Mager H, Göller G
BAYER AG, Pharma Research Center, Wuppertal, Germany.
J Pharm Sci. 1998 Mar;87(3):372-8. doi: 10.1021/js970114h.
Toxicokinetic studies often require destructive sampling and the determination of drug concentrations in the various organs. Classically, the corresponding information is summarized in one mean concentration-time profile, which is regarded as representative for the animal population. On the basis of a mean profile, only estimates of the secondary pharmacokinetic parameters (for example AUC, t1/2) but no variability measures may be obtained. In this paper two resampling techniques are contrasted to Bailer's approach. The results obtained show that the resampling techniques can be considered a reliable alternative to Bailer's approach for the estimation of the standard error of the AUC t(k)0 in the case of normally distributed concentration data. They can be extended to the estimation of a variety of other secondary pharmacokinetic parameters and their respective standard deviations. One disadvantage with Bailer's method is its restriction to linear functions of the concentrations. On the other hand, using the population approach, prior knowledge of the underlying pharmacokinetic model is necessary. The resampling techniques discussed here, the "pseudoprofile-based bootstrap" (PpbB) and the "pooled data bootstrap" (PDB), are noncompartmental approaches. They are applicable under nonnormal data constellations and permit the estimation of the usual secondary pharmacokinetic parameters along with their standard deviations, standard errors, and other statistical measures. To assess the accuracy, precision, and robustness of the resampling estimators, theoretical data from three different pharmacokinetic models with different add-on errors (up to 100% variability) were analyzed. Even for the data sets with high variability, the parameters calculated with resampling techniques differ not more than 10% from the true values. Thus, in the case of data that are not normally distributed or when additional secondary pharmacokinetic parameters and their variability are to be estimated, the resampling methods are powerful tools in the safety assessment in preclinical pharmacokinetics and in toxicokinetics where generally sparse data situations are given.
毒代动力学研究通常需要进行破坏性取样并测定各器官中的药物浓度。传统上，相应信息汇总在一个平均浓度 - 时间曲线上，该曲线被视为动物群体的代表。基于平均曲线，只能获得二级药代动力学参数（例如AUC、t1/2）的估计值，而无法获得变异性度量。本文将两种重采样技术与贝勒的方法进行了对比。所得结果表明，在浓度数据呈正态分布的情况下，重采样技术可被视为贝勒方法的可靠替代方法，用于估计AUC t(k)0的标准误差。它们可扩展用于估计各种其他二级药代动力学参数及其各自的标准差。贝勒方法的一个缺点是它仅限于浓度的线性函数。另一方面，使用群体方法时，需要对基础药代动力学模型有先验知识。这里讨论的重采样技术，即“基于伪曲线的自举法”（PpbB）和“合并数据自举法”（PDB），是非房室方法。它们适用于非正态数据情况，并允许估计常见的二级药代动力学参数及其标准差、标准误差和其他统计量。为了评估重采样估计量的准确性、精密度和稳健性，分析了来自三个不同药代动力学模型且具有不同附加误差（高达100%变异性）的理论数据。即使对于变异性高的数据集，用重采样技术计算的参数与真实值的差异也不超过10%。因此，在数据非正态分布的情况下，或者当要估计额外的二级药代动力学参数及其变异性时，重采样方法是临床前药代动力学和毒代动力学安全性评估中的有力工具，在这些领域通常存在数据稀疏的情况。
