The appropriate use of human biomonitoring data to model population chemical exposures is challenging, especially for rapidly metabolized chemicals, such as agricultural chemicals. The objective of this study is to demonstrate a novel approach integrating model predicted dietary exposures and biomonitoring data to potentially inform regulatory risk assessments. We use lambda-cyhalothrin as a case study, and for the same representative U.S. population in the National Health and Nutrition Examination Survey (NHANES), an integrated exposure and pharmacokinetic model predicted exposures are calibrated to measurements of the urinary metabolite 3-phenoxybenzoic acid (3PBA), using an approximate Bayesian computing (ABC) methodology. We demonstrate that the correlation between modeled urinary 3PBA and the NHANES 3PBA measurements more than doubled as ABC thresholding narrowed the acceptable tolerance range for predicted versus observed urinary measurements. The median predicted urinary concentrations were closer to the median measured value using ABC than using current regulatory Monte Carlo methods.

中文翻译：

---

将人体生物监测数据与模型预测膳食暴露相结合的新方法：使用高效氯氟氰菊酯的作物保护化学品案例研究


适当使用人体生物监测数据来模拟人群化学品暴露情况具有挑战性，特别是对于快速代谢的化学品，例如农用化学品。本研究的目的是展示一种整合模型预测饮食暴露和生物监测数据的新方法，为监管风险评估提供潜在信息。我们使用高效氯氟氰菊酯作为案例研究，对于国家健康和营养检查调查 (NHANES) 中的同一美国代表性人群，综合暴露和药代动力学模型预测的暴露量根据尿代谢物 3-苯氧基苯甲酸的测量值进行校准（ 3PBA），使用近似贝叶斯计算（ABC）方法。我们证明，随着 ABC 阈值缩小了预测与观察到的尿液测量值的可接受容差范围，模型尿液 3PBA 与 NHANES 3PBA 测量值之间的相关性增加了一倍以上。使用 ABC 比使用当前监管蒙特卡罗方法的中位预测尿浓度更接近中位测量值。