Jodrell D I, Murray L S, Hawtof J, Graham M A, Egorin M J
Department of Clinical Oncology, University of Edinburgh, Western General Hospital, UK.
Cancer Chemother Pharmacol. 1996;37(4):356-62. doi: 10.1007/s002800050397.
The pharmacokinetics of a drug in individual patients can be estimated using plasma samples collected at a limited number of time points. However, different methods for a limited-sampling strategy (LSS) design exist and the optimal method has not yet been defined. Plasma concentration data were available from 27 of 74 courses in a phase I study (dose range, 5-55 mg m-2) of the novel anthrapyrazole DuP-941. Three approaches to LSS development were compared. Firstly, forward stepwise regression (FSR) was used to derive equations to predict the DuP-941 area under the concentration-time curve (AUC) based on plasma concentrations measured at specified times. LSSs were developed using 14 randomly chosen data sets and were validated using the remaining 13 data sets. Secondly, "all subsets" regression (ASR) was used to develop LSSs. A jack-knife technique was also used to allow model development utilising 26 data sets and validation on the 27th data set. Thirdly, an LSS was developed using optimal sampling theory (OST), and the LSS was used in conjunction with a Bavesian algorithm. Selected sampling times for four-point LSSs were 10, 65, 185 and 485 min (FSR) and 10, 45, 200 and 480 min (OST). Ten candidate LSSs were developed using the ASR approach. ASR- and OST/Bayesian-derived four-point LSSs gave more precise (P < 0.05) estimates of AUC [mean absolute percentage of difference (MAD%) +/- SD: ASR, 6.4 +/- 3.7%; OST/Bayesian, 6.8 +/- 4.6%] than did FSR (MAD% = 15.1 +/- 9.9%). The OST/Bayesian approach is recommended because it allows estimation of all model parameters and is more flexible with regard to sample collection time and design variables.
可使用在有限数量时间点采集的血浆样本估计个体患者体内药物的药代动力学。然而，存在不同的有限采样策略（LSS）设计方法，且尚未确定最佳方法。在新型蒽吡唑DuP - 941的I期研究（剂量范围为5 - 55 mg m - 2）中，74个疗程中有27个疗程可获得血浆浓度数据。比较了三种开发LSS的方法。首先，使用向前逐步回归（FSR）来推导方程，以根据在特定时间测量的血浆浓度预测DuP - 941浓度 - 时间曲线下面积（AUC）。使用14个随机选择的数据集开发LSS，并使用其余13个数据集进行验证。其次，使用“所有子集”回归（ASR）来开发LSS。还使用留一法技术，以便利用26个数据集进行模型开发，并在第27个数据集上进行验证。第三，使用最优采样理论（OST）开发LSS，并将该LSS与贝叶斯算法结合使用。四点LSS的选定采样时间为10、65、185和485分钟（FSR）以及10、45、200和480分钟（OST）。使用ASR方法开发了10个候选LSS。与FSR（平均绝对差异百分比（MAD%）= 15.1 +/- 9.9%）相比，ASR和OST/贝叶斯推导的四点LSS对AUC的估计更精确（P < 0.05）[平均绝对差异百分比（MAD%）+/-标准差：ASR，6.4 +/- 3.7%；OST/贝叶斯，6.8 +/- 4.6%]。推荐使用OST/贝叶斯方法，因为它允许估计所有模型参数，并且在样本采集时间和设计变量方面更灵活。
