Collins Jon W, Heyward Hull J, Dumond Julie B
Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, 1093 Genetic Medicine Building, 120 Mason Farm Rd, CB 7361, Chapel Hill, NC, 27599-7361, USA.
J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):631-640. doi: 10.1007/s10928-017-9554-9. Epub 2017 Nov 8.
Sparse tissue sampling with intensive plasma sampling creates a unique data analysis problem in determining drug exposure in clinically relevant tissues. Tissue exposure may govern drug efficacy, as many drugs exert their actions in tissues. We compared tissue area-under-the-curve (AUC) generated from bootstrapped noncompartmental analysis (NCA) methods and compartmental nonlinear mixed effect (NLME) modeling. A model of observed data after single-dose tenofovir disoproxil fumarate was used to simulate plasma and tissue concentrations for two destructive tissue sampling schemes. Two groups of 100 data sets with densely-sampled plasma and one tissue sample per individual were created. The bootstrapped NCA (SAS 9.3) used a trapezoidal method to calculate geometric mean tissue AUC per dataset. For NLME, individual post hoc estimates of tissue AUC were determined, and the geometric mean from each dataset calculated. Median normalized prediction error (NPE) and absolute normalized prediction error (ANPE) were calculated for each method from the true values of the modeled concentrations. Both methods produced similar tissue AUC estimates close to true values. Although the NLME-generated AUC estimates had larger NPEs, it had smaller ANPEs. Overall, NLME NPEs showed AUC under-prediction but improved precision and fewer outliers. The bootstrapped NCA method produced more accurate estimates but with some NPEs > 100%. In general, NLME is preferred, as it accommodates less intensive tissue sampling with reasonable results, and provides simulation capabilities for optimizing tissue distribution. However, if the main goal is an accurate AUC for the studied scenario, and relatively intense tissue sampling is feasible, the NCA bootstrap method is a reasonable, and potentially less time-intensive solution.
密集血浆采样结合稀疏组织采样在确定临床相关组织中的药物暴露时会产生独特的数据分析问题。组织暴露可能决定药物疗效，因为许多药物在组织中发挥作用。我们比较了自抽样非房室分析（NCA）方法和房室非线性混合效应（NLME）建模生成的组织曲线下面积（AUC）。使用单剂量替诺福韦酯富马酸盐后的观察数据模型，对两种破坏性组织采样方案的血浆和组织浓度进行模拟。创建了两组数据集，每组100个，血浆采样密集，每个个体有一个组织样本。自抽样NCA（SAS 9.3）使用梯形法计算每个数据集的几何平均组织AUC。对于NLME，确定组织AUC的个体事后估计值，并计算每个数据集的几何平均值。根据模拟浓度的真实值，计算每种方法的中位数标准化预测误差（NPE）和绝对标准化预测误差（ANPE）。两种方法产生的组织AUC估计值与真实值接近。虽然NLME生成的AUC估计值有较大的NPE，但ANPE较小。总体而言，NLME的NPE显示AUC预测不足，但精度提高且异常值较少。自抽样NCA方法产生的估计更准确，但有些NPE>100%。一般来说，NLME更受青睐，因为它能适应强度较低的组织采样并产生合理结果，还具备优化组织分布的模拟能力。然而，如果主要目标是针对所研究场景获得准确的AUC，且相对密集的组织采样可行，那么NCA自抽样方法是一种合理且可能耗时较少的解决方案。
