The Effect of Study Design on Pharmacokinetics in Patients with Impaired Renal Function

Backgroud: To ensure the effect of renal function on drug exposure is precisely quantified, FDA guidance1recommends that studies recruit approximately equal subject numbers with normal renal function and mild, moderate and severe renal impairment. However, in population PK analyses it is common to pool data from various studies, resulting in an over-representation of subjects with normal renal function.

Aim: To explore how varying the design, with respect to subject numbers in the different renal function groups, impacts upon the precision of PK parameter estimation.

Methods: A 1-compartment, first order input, first order elimination model was used to simulate concentrations following a single 1mg/kg dose of a drug. Clearance (CL) was defined as a composite of renal CL (0.7 L/hr) and non-renal CL (0.3 L/hr). During the simulation, subjects were stratified into the 4 renal function groups where normal renal function, mild, moderate and severe renal impairment were classified as creatinine clearances of ≥80, 50–79.9, 30–49.9 and <30 mL/min respectively. 1000 trials with 100 subjects per trial were simulated under 4 different simulation scenarios. The precision in estimating renal and non-renal clearance of various designs with varying number of subjects were compared. Both intensive and sparse sampling were evaluated for each design, as well as differing magnitudes of random effects. In the sparse sampling designs, 3 sampling points were determined using D-Optimality with WinPOPT2 under the simulated annealing algorithm, while 12 sampling points were used in intensive sampling design.PK parameters were re-estimated and compared across the designs. The percentage of times that the PK estimate was within 10% of the true value was computed for each design.

Results: Probabilities of the PK estimate falling within 10% of the true value were decreased when (1) the number of renal function group was decreased; (2) the number of renal impaired subject was decreased; (3) the number of total subject was decreased. The probability in sparse sampling design was comparable to that in intensive sampling design.

Conclusion: Renal clearance appeared to be well estimated with as few as 5 subjects in each impaired renal function group. Excluding subjects with severe renal impairment in pooled population PK analysis does not give a reliable estimate of the renal effect on CL.


  1. FDA.
  2. Duffull, S.B. et al. WinPOPT v1.1,