Exposure estimation using repeated blood concentration measurements |
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| Authors: | Chu-Chih Chen Meng-Chiuan Shih Kuen-Yuh Wu |
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| Institution: | (1) Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan;(2) Department of Mathematics, Tamkang University, Taipei, Taiwan;(3) Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei, Taiwan |
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| Abstract: | Physiologically based toxicokinetic (PBTK) modeling has been well established to study the distributions of chemicals in target
tissues. In addition, the hierarchical Bayesian statistical approach using Markov Chain Monte Carlo (MCMC) simulations has
been applied successfully for parameter estimation. The aim was to estimate the constant inhalation exposure concentration
(assumed) using a PBTK model based on repeated measurements in venous blood, so that exposures could be estimated. By treating
the constant exterior exposure as an unknown parameter of a four-compartment PBTK model, we applied MCMC simulations to estimate
the exposure based on a hierarchical Bayesian approach. The dataset on 16 volunteers exposed to 100 ppm (≅0.538 mg/L) trichloroethylene
vapors for 4 h was reanalyzed as an illustration. Cases of time-dependent exposures with a constant mean were also studied
via 100 simulated datasets. The posterior geometric mean of 0.571, with narrow 95% posterior confidence interval (CI) (0.506,
0.645), estimated the true trichloroethylene inhalation concentration (0.538 mg/L) with very high precision. Also, the proposed
method estimated the overall constant mean of the simulated time-dependent exposure scenarios well with slightly wider 95%
CIs. The proposed method justifies the accuracy of exposure estimation from biomonitoring data using PBTK model and MCMC simulations
from a real dataset and simulation studies numerically, which provides a starting point for future applications in occupational
exposure assessment. |
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