Girard P, Sheiner L B, Kastrissios H, Blaschke T F
Department of Pharmacy, School of Pharmacy, University of California, San Francisco, USA.
J Pharmacokinet Biopharm. 1996 Jun;24(3):265-82. doi: 10.1007/BF02353671.
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.
对于多次口服剂量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情况，以便使模型能够拟合输入-输出（剂量-浓度）关系。最近研发的置于药瓶上的电子监测装置，能够获取数月内药瓶开启时间/日期的精确记录。据报道，此类记录是门诊患者用药行为最可靠的测量方式。为了研究使用和汇总这些丰富新信息的策略，开发了一种马尔可夫链过程模型，该模型可模拟来自电子监测患者真实数据的依从性数据，并进行了数据模拟与分析。结果表明，忽略实际给药信息的传统群体药代动力学分析方法往往会估计出有偏差的清除率和容积，并显著高估个体间的随机变异性。最佳的给药信息汇总策略包括：首先在不使用协变量且仅使用有限数量剂量记录的情况下估计群体药代动力学参数，后者基于对感兴趣隔室内药物半衰期的先验估计来选择；然后使用首次群体拟合得到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。这种汇总策略产生的参数估计值与使用完整给药信息记录时相同，同时将群体药代动力学分析所需的CPU时间减少至少75%。
