Georgopoulos Panos G, Wang Sheng-Wei, Yang Yu-Ching, Xue Jianping, Zartarian Valerie G, McCurdy Thomas, Ozkaynak Halûk
Environmental and Occupational Health Sciences Institute (EOHSI), A Joint Institute of UMDNJ--R.W. Johnson Medical School and Rutgers University, 170 Frelinghuysen Road, Piscataway, New Jersey 08854, USA.
J Expo Sci Environ Epidemiol. 2008 Sep;18(5):462-76. doi: 10.1038/sj.jes.7500637. Epub 2007 Dec 12.
This article presents an integrated, biologically based, source-to-dose assessment framework for modeling multimedia/multipathway/multiroute exposures to arsenic. Case studies demonstrating this framework are presented for three US counties (Hunderton County, NJ; Pima County, AZ; and Franklin County, OH), representing substantially different conditions of exposure. The approach taken utilizes the Modeling ENvironment for TOtal Risk studies (MENTOR) in an implementation that incorporates and extends the approach pioneered by Stochastic Human Exposure and Dose Simulation (SHEDS), in conjunction with a number of available databases, including NATA, NHEXAS, CSFII, and CHAD, and extends modeling techniques that have been developed in recent years. Model results indicate that, in most cases, the food intake pathway is the dominant contributor to total exposure and dose to arsenic. Model predictions are evaluated qualitatively by comparing distributions of predicted total arsenic amounts in urine with those derived using biomarker measurements from the NHEXAS--Region V study: the population distributions of urinary total arsenic levels calculated through MENTOR and from the NHEXAS measurements are in general qualitative agreement. Observed differences are due to various factors, such as interindividual variation in arsenic metabolism in humans, that are not fully accounted for in the current model implementation but can be incorporated in the future, in the open framework of MENTOR. The present study demonstrates that integrated source-to-dose modeling for arsenic can not only provide estimates of the relative contributions of multipathway exposure routes to the total exposure estimates, but can also estimate internal target tissue doses for speciated organic and inorganic arsenic, which can eventually be used to improve evaluation of health risks associated with exposures to arsenic from multiple sources, routes, and pathways.
本文提出了一个基于生物学的综合源到剂量评估框架，用于模拟砷的多媒体/多途径/多路线暴露。针对美国三个县（新泽西州亨德顿县、亚利桑那州皮马县和俄亥俄州富兰克林县）呈现了证明该框架的案例研究，这些县代表了截然不同的暴露条件。所采用的方法在实施过程中利用了用于总风险研究的建模环境（MENTOR），它结合并扩展了随机人类暴露和剂量模拟（SHEDS）开创的方法，同时结合了多个可用数据库，包括国家空气毒物评估（NATA）、国家人类暴露分析调查系统（NHEXAS）、美国农业部个人食物摄入量连续调查（CSFII）和综合人类活动数据库（CHAD），并扩展了近年来开发的建模技术。模型结果表明，在大多数情况下，食物摄入途径是砷总暴露和剂量的主要贡献者。通过将尿液中预测的总砷量分布与使用NHEXAS - 第五区域研究中的生物标志物测量得出的分布进行比较，对模型预测进行了定性评估：通过MENTOR计算得出的尿总砷水平的总体分布与NHEXAS测量结果在总体上定性一致。观察到的差异是由多种因素导致的，例如人类砷代谢的个体间差异，这些因素在当前模型实施中没有得到充分考虑，但在MENTOR的开放框架下未来可以纳入。本研究表明，砷的综合源到剂量建模不仅可以提供多途径暴露途径对总暴露估计的相对贡献估计，还可以估计特定有机和无机砷的内部靶组织剂量，最终可用于改进对与多种来源、途径和路线的砷暴露相关的健康风险的评估。
