, Kleinstreuer N, Sipes N, Xia M, Huang R, Crofton K, Thomas RS. Editor's Highlight: Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. Toxicol Sci 2016; 152:323-39. [PMID: 27208079 DOI: 10.1093/toxsci/kfw092] [Citation(s) in RCA: 144] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 μM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.
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Affiliation(s)
- Richard Judson
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina;
| | - Keith Houck
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Matt Martin
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Ann M Richard
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Thomas B Knudsen
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Imran Shah
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Stephen Little
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - John Wambaugh
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - R Woodrow Setzer
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Parth Kothiya
- Contractor to the U.S. EPA National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Jimmy Phuong
- Contractor to the U.S. EPA National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Dayne Filer
- ORISE Fellow at the U.S. EPA National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Doris Smith
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - David Reif
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Daniel Rotroff
- Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | | | - Nisha Sipes
- National Toxicology Program, Research Triangle Park, North Carolina
| | - Menghang Xia
- NIH National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Ruili Huang
- NIH National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Kevin Crofton
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
| | - Russell S Thomas
- *U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, North Carolina
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47
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Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM, Grulke CM, Wambaugh JF, Isaacs KK, Judson R, Williams AJ, Sobus JR. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ Int 2016; 88:269-280. [PMID: 26812473 DOI: 10.1016/j.envint.2015.12.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Revised: 12/03/2015] [Accepted: 12/09/2015] [Indexed: 05/18/2023]
Abstract
There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPA's ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPA's Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC-TOF/molecular feature data (match score≥90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.
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Affiliation(s)
- Julia E Rager
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Mark J Strynar
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Shuang Liang
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Rebecca L McMahen
- Oak Ridge Institute for Science and Education (ORISE) Participant, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Christopher M Grulke
- Lockheed Martin, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Kristin K Isaacs
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Richard Judson
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States.
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48
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Deal S, Wambaugh J, Judson R, Mosher S, Radio N, Houck K, Padilla S. Development of a quantitative morphological assessment of toxicant-treated zebrafish larvae using brightfield imaging and high-content analysis. J Appl Toxicol 2016; 36:1214-22. [PMID: 26924781 DOI: 10.1002/jat.3290] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Revised: 11/22/2015] [Accepted: 12/15/2015] [Indexed: 11/11/2022]
Abstract
One of the rate-limiting procedures in a developmental zebrafish screen is the morphological assessment of each larva. Most researchers opt for a time-consuming, structured visual assessment by trained human observer(s). The present studies were designed to develop a more objective, accurate and rapid method for screening zebrafish for dysmorphology. Instead of the very detailed human assessment, we have developed the computational malformation index, which combines the use of high-content imaging with a very brief human visual assessment. Each larva was quickly assessed by a human observer (basic visual assessment), killed, fixed and assessed for dysmorphology with the Zebratox V4 BioApplication using the Cellomics® ArrayScan® V(TI) high-content image analysis platform. The basic visual assessment adds in-life parameters, and the high-content analysis assesses each individual larva for various features (total area, width, spine length, head-tail length, length-width ratio, perimeter-area ratio). In developing the computational malformation index, a training set of hundreds of embryos treated with hundreds of chemicals were visually assessed using the basic or detailed method. In the second phase, we assessed both the stability of these high-content measurements and its performance using a test set of zebrafish treated with a dose range of two reference chemicals (trans-retinoic acid or cadmium). We found the measures were stable for at least 1 week and comparison of these automated measures to detailed visual inspection of the larvae showed excellent congruence. Our computational malformation index provides an objective manner for rapid phenotypic brightfield assessment of individual larva in a developmental zebrafish assay. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Samantha Deal
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, NC, USA.,Division of Pediatrics Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, 6701, Fannin St., Houston, TX, USA
| | - John Wambaugh
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Richard Judson
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Shad Mosher
- ORISE Fellow, National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Nick Radio
- Thermo Fisher Scientific, Cellular Imaging and Analysis, Pittsburgh, PA, USA
| | - Keith Houck
- National Center for Computational Toxicology, US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Stephanie Padilla
- National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, NC, USA
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49
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Ryan N, Chorley B, Tice RR, Judson R, Corton JC. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium. Toxicol Sci 2016; 151:88-103. [PMID: 26865669 DOI: 10.1093/toxsci/kfw026] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells.
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Affiliation(s)
- Natalia Ryan
- *Oak Ridge Institute for Science and Education (ORISE) Integrated Systems Toxicology Division, US-EPA
| | | | - Raymond R Tice
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences
| | - Richard Judson
- National Center for Computational Toxicology, US-EPA, Research Triangle Park, North Carolina 27711
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50
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Wexler P, Judson R, de Marcellus S, de Knecht J, Leinala E. Health effects of toxicants: Online knowledge support. Life Sci 2016; 145:284-93. [PMID: 26506572 DOI: 10.1016/j.lfs.2015.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Revised: 09/26/2015] [Accepted: 10/02/2015] [Indexed: 11/26/2022]
Abstract
Research in toxicology generates vast quantities of data which reside on the Web and are subsequently appropriated and utilized to support further research. This data includes a broad spectrum of information about chemical, biological and radiological agents which can affect health, the nature of the effects, treatment, regulatory measures, and more. Information is structured in a variety of formats, including traditional databases, portals, prediction models, and decision making support tools. Online resources are created and housed by a variety of institutions, including libraries and government agencies. This paper focuses on three such institutions and the tools they offer to the public: the National Library of Medicine (NLM) and its Toxicology and Environmental Health Information Program, the United States Environmental Protection Agency (EPA), and the Organisation for Economic Co-operation and Development (OECD). Reference is also made to other relevant organizations.
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Affiliation(s)
- Philip Wexler
- National Library of Medicine (NLM), Toxicology and Environmental Health Information Program, USA
| | | | - Sally de Marcellus
- Organisation for Economic Co-operation and Development (OECD), Environment Directorate, Environment, Health and Safety Division, France
| | - Joop de Knecht
- Organisation for Economic Co-operation and Development (OECD), Environment Directorate, Environment, Health and Safety Division, France
| | - Eeva Leinala
- Organisation for Economic Co-operation and Development (OECD), Environment Directorate, Environment, Health and Safety Division, France
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