Kodell R L, Chen J J, Delongchamp R R, Young J F
Division of Biometry and Risk Assessment, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
Regul Toxicol Pharmacol. 2006 Aug;45(3):265-72. doi: 10.1016/j.yrtph.2006.05.002.
Probabilistic risk assessment is gaining acceptance as the most appropriate way to characterize and communicate uncertainties in estimates of human health risk and/or reference levels of exposure such as benchmark doses. Although probabilistic techniques are well established in the exposure-assessment component of the National Research Council's risk-assessment paradigm, they are less well developed in the dose-response-assessment component. This paper proposes the use of hierarchical statistical models as tools for implementing probabilistic dose-response assessments, in that such models provide a natural connection between the pharmacokinetic (PK) and pharmacodynamic (PD) components of dose-response models. The results show that incorporating internal dose information into dose-response assessments via the coupling of PK and PD models in a hierarchical structure can reduce the uncertainty in the dose-response assessment of risk. However, information on the mean of the internal dose distribution is sufficient; having information on the variance of internal dose does not affect the uncertainty in the resulting estimates of excess risks or benchmark doses. In addition, the complexity of a PK model of internal dose does not affect how the variability in risk is measured via the ultimate endpoint.
概率风险评估正逐渐被接受为表征和传达人类健康风险估计值及/或暴露参考水平（如基准剂量）中的不确定性的最适当方法。尽管概率技术在国家研究委员会风险评估范式的暴露评估部分已得到充分确立，但在剂量反应评估部分的发展却较为滞后。本文提议使用层次统计模型作为实施概率剂量反应评估的工具，因为此类模型在剂量反应模型的药代动力学（PK）和药效动力学（PD）成分之间提供了自然的联系。结果表明，通过在层次结构中耦合PK和PD模型将内部剂量信息纳入剂量反应评估，可以降低风险剂量反应评估中的不确定性。然而，关于内部剂量分布均值的信息就足够了；拥有内部剂量方差的信息并不会影响由此产生的超额风险或基准剂量估计值的不确定性。此外，内部剂量PK模型的复杂性不会影响通过最终终点测量风险变异性的方式。
