首页> 美国卫生研究院文献>Journal of Toxicology >Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans
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Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

机译:定量性质与属性的关系用于筛查大鼠挥发性有机化合物的内在清除水平并将其整合到PBPK模型中以预测人类的吸入药代动力学

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摘要

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CLint) 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. CLint, 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 CLint normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CLint (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 CLint) to minimum (using UMCI for CLint) 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 CLint in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CLint on human pharmacokinetics of VOCs.
机译:这项研究的目的是(i)为从体内动物研究中获得的固有清除率(CLint)建立筛选级定量性质-物性关系(QPPR),以及(ii)将其与人的生理学结合到PBPK模型中预测VOC的吸入药代动力学。从文献中获得了26种VOC的CLint,以体内的V max(μmol/ h / kg bw大鼠)与K m(μM)之比计算得出。通过逐步线性回归分析得出的QPPR模型通过了验证步骤(R 2 = 0.8;留一法交叉验证Q 2 = 0.75),以将CLint标准化为VOC的磷脂(PL)亲和力。 QPPR有助于根据log P ow,log血液:水PC和电离电势的输入数据计算CLint(L PL / h / kg bw大鼠)。然后将QPPR的预测作为95%平均置信区间(LMCI和UMCI,分别)的上下限整合到人PBPK模型中。 QPPR-PBPK模型预测的最大AUC(使用LMCI用于CLint)与最小(使用UMCI用于CLint)之比为1.36±0.4,范围为1.06(1,1-二氯乙烯)至2.8(异戊二烯)。总体而言,本研究开发的集成QPPR-PBPK建模方法是一种实用的方法,可用来描述缺乏CLint知识对预测VOC的人体药代动力学的影响以及CLint预测不确定性对VOC的人体药代动力学的影响。

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