EVALUATION OF BIOEQUIVALENCE OF HIGHLY VARIABLE DRUGS USING CLINICAL-TRIAL SIMULATIONS - II - COMPARISON OF SINGLE AND MULTIPLE-DOSE TRIALSUSING AUC AND CMAX

Purpose. Evaluating of the effects of high intrasubject variability in clearance (CL) and volume of distribution (V), on 90% confidence inte rvals (CIs) for AUC (Area Under the concentration Curve) in single and multiple-dose bioequivalence studies. The main methodology was Monte Carlo simulation, and we also used deterministic simulation, and exami nation of clinical trials. The results are compared with those previou sly observed for Cmax (maximum concentration.) Methods. The time cours e of drug concentration in plasma was simulated using a one-compartmen t model with log-normal statistical distributions of intersubject and intrasubject variabilities in the pharmacokinetic parameters. Both imm ediate-release and prolonged-release products were simulated using sev eral levels of intrasubject variability in single-dose and multiple-do se studies. Simulations of 2000 clinical bioequivalence trials per con dition (138 conditions) with 30 subjects in each crossover trial were carried out. Simulated data were compared with data from actual bioequ ivalence trials. R Results. The current simulations for AUC show simil ar probabilities of failure for single-dose and multiple-dose bioequiv alence studies, even with differences in the rate of absorption or fra ction absorbed. AUC values from prolonged-release scenario studies are more sensitive to changes in the first order absorption rate constant ka, and to variability in CL and V than AUC from studies of immediate -release studies. Conclusions. We showed that multiple-dose designs fo r highly variable drugs do not always reduce intrasubject variability in either AUC or Cmax, although the behavior of AUC differs from Cmax. Single dose AUC to the last quantifiable concentration was more relia ble than either single dose AUC extrapolated to infinity, or multiple dose AUC during a steady-state interval. Multiple-dose designs may not be the best solution for assessing bioequivalence of highly variable drugs.