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.
| Original language | English |
|---|---|
| Pages (from-to) | 1063-1083 |
| Number of pages | 21 |
| Journal | Journal of Biopharmaceutical Statistics |
| Volume | 18 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 2008 |
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Keywords
- Area under the concentration curve ratio (AUCR)
- Bayesian hierarchical model
- Drug-drug interaction (DDI)
- Meta-analysis
- Monte Carlo Markov chain (MCMC)
- Pharmacokinetics (PK)
- Prediction
ASJC Scopus subject areas
- Pharmacology (medical)
- Pharmacology
- Statistics and Probability
Cite this
A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction. / Yu, Menggang; Kim, Seongho; Wang, Zhiping; Hall, Stephen; Li, Lang.
In: Journal of Biopharmaceutical Statistics, Vol. 18, No. 6, 11.2008, p. 1063-1083.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction
AU - Yu, Menggang
AU - Kim, Seongho
AU - Wang, Zhiping
AU - Hall, Stephen
AU - Li, Lang
PY - 2008/11
Y1 - 2008/11
N2 - 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.
AB - 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.
KW - Area under the concentration curve ratio (AUCR)
KW - Bayesian hierarchical model
KW - Drug-drug interaction (DDI)
KW - Meta-analysis
KW - Monte Carlo Markov chain (MCMC)
KW - Pharmacokinetics (PK)
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=57349155114&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57349155114&partnerID=8YFLogxK
U2 - 10.1080/10543400802369004
DO - 10.1080/10543400802369004
M3 - Article
C2 - 18991108
AN - SCOPUS:57349155114
VL - 18
SP - 1063
EP - 1083
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
SN - 1054-3406
IS - 6
ER -