Characterization of AUCs from Sparsely Sampled Populations in Toxicology Studies
Characterization of AUCs from Sparsely Sampled Populations in Toxicology Studies
复制标题
毒理学研究中稀疏样本群体 AUC 的表征
DOI：
10.1023/a:1016097227603
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发表时间：
1996
影响因子：
3.7
通讯作者：
V. Batra
中科院分区：
文献类型：
--
作者：
Sudhakar M Pai;S. Fettner;G. Hajian;M. Cayen;V. Batra
目的.这项工作的目的是开发和验证血液采样方案，以便从少量样品（稀疏采样）中准确测定AUC。这将使AUC测定直接在毒理学研究中，而不需要利用大量的动物。使用抗癫痫药非尔氨酯（F）和抗组胺药氯雷他定（L）毒代动力学（TK）研究中大鼠的血浆浓度-时间（Cp-t）数据，开发了稀疏采样方案;可获得F、L及其活性循环代谢物去碳乙胺氯雷他定（DCL）在13-16个时间点（N = 4或5只大鼠/时间点）的Cp-t数据。使用完整曲线和研究者指定的5个时间点（称为“关键”时间点）确定AUC。使用bootstrap（重新采样）技术，通过在每个“关键”点从4或5只大鼠中采样（N = 2只大鼠/点，替换）计算1000个AUC。随后使用PCNONLIN对数据进行建模，并且参数（ka、ke和Vd）进行不同程度的扰动，以模拟毒理学研究期间由于酶诱导/抑制等可能发生的药代动力学（PK）变化。最后，对Cp-t和/或PK参数应用随机噪声（10 - 40%）进行Monte Carlo模拟，以检查其对稀疏采样AUC的影响。5个时间点（2只大鼠/点）准确且精密地估计了F、L和DCL的AUC;与完整曲线的偏差约为10%，精密度（%CV）约为15%。此外，改变的动力学和随机噪声对稀疏采样的AUC的影响最小。稀疏采样可以准确估计AUC，并且可以在啮齿动物毒理学研究中实施，以显著减少TK评价的动物数量。同样的原则也适用于安全性评估中使用的其他种属的稀疏抽样设计。
Purpose. The objective of this work was to develop and validate blood sampling schemes for accurate AUC determination from a few samples (sparse sampling). This will enable AUC determination directly in toxicology studies, without the need to utilize a large number of animals.Methods. Sparse sampling schemes were developed using plasma concentration-time (Cp-t) data in rats from toxicokinetic (TK) studies with the antiepileptic felbamate (F) and the antihistamine loratadine (L); Cp-t data at 13–16 time-points (N = 4 or 5 rats/time-point) were available for F, L and its active circulating metabolite descarboethoxyloratadine (DCL). AUCs were determined using the full profile and from 5 investigator designated time-points termed “critical” time-points. Using the bootstrap (re-sampling) technique, 1000 AUCs were computed by sampling (N = 2 rats/point, with replacement) from the 4 or 5 rats at each “critical” point. The data were subsequently modeled using PCNONLIN, and the parameters (ka, ke, and Vd) were perturbed by different degrees to simulate pharmacokinetic (PK) changes that may occur during a toxicology study due to enzyme induction/inhibition, etc. Finally, Monte Carlo simulations were performed with random noise (10 to 40%) applied to Cp-t and/or PK parameters to examine its impact on AUCs from sparse sampling.Results. The 5 time-points with 2 rats/point accurately and precisely estimated the AUC for F, L and DCL; the deviation from the full profile was ~10%, with a precision (%CV) of ~15%. Further, altered kinetics and random noise had minimal impact on AUCs from sparse sampling.Conclusions. Sparse sampling can accurately estimate AUCs and can be implemented in rodent toxicology studies to significantly reduce the number of animals for TK evaluations. The same principle is applicable to sparse sampling designs in other species used in safety assessments.