Drug-drug interaction prediction assessment

Jihao Zhou, Zhaohui Qin, Quinney K. Sara, Seongho Kim, Zhiping Wang, Stephen D. Hall, Lang Li

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Model-based drug-drug interaction (DDI) is an important in-silico tool to assess the in vivo consequences of in vitro DDI. Before its general application to new drug compounds, the DDI model is always established from known interaction data. For the first time, tests for difference and equivalent tests are implemented to compare reported and model-base simulated DDI (log AUCR) in the sample mean and variance. The biases and predictive confidence interval coverage probabilities are introduced to assess the DDI prediction performance. Sample size and power guidelines are developed for DDI model simulations. These issues have never been discussed in trial simulation studies to investigate DDI prediction. A ketoconazole (KETO)/midazolam (MDZ) example is employed to demonstrate these statistical methods. Based on published KETO and MDZ pharmacokinetics data and their in vitro inhibition rate constant data, current model-based DDI prediction underpredicts the area under concentration curve ratio (AUCR) and its between-subject variance compared to the reported study.

Original languageEnglish (US)
Pages (from-to)641-657
Number of pages17
JournalJournal of biopharmaceutical statistics
Volume19
Issue number4
DOIs
StatePublished - Jul 1 2009

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Keywords

  • Area under the concentration curve ratio (AUCR)
  • Drug-drug interaction (DDI)
  • Equivalence test
  • Pharmacokinetics
  • Simulation

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Pharmacology
  • Statistics and Probability

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