Do we need full compliance data for population pharmacokinetic analysis?
Do we need full compliance data for population pharmacokinetic analysis?
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DOI：
10.1007/bf02353671
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发表时间：
1996-06-01
期刊：
影响因子：
--
通讯作者：
Blaschke, TF
中科院分区：
文献类型：
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作者：
Girard, P;Sheiner, LB;Blaschke, TF
对于多次口服给药的群体药代动力学分析，关键问题之一是尽可能精确地了解剂量输入，以便将模型拟合到输入-输出（剂量-浓度）关系。最近开发的放置在药丸容器上的电子监测装置允许在数月内获得容器打开的时间/日期的精确记录。据报道，这些记录是对非卧床患者服药行为的最可靠的测量。为了研究使用和总结这种新的丰富信息的策略，开发了马尔可夫链过程模型，该模型模拟来自电子监测患者的真实的数据的依从性数据，并进行了数据模拟和分析。结果表明，传统的群体药代动力学分析方法，忽略了实际的剂量信息往往会估计偏倚的清除率和体积，并显着高估随机个体间变异。最佳给药信息汇总策略包括：初始估计群体药代动力学参数，不使用协变量，仅使用有限数量的剂量记录，后者是基于目标隔室中药物半衰期的先验估计值选择的;然后使用来自第一次群体拟合的群体或个体后验贝叶斯参数估计值重新汇总剂量记录;最后使用新汇总的剂量记录重新估计群体参数。这种汇总策略产生与使用完整给药信息记录相同的参数估计值，同时将群体药代动力学分析所需的CPU时间减少至少75%。
For population pharmacokinetic analysis of multiple oral doses one of the key issues is knowing as precisely as possible the dose inputs in order to fit a model to the input-output (dose-concentration) relationship. Recently developed electronic monitoring devices, placed on pill containers, permit precise records to be obtained over months, of the time/date opening of the container. Such records are reported to be the most reliable measurement of drug taking behavior for ambulatory patients. To investigate strategies for using and summarizing this new abundant information, a Markov chain process model was developed that simulates compliance data from real data from electronically monitored patients, and data simulations and analyses were conducted. Results indicate that traditional population pharmacokinetic analysis methods that ignore actual dosing information tend to estimate biased clearance and volume and markedly overestimate random interindividual variability. The best dosing information summarization strategies consist of initially estimating population pharmacokinetic parameters, using no covariates and only a limited number of dose records, the latter chosen based on an a priori estimate of the half-life of the drug in the compartment of interest; then resummarizing the dose records using either population or individual posterior Bayes parameter estimates from the first population fit; and finally reestimating the population parameters using the newly summarized dose records. Such summarization strategies yield the same parameter estimates as using full dosing information records while reducing by at least 75% the CPU time needed for a population pharmacokinetic analysis.