A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction

Menggang Yu, Seongho Kim, Zhiping Wang, Stephen Hall, Lang Li

Research output: Contribution to journalArticle

7 Citations (Scopus)

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 languageEnglish
Pages (from-to)1063-1083
Number of pages21
JournalJournal of Biopharmaceutical Statistics
Volume18
Issue number6
DOIs
StatePublished - Nov 2008

Fingerprint

Sample variance
Bayes Theorem
Pharmacokinetics
Sample mean
Drug Interactions
Meta-Analysis
Drugs
Prediction
Interaction
Pharmaceutical Preparations
Model Analysis
Midazolam
Research
Predict

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 journalArticle

Yu, Menggang ; Kim, Seongho ; Wang, Zhiping ; Hall, Stephen ; Li, Lang. / A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction. In: Journal of Biopharmaceutical Statistics. 2008 ; Vol. 18, No. 6. pp. 1063-1083.
@article{35d1c68cb5cf4240996a6e4a41e0cf27,
title = "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.",
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",
author = "Menggang Yu and Seongho Kim and Zhiping Wang and Stephen Hall and Lang Li",
year = "2008",
month = "11",
doi = "10.1080/10543400802369004",
language = "English",
volume = "18",
pages = "1063--1083",
journal = "Journal of Biopharmaceutical Statistics",
issn = "1054-3406",
publisher = "Taylor and Francis Ltd.",
number = "6",

}

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 -