摘要
在临床前药物动力学研究中，通常无法获得每只动物个体的完整药物动力学观测数据。在某些情况下，每只动物个体只能提供一个观测数据。人们并不知道如何有效利用此类数据进行药物动力学参数的估算。本研究旨在比较一种新方法和传统方法在估算“单动物个体一单观测值”型数据的药物动力学参数时的优劣。本研究假设共有15只动物分别单次静脉注射相同剂量的药物，每只动物提供一个观测数据。共有5个观测时间点，每个观测时间点包括三个观测数据。数据仿真采用符合一级消除的一室模型。清除率（CL）和表观分布容积（V）的个体间变异均包含10％、30％＊1150％~个水平，统计模型选定为比例型残差模型，也包括三个水平：10％、30％和50％。本研究对比了药物动力学参数估算的两种方法（传统方法和有限重复抽样法），传统方法（M1）直接对原始数据（即“单动物个体一单观测值”型数据）进行参数估算，而有限重复抽样法（M2）则是按照观测时间点对原始数据进行排列组合，将其扩展为一套由含有完整药物动力学数据的虚拟动物个体组成的新的数据集，本研究中共243（C3^1×C3^1×C3^1×C3^1×C3^1）只虚拟动物个体。本研究共重复了100次仿真，仿真与参数估算均采用NONMEM软件完成。结果显示，M2方法所估算的CL和v与其相应的仿真值更接近，但在不同ⅡⅤ及残差的组合下稍有差异。总体而言，M2方法的优势随着残差的增大而减小，其也同样收到ⅡⅤ大小的影响，ⅡⅤ增大时M2优势亦会下降。同M1方法类似，M2方法对参数的ⅡⅤ也没有还原能力。与传统方法相比，有限重复抽样法在估算“单动物个体一单观测值”型数据药物动力学参数时可以提供更加可靠的结果。与个体间变异相比，估算结果主要收到残差大小的影响。
During pre-clinical pharmacokinetic research, it is not easy to gather complete pharmacokinetic data in each animal. In some cases, an animal can only provide a single observation. Under this circumstance, it is not clear how to utilize this data to estimate the pharmacokinetic parameters effectively. This study was aimed at comparing a new method to handle such single-observation-per-animal type data with the conventional method in estimating pharmacokinetic parameters. We assumed there were 15 animals within the study receiving a single dose by intravenous injection. Each animal provided one observation point. There were five time points in total, and each time point contained three measurements. The data were simulated with a one-compartment model with first-order elimination. The inter-individual variabilities （ⅡV） were set to 10%, 30% and 50% for both clearance （CL） and apparent volume of distribution （V）. A proportional model was used to describe the residual error, which was also set to 10%, 30% and 50%. Two methods （conventional method and the finite msampling method） to handle with the simulated single-observation-per-animal type data in estimating pharmacokinetic parameters were compared. The conventional method （MI） estimated pharmacokinetic parameters directly with original data, i.e., single-observation-per-animal type data. The finite resampling method （M2） was to expand original data to a new dataset by resampling original data with all kinds of combinations by time. After resampling, each individual in the new dataset contained complete pharmacokinetic data, i.e., in this study, there were 243 （C3^1×C3^1×C3^1×C3^1×C3^1） kinds of possible combinations and each of them was a virtual animal. The study was simulated 100 times by the NONMEM software. According to the results, parameter estimates of CL and V by M2 based on the simulated dataset were closer to their true values, though there was a small difference among different combinations of ⅡVs and the residual errors. In general, M2 was less advantageous over M1 when the residual error increased. It was also influenced by the levels of ⅡV as higher levels of IIV could lead to a decrease in the advantage of M2. However, M2 had no ability to estimate the ⅡV of parameters, nor did M1. The finite resampling method could provide more reliable results compared to the conventional method in estimating pharmacokinetic parameters with single-observation-per-animal type data. Compared to the inter-individual variability, the results of estimation were mainly influenced by the residual error.
