Zachary Stanfield1, R Woodrow Setzer1, Victoria Hull1,2, Risa R Sayre1, Kristin K Isaacs1, John F Wambaugh3. 1. Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA. 2. Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37830, USA. 3. Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA. Wambaugh.john@epa.gov.
Abstract
BACKGROUND: Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data. OBJECTIVE: Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty. METHODS: Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts. RESULTS: Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015-2016 NHANES cohort. SIGNIFICANCE: The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.
BACKGROUND: Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data. OBJECTIVE: Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty. METHODS: Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts. RESULTS: Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015-2016 NHANES cohort. SIGNIFICANCE: The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.
Authors: Barbara A Wetmore; John F Wambaugh; Stephen S Ferguson; Mark A Sochaski; Daniel M Rotroff; Kimberly Freeman; Harvey J Clewell; David J Dix; Melvin E Andersen; Keith A Houck; Brittany Allen; Richard S Judson; Reetu Singh; Robert J Kavlock; Ann M Richard; Russell S Thomas Journal: Toxicol Sci Date: 2011-09-26 Impact factor: 4.849
Authors: John F Wambaugh; R Woodrow Setzer; David M Reif; Sumit Gangwal; Jade Mitchell-Blackwood; Jon A Arnot; Olivier Joliet; Alicia Frame; James Rabinowitz; Thomas B Knudsen; Richard S Judson; Peter Egeghy; Daniel Vallero; Elaine A Cohen Hubal Journal: Environ Sci Technol Date: 2013-07-11 Impact factor: 9.028
Authors: John F Wambaugh; Anran Wang; Kathie L Dionisio; Alicia Frame; Peter Egeghy; Richard Judson; R Woodrow Setzer Journal: Environ Sci Technol Date: 2014-10-24 Impact factor: 9.028
Authors: Jürgen Angerer; Michael G Bird; Thomas A Burke; Nancy G Doerrer; Larry Needham; Steven H Robison; Linda Sheldon; Hal Zenick Journal: Toxicol Sci Date: 2006-06-19 Impact factor: 4.849
Authors: Richard S Judson; Robert J Kavlock; R Woodrow Setzer; Elaine A Cohen Hubal; Matthew T Martin; Thomas B Knudsen; Keith A Houck; Russell S Thomas; Barbara A Wetmore; David J Dix Journal: Chem Res Toxicol Date: 2011-03-08 Impact factor: 3.739
Authors: Jon R Sobus; Robert S DeWoskin; Yu-Mei Tan; Joachim D Pleil; Martin Blake Phillips; Barbara Jane George; Krista Christensen; Dina M Schreinemachers; Marc A Williams; Elaine A Cohen Hubal; Stephen W Edwards Journal: Environ Health Perspect Date: 2015-04-10 Impact factor: 9.031