Reconstructing population exposures from dose biomarkers: inhalation of trichloroethylene (TCE) as a case study

Abstract

Physiologically based pharmacokinetic (PBPK) modeling is a well-established toxicological tool designed to relate exposure to a target tissue dose. The emergence of federal and state programs for environmental health tracking and the availability of exposure monitoring through biomarkers creates the opportunity to apply PBPK models to estimate exposures to environmental contaminants from urine, blood, and tissue samples. However, reconstructing exposures for large populations is complicated by often having too few biomarker samples, large uncertainties about exposures, and large interindividual variability. In this paper, we use an illustrative case study to identify some of these difficulties, and for a process for confronting them by reconstructing population-scale exposures using Bayesian inference. The application consists of interpreting biomarker data from eight adult males with controlled exposures to trichloroethylene (TCE) as if the biomarkers were random samples from a large population with unknown exposure conditions. The TCE concentrations in blood from the individuals fell into two distinctly different groups even though the individuals were simultaneously in a single exposure chamber. We successfully reconstructed the exposure scenarios for both subgroups F although the reconstruction of one subgroup is different than what is believed to be the true experimental conditions. We were however unable to predict with high certainty the concentration of TCE in air.

Exposure information in environmental health research: Current opportunities and future directions for particulate matter, ozone, and toxic air pollutants

“…It is important to recognize that even when there is an abundance of information on exposure biomarkers, there may not be sufficient information to establish source-to-dose links. For example (Sohn et al, 2004) have illustrated some of the problems in dose reconstruction even in cases where there is good temporal data on tissue concentrations. As noted by Ryan et al (2007) there is a continuum of information that must be assembled in order to use exposure biomarker data to establish source-to-dose links.…”

Biomonitoring equivalents: A screening approach for interpreting biomonitoring results from a public health risk perspective

“…The reverse dosimetry approach has the advantage that the result of the modeling will yield an applied dose that can then be readily compared to existing regulatory guidance values (e.g., RfDs, RfCs, UCRs, etc.). The major disadvantage is that this modeling approach requires an inverse solution for a system with numerous variables in which there is no unique solution (Rigas et al, 2001;Sohn et al, 2004) (i.e., there are, within limits, any number of exposure scenarios that could yield the same biomonitoring level). In starting with a population-based distribution of biomonitoring levels (i.e., a range of biomonitoring levels), reverse dosimetry requires back-calculating to a population distribution of applied doses, yielding a wide range of potential exposures associated with the observed distribution in biomonitoring data.…”

“…In starting with a population-based distribution of biomonitoring levels (i.e., a range of biomonitoring levels), reverse dosimetry requires back-calculating to a population distribution of applied doses, yielding a wide range of potential exposures associated with the observed distribution in biomonitoring data. Researchers have attempted to back-calculate exposures from biomonitoring data, with discouraging results due to the variability in potential exposure scenarios and human pharmacokinetics (see for example Rigas et al, 2001;Sohn et al, 2004).…”

Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

“…Subsequent measurements of their blood showed two distinct groups with different levels of poison in their blood stream. Sohn et al (2004) conclude that any risk analysis requires sufficient and reliable information, and suggest that as uncertainty can, in theory, be reduced it is always worth considering if there are any additional exposure data likely or practical to be obtained.…”

“…Sohn et al (2004), in their epidemiological study evaluating the measurement of exposure to trichloroethylene demonstrate the difficulty in estimating exposure from uncertain data. This research used a Bayesian model in a controlled experiment where a group of men were exposed, in the same room, to a fixed level of poison in air.…”