Tan Yu-Mei, Liao Kai H, Conolly Rory B, Blount Benjamin C, Mason Ann M, Clewell Harvey J
Center for Human Health Assessment, CIIT Centers for Health Research, Research Triangle Park, North Carolina 27709, USA.
J Toxicol Environ Health A. 2006 Sep;69(18):1727-56. doi: 10.1080/15287390600631367.
Biomonitoring data provide evidence of human exposure to environmental chemicals by quantifying the chemical or its metabolite in a biological matrix. To better understand the correlation between biomonitoring data and environmental exposure, physiologically based pharmacokinetic (PBPK) modeling can be of use. The objective of this study was to use a combined PBPK model with an exposure model for showering to estimate the intake concentrations of chloroform based on measured blood and exhaled breath concentrations of chloroform. First, the predictive ability of the combined model was evaluated with three published studies describing exhaled breath and blood concentrations in people exposed to chloroform under controlled showering events. Following that, a plausible exposure regimen was defined combining inhalation, ingestion, and dermal exposures associated with residential use of water containing typical concentrations of chloroform to simulate blood and exhaled breath concentrations of chloroform. Simulation results showed that inhalation and dermal exposure could contribute substantially to total chloroform exposure. Next, sensitivity analysis and Monte Carlo analysis were performed to investigate the sources of variability in model output. The variability in exposure conditions (e.g., shower duration) was shown to contribute more than the variability in pharmacokinetics (e.g., body weight) to the predicted variability in blood and exhaled breath concentrations of chloroform. Lastly, the model was used in a reverse dosimetry approach to estimate distributions of exposure consistent with concentrations of chloroform measured in human blood and exhaled breath.
生物监测数据通过量化生物基质中的化学物质或其代谢物来提供人类接触环境化学物质的证据。为了更好地理解生物监测数据与环境暴露之间的相关性,基于生理的药代动力学(PBPK)建模可能会有所帮助。本研究的目的是使用结合了淋浴暴露模型的PBPK模型,根据测得的血液和呼出气体中氯仿的浓度来估算氯仿的摄入浓度。首先,利用三项已发表的研究评估了该组合模型的预测能力,这些研究描述了在受控淋浴事件中接触氯仿的人的呼出气体和血液浓度。随后,定义了一种合理的暴露方案,结合了与使用含有典型浓度氯仿的家用自来水相关的吸入、摄入和皮肤暴露,以模拟氯仿的血液和呼出气体浓度。模拟结果表明,吸入和皮肤暴露对氯仿的总暴露有很大贡献。接下来,进行了敏感性分析和蒙特卡洛分析,以研究模型输出变异性的来源。结果表明,暴露条件(如淋浴时间)的变异性对氯仿血液和呼出气体浓度预测变异性的贡献大于药代动力学(如体重)的变异性。最后,该模型被用于反向剂量测定法,以估计与人体血液和呼出气体中测得的氯仿浓度一致的暴露分布。