Designing population pharmacokinetic studies: Performance of mixed designs

The interplay of the following factors: population design (PDN), the cost f unction in terms of maximum cost (Max. C) (i.e., maximum number of samples / sample size), sample size, and intersubject variability [restricted (30%) to moderate (60%)] on the estimation of pharmacokinetic parameters from po pulation pharmacokinetic data sets obtained using mixed designs was investi gated in a simulation study. A two compartment model with multiple bolus in travenous inputs was assumed, and the residual variability was set at 15%. The sample size (N) investigated ranged from 30 to 200 with the associated cost function varying accordingly with the five individual and sixteen popu lation designs studied. Accurate and precise estimates of structural model parameters were obtained for N greater than or equal to 50 (Max.C greater t han or equal to 150) irrespective of the intersubject variability (ITV) and PDN investigated. When ITV was 30%, all structural model parameters were w ell estimated irrespective of the PDN. Robust estimates of clearance and it s variability were obtained for all N at all levels of ITV with Max. C grea ter than or equal to 90 (PDN greater than or equal to 4). Imprecise estimat es of ITV in V1, V2, and Q were obtained at 60% ITV irrespective of N, PDN, or Max. C. Positive bias was associated with the estimation of variability in V1, V2, and Q with PDN less than or equal to 4 (Max. C less than or equ al to 150). This was due in part to a greater proportion of subjects sample d only once. Correspondingly, residual variability was underestimated. It i s of utmost importance to avoid this artifact by ensuring that at least a m oderate subset of subjects contributing data to a population pharmacokineti c study contribute data more than once. Given a sample size and ITV, the co st function must be considered in designing a population pharmacokinetic st udy using mixed designs.