Tod M, Rocchisani J M
Departement de Pharmacotoxicologie, Hôpital Avicenne, Bobigny, France.
J Pharmacokinet Biopharm. 1997 Aug;25(4):515-37. doi: 10.1023/a:1025701327672.
Optimization of the sampling schedule can be used in pharmacokinetic (PK) experiments to increase the accuracy and the precision of parameter estimation or to reduce the number of samples required. Several optimization criteria that formally incorporate prior parameter uncertainty have been proposed earlier. These criteria consist in finding the sampling schedule that maximizes the expectation (over a given parameter distribution) of det F (ED-optimality) or Log(det F) (API-optimality), or minimizes the expectation of 1/det F (EID-optimality), where F is the Fisher information matrix. The precision and the accuracy of parameter estimation after having fitted a PK model to a small number of optimal data points (determined according to D, ED, EID, and API criteria) or to a naive sampling schedule were compared in a Monte Carlo simulation study. A one-compartment model with first-order absorption rate (3 parameters) and a two-compartment model with zero-order infusion rate (4 parameters) were considered. Data were simulated for 300 subjects with both structural models, combined with several residual error models (homoscedastic, heteroscedastic with constant or variable coefficient of variation). Interindividual variabilities in PK parameters ranged from 25-66%. ED-, EID-, and API-optimal sampling times were calculated using the software OSP-Fit. Three or five samples were allowed for parameter estimation by extended least-squares. Performances of each design criterion were evaluated in terms of mean prediction error, root mean squared error, and number of acceptable estimates (i.e., with a SE less than 30%). Compared to the D-optimal design, the EID and API designs reduced the bias and the imprecision of the estimation of the parameters having a large interindividual variability. Moreover, the API design resulted in some cases in a higher number of acceptable estimates.
在药代动力学（PK）实验中，可通过优化采样计划来提高参数估计的准确性和精密度，或减少所需的样本数量。此前已提出了几种正式纳入先验参数不确定性的优化标准。这些标准在于找到能使det F的期望（在给定参数分布上）最大化（ED最优性）或Log(det F)最大化（API最优性），或使1/det F的期望最小化（EID最优性）的采样计划，其中F是费舍尔信息矩阵。在一项蒙特卡罗模拟研究中，比较了将PK模型拟合到少量最优数据点（根据D、ED、EID和API标准确定）或简单采样计划后参数估计的精密度和准确性。考虑了具有一级吸收速率的单室模型（3个参数）和具有零级输注速率的双室模型（4个参数）。使用这两种结构模型对300名受试者的数据进行了模拟，并结合了几种残差误差模型（同方差、具有恒定或可变变异系数的异方差）。PK参数的个体间变异性范围为25%-66%。使用软件OSP-Fit计算ED、EID和API最优采样时间。通过扩展最小二乘法允许用三个或五个样本进行参数估计。根据平均预测误差、均方根误差和可接受估计的数量（即标准误小于30%）评估每个设计标准的性能。与D最优设计相比，EID和API设计减少了对具有较大个体间变异性参数估计的偏差和不精确性。此外，在某些情况下，API设计产生了更多可接受的估计。
