A Bayesian Meta-Analysis on Published Sample Mean and Variance Pharmacokinetic Data with Application to Drug–Drug Interaction Prediction

Abstract

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.

Key Words:

• Area under the concentration curve ratio (AUCR)
• Bayesian hierarchical model
• Drug–drug interaction (DDI)
• Meta-analysis
• Monte Carlo Markov chain (MCMC)
• Pharmacokinetics (PK)
• Prediction

ACKNOWLEDGMENT

Drs. Lang Li, Menggang Yu, and Stephen Hall's researches are supported by NIH grants, R01 GM74217 (LL), R01 GM67308 (SH), FD-T-001756(SH).

Notes

^†In the original paper, MDZ was through IV fusion in a very short period to 2 mg, we used the portion of data after fusion and assumed IV bolus administration.

Note: Model 2∗: a Bayesian three-level hierarchical model for the sample mean and variance. Model 1∗∗: a Bayesian three-level hierarchical model for the sample mean. 90% CI∗∗∗: a 90% credit interval. It is represented by its relative scale to the mean.

Note: the results are presented as mean × /÷ (1 + CV)∗100%.

Model 2∗: a Bayesian three-level hierarchical model for the sample mean and variance. Model 1∗∗: a Bayesian three-level hierarchical model for the sample mean.