A Simulation Study of the Comparative Performance of Partial Area under the Curve (pAUC) and Partial Area under the Effect Curve (pAUEC) Metrics in Crossover Versus Replicated Crossover Bioequivalence Studies for Concerta and Ritalin LA

Concerta and Ritalin LA are methylphenidate (MPH) drugs with different release mechanisms. Generic bioequivalence (BE) to these conventionally uses pAUC (partial area under the curve) as metrics in addition to C-max (maximum concentration), AUC0-t (area from time 0 to time t), and AUC0-infinity. The recommended BE design was a standard two-formulation, two-sequence, and two-period crossover; however, the currently recommended design is a replicated crossover to better define subject-by-formulation interaction variance. The current purpose was to compare via simulation, using literature MPH models, the performance of the pAUC metrics in establishing BE via the standard crossover design versus a replicated design, and the relationship of the pAUC metrics to PD (pharmacodynamics) metrics, e.g., SKAMP (Swanson, Kotkin, Agler, M-Flynn, and Pelham rating scale) composite scores pAUEC (partial area under the effect curve). One-thousand, 40-subject studies were simulated with model literature parameters. An indirect response model described the SKAMP composite scores corrected for placebo. Performance of the pAUC metrics was demonstrated by the calculation of 90% confidence intervals (CIs) for each k0fast (fast absorption rate constant) and kaslow (slow absorption rate constant) test/reference (T/R) ratio. The 90% CIs resulting from changes in the kOfast and kaslow ratios, e.g., T/R, showed greater sensitivity to changes in the ratios at quotients below 0.8 than above for both Concerta and Ritalin LA. Ritalin LA pAUC values were insensitive to increases in either ratio once the ratio exceeded 1.0 and the study design. Correlations between least squares means (LSM) for pAUC and the SKAMP pAUEC for the composite scores were near 90%.

Automated summary

This study compared the performance of pAUC metrics in establishing bioequivalence using the standard crossover design versus a replicated design, and examined the relationship between pAUC metrics and pharmacodynamics metrics. The results showed that pAUC metrics were sensitive to changes in drug ratios and were highly correlated with pharmacodynamics metrics.