A statistical assessment on the stochastic relationship between biomarker concentrations and environmental exposures

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.

Section: Introductionmentioning

confidence: 99%

Exposure estimation using repeated blood concentration measurements

Chen

Shih

2009

“…In addition, occupational health and safety organizations utilize the levels of parent contaminants or metabolites in biological matrices (biomarkers) to assess exposures to the parent contaminants (known as biological exposure indices, BEI). However, the relationships between exposure and biomarkers are not known because of their dependence on physiological parameters, kinetics of metabolism and exposure characteristics (duration, magnitude, frequency, co-exposures) (Chen et al 2004). Non-linear models and physiologically based toxicokinetic and pharmacokinetic models have been applied to describe the stochastic characteristics of exposure characterization using biomarkers (Chen et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Exposure characterization is instrumental in case-cohort and longitudinal epidemiological studies, and health risk assessments (Maxwell and Kastenberg 1999;Mutshinda et al 2008;Chen et al 2004). Biological monitoring evolved into an exposure assessment method to measure the total amount of a chemical contaminant present in the human body (Wiwanitkit et al 2008;Chen et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Effects of smoking on the levels of urinary biomarkers of aromatic hydrocarbons in oil refinery workers

Chalbot

Vei

Kavouras

et al. 2012

Occupational exposures to aromatic hydrocarbons may be overwhelmed by mainstream and secondary smoking exposures. The objective of this study was to evaluate the influence of occupational exposures and smoking on benzene and toluene urinary metabolites. The metabolites were measured in pre and post-work shift urine specimens in oil refinery workers by gas chromatography/ mass spectrometry techniques. Post-shift concentration differences between non-smokers and smokers were statistically significant (at p-value \ 0.05). Pre-and post-shift concentrations of urinary phenol (benzene biomarker) did not vary significantly for both non-smokers and smokers. Occupational exposures to toluene triggered an increase of post-shift levels (compared to pre-shift levels) of urinary cresol for non-smokers and workers who had less than 10 cigarettes during the work shift. For these groups, nonoccupational exposures before and during the work shift did not vary. For smokers who did not smoke during the work shift and for those who had more than ten cigarettes during the shift, the post-shift levels of both cresol and hippuric acid were reduced approximately 30% lower than pre-shift levels. This is due to increased exposures to tobacco smoke before the work shift. The relationships between phenol and (cresol ? hippuric acid) levels for non-smokers and smokers indicated that elevated coexposures due to smoking result in the reduction of all metabolites levels in urine. These findings demonstrated that exposures to tobacco smoke may stochastically interfere with occupational exposures when biological monitoring is used to assess occupational health risks. Factors influencing the magnitude of the interference were specimen collection time (in relation to the timing of occupational exposures and excretion rates of biomarkers), smoking intensity and timing before and during the work shift.

“…Assessing environmental exposures to pollutants over time is of great importance to environmental epidemiology studies and health risk assessments (Chen et al 2004(Chen et al , 2010Maxwell and Kastenberg 1999;Mutshinda et al 2008), helping to protect the public and safeguard occupational health. However, detailed exposure information is seldom available and is often confounded by interindividual and spatiotemporal variations.…”

Section: Introductionmentioning

confidence: 99%

Exposure reconstruction using a physiologically based toxicokinetic model with cumulative amount of metabolite in urine: a case study of trichloroethylene inhalation

Chen

Shih

2011

The use of a physiologically based toxicokinetic (PBTK) model to reconstruct chemical exposure using human biomonitoring data, urinary metabolites in particular, has not been fully explored. In this paper, the trichloroethylene (TCE) exposure dataset by Fisher et al. (Toxicol Appl Pharm 152:339-359, 1998) was reanalyzed to investigate this new approach. By treating exterior chemical exposure as an unknown model parameter, a PBTK model was used to estimate exposure and model parameters by measuring the cumulative amount of trichloroethanol glucuronide (TCOG), a metabolite of TCE, in voided urine and a single blood sample of the study subjects by Markov chain Monte Carlo (MCMC) simulations. An estimated exterior exposure of 0.532 mg/l successfully reconstructed the true inhalation concentration of 0.538 mg/l with a 95% CI (0.441-0.645) mg/l. Based on the simulation results, a feasible urine sample collection period would be 12-16 h after TCE exposure, with blood sampling at the end of the exposure period. Given the known metabolic pathway and exposure duration, the proposed computational procedure provides a simple and reliable method for environmental (occupational) exposure and PBTK model parameter estimation, which is more feasible than repeated blood sampling.