A Bayesian Meta-Analysis on Published Sample Mean and Variance Pharmacokinetic Data with Application to Drug-Drug Interaction Prediction
A Bayesian Meta-Analysis on Published Sample Mean and Variance Pharmacokinetic Data with Application to Drug-Drug Interaction Prediction
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DOI：
10.1080/10543400802369004
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发表时间：
2008-01-01
影响因子：
1.1
通讯作者：
Li, Lang
中科院分区：
文献类型：
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作者：
Yu, Menggang;Kim, Seongho;Li, Lang
在药物-药物相互作用(DDI)研究中，通常用药物个体药代动力学(PK)来预测两种药物的相互作用。尽管关于抑制剂或诱导剂和底物PK的临床PK研究的特定受试者的药物浓度数据通常不会公布，但样本平均血浆药物浓度及其标准偏差已被常规报道。因此，非常需要使用这些汇总的PK数据进行Meta分析和DDI预测。本研究提出了一种基于三层递阶贝叶斯Meta分析模型的DDI预测方法。三个水平模拟样本均值和方差、研究间方差和先验分布。通过一个酮康唑-咪达唑仑的例子和模拟，我们证明了我们的荟萃分析模型不仅能够以较小的偏差估计PK参数，而且能够很好地恢复它们的研究间和研究间方差。更重要的是，PK参数及其方差分量的后验分布使我们能够在总体平均水平和研究特定水平上预测DDI。我们还能够预测受试者/研究对象之间的DDI差异。这些统计预测从未在DDI研究中被调查过。我们的模拟研究表明，我们的Meta分析方法在PK参数估计和DDI预测中具有较小的偏差。进行了敏感性分析，考察了相互作用pk参数，如抑制常数Ki对DDI预测的影响。
In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.