Yu Menggang, Kim Seongho, Wang Zhiping, Hall Stephen, Li Lang
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, Indianapolis, Indiana 46023, USA.
J Biopharm Stat. 2008;18(6):1063-83. doi: 10.1080/10543400802369004.
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.
在药物相互作用（DDI）研究中，通常通过个体药物的药代动力学（PK）来预测两药相互作用。虽然关于抑制剂或诱导剂以及底物PK的临床PK研究中受试者特异性药物浓度数据通常不会发表，但样本平均血浆药物浓度及其标准差已被常规报告。因此，非常需要使用此类汇总的PK数据进行荟萃分析和DDI预测。在本研究中，开发了一种基于三级分层贝叶斯荟萃分析模型的创新DDI预测方法。该三级模型对样本均值和方差、研究间方差以及先验分布进行建模。通过酮康唑-咪达唑仑的实例和模拟，我们证明我们的荟萃分析模型不仅能够以较小的偏差估计PK参数，还能很好地恢复研究间和个体间的方差。更重要的是，PK参数及其方差成分的后验分布使我们能够在群体平均水平和特定研究水平上预测DDI。我们还能够预测个体间/研究间方差的DDI。这些统计预测在DDI研究中从未被探讨过。我们的模拟研究表明，我们的荟萃分析方法在PK参数估计和DDI预测方面偏差较小。进行了敏感性分析，以研究相互作用PK参数（如抑制常数Ki）对DDI预测的影响。
