Using publicly available data, a physiologically-based pharmacokinetic model and Bayesian simulation to improve arsenic non-cancer dose-response

Environment International. 2016;92:239-246

 

Journal Homepage

Journal Title: Environment International

ISSN: 0160-4120 (Print)

Publisher: Elsevier

LCC Subject Category: Geography. Anthropology. Recreation: Environmental sciences

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Zhaomin Dong (Global Centre for Environmental Remediation, Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia)
CuiXia Liu (Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia; School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)
Yanju Liu (Global Centre for Environmental Remediation, Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia)
Kaihong Yan (Global Centre for Environmental Remediation, Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia)
Kirk T. Semple (Lancaster Environment Centre, Lancaster University, LA1 4YQ Lancaster, United Kingdom)
Ravi Naidu (Global Centre for Environmental Remediation, Faculty of Science and Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Mawson Lakes, SA 5095, Australia; Corresponding author at: ATC Building, Global Center for Environmental Remediation, Faculty of Science and Information Technology, University of Newcastle, Callaghan, NSW 2308, Australia.)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 12 weeks

 

Abstract | Full Text

Publicly available data can potentially examine the relationship between environmental exposure and public health, however, it has not yet been widely applied. Arsenic is of environmental concern, and previous studies mathematically parameterized exposure duration to create a link between duration of exposure and increase in risk. However, since the dose metric emerging from exposure duration is not a linear or explicit variable, it is difficult to address the effects of exposure duration simply by using mathematical functions. To relate cumulative dose metric to public health requires a lifetime physiologically-based pharmacokinetic (PBPK) model, yet this model is not available at a population level. In this study, the data from the U.S. total diet study (TDS, 2006–2011) was employed to assess exposure: daily dietary intakes for total arsenic (tAs) and inorganic arsenic (iAs) were estimated to be 0.15 and 0.028 μg/kg/day, respectively. Meanwhile, using National Health and Nutrition Examination Survey (NHANES, 2011–2012) data, the fraction of urinary As(III) levels (geometric mean: 0.31 μg/L) in tAs (geometric mean: 7.75 μg/L) was firstly reported to be approximately 4%. Together with Bayesian technique, the assessed exposure and urinary As(III) concentration were input to successfully optimize a lifetime population PBPK model. Finally, this optimized PBPK model was used to derive an oral reference dose (Rfd) of 0.8 μg/kg/day for iAs exposure. Our study also suggests the previous approach (by using mathematical functions to account for exposure duration) may result in a conservative Rfd estimation. Keywords: PBPK model, Dose response, Bayesian simulation, Arsenic, Publicly available data