Using publicly available data, a physiologically-based pharmacokinetic model and Bayesian simulation to improve arsenic non-cancer dose-response.
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 0.028μg/kg/day, respectively. Meanwhile, using National Health and Nutrition Examination Survey (NHANES, 2011-2012) data, the fraction of urinary As(III) levels (geometric mean: 0.31μg/L) in tAs (geometric mean: 7.75μg/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 0.8μg/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.
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.
Zhaomin
Z
Dong
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.
CuiXia
C
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.
Yanju
Y
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
K
Yan
Lancaster Environment Centre, Lancaster University, LA1 4YQ Lancaster, United Kingdom.
Kirk T
KT
Semple
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. Electronic address: ravi.naidu@crccare.com.
Ravi
R
Naidu
D001151:Arsenic / Q000008:administration & dosage / Q000493:pharmacokinetics*; D001499:Bayes Theorem; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D004785:Environmental Pollutants / Q000008:administration & dosage / Q000493:pharmacokinetics; D006801:Humans; D008954:Models, Biological; D009749:Nutrition Surveys; D018570:Risk Assessment
D004785:Environmental Pollutants; D001151:Arsenic
Arsenic; Bayesian simulation; Dose response; PBPK model; Publicly available data