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Elaine A Cohen Hubal  - - - 
Top co-authors See all
Olivier Jolliet

154 shared publications

Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States

Richard Judson

151 shared publications

U.S. EPA, Research Triangle Park, North Carolina, USA

David Reif

102 shared publications

Center for Human Health and the Environment, and Department of Biological Sciences, North Carolina State University

John Wambaugh

81 shared publications

National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States

Lucas M Neas

74 shared publications

National Health and Environmental Effects Research Laboratory, US Environmental Protection Agency, Research Triangle Park, Durham, NC, USA

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Publication Record
Distribution of Articles published per year 
(2000 - 2018)
Total number of journals
published in
 
16
 
Publications See all
Article 0 Reads 0 Citations Advancing internal exposure and physiologically-based toxicokinetic modeling for 21st-century risk assessments Elaine A. Cohen Hubal, Barbara A. Wetmore, John F. Wambaugh,... Published: 16 August 2018
Journal of Exposure Science & Environmental Epidemiology, doi: 10.1038/s41370-018-0046-9
DOI See at publisher website ABS Show/hide abstract
Scientifically sound, risk-informed evaluation of chemicals is essential to protecting public health. Systematically leveraging information from exposure, toxicology, and epidemiology studies can provide a holistic understanding of how real-world exposure to chemicals may impact the health of populations, including sensitive and vulnerable individuals and life-stages. Increasingly, public health policy makers are employing toxicokinetic (TK) modeling tools to integrate these data streams and predict potential human health impact. Development of a suite of tools for predicting internal exposure, including physiologically-based toxicokinetic (PBTK) models, is being driven by needs to address large numbers of data-poor chemicals efficiently, translate bioactivity, and mechanistic information from new in vitro test systems, and integrate multiple lines of evidence to enable scientifically sound, risk-informed decisions. New modeling approaches are being designed “fit for purpose” to inform specific decision contexts, with applications ranging from rapid screening of hundreds of chemicals, to improved prediction of risks during sensitive stages of development. New data are being generated experimentally and computationally to support these models. Progress to meet the demand for internal exposure and PBTK modeling tools will require transparent publication of models and data to build credibility in results, as well as opportunities to partner with decision makers to evaluate and build confidence in use of these for improved decisions that promote safe use of chemicals.
Article 0 Reads 13 Citations Uses of NHANES Biomarker Data for Chemical Risk Assessment: Trends, Challenges, and Opportunities Jon R. Sobus, Robert S. DeWoskin, Yu-Mei Tan, Joachim D. Ple... Published: 01 October 2015
Environmental Health Perspectives, doi: 10.1289/ehp.1409177
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Each year, the U.S. NHANES measures hundreds of chemical biomarkers in samples from thousands of study participants. These biomarker measurements are used to establish population reference ranges, track exposure trends, identify population subsets with elevated exposures, and prioritize research needs. There is now interest in further utilizing the NHANES data to inform chemical risk assessments.
Article 0 Reads 7 Citations Data-Driven Asthma Endotypes Defined from Blood Biomarker and Gene Expression Data Barbara Jane George, David M. Reif, Jane E. Gallagher, ClarL... Published: 02 February 2015
PLOS ONE, doi: 10.1371/journal.pone.0117445
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.
Article 0 Reads 12 Citations Comparison of modeling approaches to prioritize chemicals based on estimates of exposure and exposure potential Jade Mitchell, Jon A. Arnot, Olivier Jolliet, Panos G. Georg... Published: 01 August 2013
Science of The Total Environment, doi: 10.1016/j.scitotenv.2013.04.051
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Article 0 Reads 9 Citations High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project John F. Wambaugh, R. Woodrow Setzer, David Reif, Sumit Gangw... Published: 11 July 2013
Environmental Science & Technology, doi: 10.1021/es400482g
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Article 2 Reads 5 Citations Sustainability, Health and Environmental Metrics: Impact on Ranking and Associations with Socioeconomic Measures for 50 ... Jane E. Gallagher, Elaine Cohen Hubal, Laura Jackson, Jeffer... Published: 22 February 2013
Sustainability, doi: 10.3390/su5020789
DOI See at publisher website ABS Show/hide abstract
Waste and materials management, land use planning, transportation and infrastructure including water and energy can have indirect or direct beneficial impacts on the environment and public health. The potential for impact, however, is rarely viewed in an integrated fashion. To facilitate such an integrated view in support of community-based policy decision making, we catalogued and evaluated associations between common, publically available, Environmental (e), Health (h), and Sustainability (s) metrics and sociodemographic measurements (n = 10) for 50 populous U.S. cities. E, H, S indices combined from two sources were derived from component (e) (h) (s) metrics for each city. A composite EHS Index was derived to reflect the integration across the E, H, and S indices. Rank order of high performing cities was highly dependent on the E, H and S indices considered. When viewed together with sociodemographic measurements, our analyses further the understanding of the interplay between these broad categories and reveal significant sociodemographic disparities (e.g., race, education, income) associated with low performing cities. Our analyses demonstrate how publically available environmental, health, sustainability and socioeconomic data sets can be used to better understand interconnections between these diverse domains for more holistic community assessments.
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