Design of informative renal impairment studies: evaluation of the impact of design stratification on bias, precision and dose adjustment error.

Abstract

METHODS

Study designs were simulated and re-estimated using a hypothetical 2-compartmental PK model with varying magnitude of the fraction of renal elimination (FR) and magnitude of between-subject variability (BSV). The DAE was computed based on the difference between the theoretical necessary dose adjustment versus the empirical estimated dose adjustment to reach a similar exposure as controls.

CONCLUSIONS

We quantified the impact of study design imbalance on bias and precision of PK parameters and DAE, as may occur for RI studies in some indications. Adding additional data from earlier studies to the analysis dataset improves the bias and precision of PK parameters.

RESULTS

Although some design imbalance may still lead to DAEs of acceptable magnitude (DAE < -11.05-14.44 inter-quartile range, IQR), at least some patients are necessary in the more severe RI groups. When 100 additional patients with normal renal function were included in a sub-informative design, the DAE changed from < -7.63-16.64 IQR to < -8.89-8.69 IQR.

PURPOSE

Renal impairment (RI) studies are conducted to estimate the impact of RI on pharmacokinetics (PK). In some disease areas, these studies can be difficult to conduct, for instance due to the limited number of eligible patients. The objective of this analysis was to evaluate bias and precision of population PK parameters, and the dose adjustment error (DAE) for RI studies i) with different levels of study design imbalance in the stratification of subjects across RI categories, and ii) that include additional patients in the control arm of RI studies, that may be available from previously conducted PK studies.