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FACTORS INFLUENCING PREDICTION OF BROMODICHLOROMETHANE (BDCM) IN EXHALED BREATH: FURTHER EVALUATION OF A HUMAN BDCM PBPK MODEL
Citation:
Kenyon, E., C. Eklund, J. Simmons, AND R. Pegram. FACTORS INFLUENCING PREDICTION OF BROMODICHLOROMETHANE (BDCM) IN EXHALED BREATH: FURTHER EVALUATION OF A HUMAN BDCM PBPK MODEL. Society of Toxicology, San Antonio, Texas, March 11 - 15, 2018.
Impact/Purpose:
When comparing PBPK model-predicted exhaled breath bromodichloromethane concentration to data from independent exposure studies, a clear understanding of study design and sampling methods is critical. Expanded utility of human exposure data can also be achieved by utilization of PBPK modeling in the study design phase.
Description:
Confidence in the predictive capability of a PBPK model is increased when the model is demonstrated to predict multiple pharmacokinetic outcomes from diverse studies under different exposure conditions. We previously showed that our multi-route human BDCM PBPK model adequately (within ~2.5-fold) predicts both blood and urine BDCM concentration data from human exposure studies; activities in these studies included drinking, bathing, showering and swimming. Here, we evaluated the ability of the model to predict an exposure biomarker, the concentration of BDCM in exhaled breath (exBDCM). Four human subject studies of swimmers (dermal, inhalation exposure) and/or pool attendants (inhalation only) were modeled. The model adequately predicted exBDCM for both sedentary (pool monitors) and exercising (swimmers) subjects in two studies. In contrast, exBDCM was over-predicted by a factor of 3- to 5-fold for swimmers in a third study and under-predicted by 4- to 5-fold for sedentary subjects in a fourth study. The model’s ability to predict exBDCM was better in studies where sufficient data were available to estimate alveolar ventilation rate and cardiac output, e.g. time and distance swum, estimated energy expenditure. Under-prediction of exBDCM may be attributable sample dilution due to collection of total exhaled breath. Model parameters have varying degrees of influence on model output. Global sensitivity analysis revealed that the most influential parameters affecting exBDCM were: blood-to-air partition coefficient, cardiac output, alveolar ventilation rate, and skin diffusion coefficient. Of these parameters, cardiac output and alveolar ventilation rate have the greatest variability and uncertainty, especially in exercising subjects. In summary, when comparing PBPK model-predicted exBDCM to data from independent exposure studies, a clear understanding of study design and sampling methods is critical. Further, expanded utility of human exposure data can be achieved by utilization of PBPK modeling in the study design phase. (This abstract does not reflect U.S. EPA policy).