Drug-drug interaction prediction assessment.
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.
Zhou
Jihao
J
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Qin
Zhaohui
Z
Sara
Quinney K
QK
Kim
Seongho
S
Wang
Zhiping
Z
Hall
Stephen D
SD
Li
Lang
L
D000704:Analysis of Variance; D019540:Area Under Curve; D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004347:Drug Interactions; D006801:Humans; D007654:Ketoconazole / Q000493:pharmacokinetics; D008874:Midazolam / Q000493:pharmacokinetics; D015233:Models, Statistical; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size
D008874:Midazolam; D007654:Ketoconazole