The Bayesian Analysis of Population Pharmacokinetic Models
群体药代动力学模型的贝叶斯分析AI 解读 ↓
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群体药代动力学模型的贝叶斯分析
- 研究问题
- 在个体测量稀疏、噪声较大且参数维度很高时，如何估计群体药代动力学模型并识别相关协变量与群体差异？
- 方法与思路
- 作者采用一般贝叶斯层级模型与 Hastings–Metropolis 算法分析奎尼丁浓度数据，构建协变量模型并检查分布假设。
- 材料陈述的贡献
- 摘要展示非线性群体药代模型的贝叶斯分析，包括协变量建模、分布假设敏感性分析及较广泛的图形模型检查。
- 阅读关注点
- 应核对浓度与时间的非线性模型、层级分布和先验设定，关注采样收敛、时变协变量处理及图形检查的诊断范围。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；摘要未给出协变量估计结果或采样表现，不能据此判断具体用药方案或模型普遍适用性。
原文摘要
Pharmacokinetics is the study of the time course of a drug and its metabolites following its introduction into the body. Population pharmacokinetic studies are becoming increasingly important as an aid to drug development. The data from such studies typically consist of dose histories, drug concentrations with associated sampling times, and often covariate measurements such as the age and weight of each subject. These studies aim to provide an understanding of the pharmacokinetics of the drug in question and so lead to an informed choice of dosage regimen. Such an understanding includes determining those covariates that are important predictors of fundamental pharmacokinetic parameters, such as clearance, defined as the volume of plasma cleared of drug in a unit of time. Determining those subpopulations (e.g., the elderly) with altered kinetics has implications for the choice of an appropriate dosage regimens, because predictive concentration profiles arising from a particular regimen in different populations may be very different. In this article a general Bayesian hierarchical model is described. Pharmacokinetic models relating concentration to time are generally nonlinear, and the data are often sparse and/or noisy. The number of individuals on whom data have been collected is often large, and so the dimensionality of the parameter space is large. Consequently, estimation, from a Bayesian or a classical perspective, is not straightforward. In this article the Hastings—Metropolis algorithm is used for learning about the posterior distribution. An analysis of concentration data collected after the administration of the antiarrhythmic drug quinidine is presented. The data consist of 361 measurements on a total of 136 patients. Nine covariates are also available for each individual. These covariates are a mixture of discrete and continuous measurements. Some of the covariates are constant within an individual during the course of the study, whereas others change. A covariate model is constructed, and the sensitivity of the inferences to distributional assumptions is examined. The importance of assessing the appropriateness of modeling assumptions is emphasized and extensive model checking is carried out for the quinidine data using graphical diagnostics.