Checking distributional assumptions for pharmacokinetic summary statistics based on simulations with compartmental models.
J Biopharm Stat
; 27(5): 756-772, 2017.
Article
en En
| MEDLINE
| ID: mdl-27669105
ABSTRACT
Bioequivalence (BE) studies are an essential part of the evaluation of generic drugs. The most common in vivo BE study design is the two-period two-treatment crossover design. AUC (area under the concentration-time curve) and Cmax (maximum concentration) are obtained from the observed concentration-time profiles for each subject from each treatment under each sequence. In the BE evaluation of pharmacokinetic crossover studies, the normality of the univariate response variable, e.g. log(AUC)1 or log(Cmax), is often assumed in the literature without much evidence. Therefore, we investigate the distributional assumption of the normality of response variables, log(AUC) and log(Cmax), by simulating concentration-time profiles from two-stage pharmacokinetic models (commonly used in pharmacokinetic research) for a wide range of pharmacokinetic parameters and measurement error structures. Our simulations show that, under reasonable distributional assumptions on the pharmacokinetic parameters, log(AUC) has heavy tails and log(Cmax) is skewed. Sensitivity analyses are conducted to investigate how the distribution of the standardized log(AUC) (or the standardized log(Cmax)) for a large number of simulated subjects deviates from normality if distributions of errors in the pharmacokinetic model for plasma concentrations deviate from normality and if the plasma concentration can be described by different compartmental models.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Distribuciones Estadísticas
/
Medicamentos Genéricos
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Biopharm Stat
Asunto de la revista:
FARMACOLOGIA
Año:
2017
Tipo del documento:
Article
País de afiliación:
Estados Unidos