Peyret Thomas, Krishnan Kannan
Département de Santé Environnementale et Santé au Travail, Université de Montréal, C.P. 6128, Succursale Centre-Ville, Montréal, QC, Canada H3C 3J7.
J Toxicol. 2012;2012:286079. doi: 10.1155/2012/286079. Epub 2012 May 22.
The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CL(int)) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CL(int), calculated as the ratio of the in vivo V(max) (μmol/h/kg bw rat) to the K(m) (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R(2) = 0.8; leave-one-out cross-validation Q(2) = 0.75) for CL(int) normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CL(int) (L PL/h/kg bw rat) from the input data on log P(ow), log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CL(int)) to minimum (using UMCI for CL(int)) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CL(int) in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CL(int) on human pharmacokinetics of VOCs.
（i）开发一种筛选水平的定量性质-性质关系（QPPR），用于从体内动物研究中获得的内在清除率（CL(int)）；（ii）将其与人体生理学整合到一个生理药代动力学（PBPK）模型中，以预测挥发性有机化合物（VOCs）的吸入药代动力学。从文献中获取了26种VOCs的CL(int)，其计算方法为体内V(max)（μmol/h/kg bw大鼠）与K(m)（μM）的比值。通过逐步线性回归分析得到的QPPR模型，对于根据VOCs的磷脂（PL）亲和力进行归一化的CL(int)，通过了验证步骤（R(2) = 0.8；留一法交叉验证Q(2) = 0.75）。QPPR有助于根据log P(ow)、log血：水分配系数（PC）和电离势的输入数据计算CL(int)（L PL/h/kg bw大鼠）。然后，将QPPR预测的作为95%平均置信区间下限和上限（分别为LMCI和UMCI）整合到人体PBPK模型中。QPPR-PBPK模型预测的最大（使用CL(int)的LMCI）与最小（使用CL(int)的UMCI）AUC之比为1.36 ± 0.4，范围从1.06（1,1-二氯乙烯）到2.8（异戊二烯）。总体而言，本研究中开发的整合QPPR-PBPK建模方法是一种务实的方式，用于描述在预测VOCs人体药代动力学时CL(int)知识缺乏的影响，以及CL(int)预测不确定性对VOCs人体药代动力学的影响。
