Impact of random and fixed (optimal) sampling approach on the Bayesian estimation of clearance

The population pharmacokinetic approach is based on sparse sampling. In sparse sampling approaches, the selection of the time point(s) is very critical for the prediction of pharmacokinetic parameters. Several investigators have shown that the predictive performance of the Bayesian approach is influenced by the initial estimates of pharmacokinetic parameters as well as the time of blood sampling. The objective of this study is to evaluate the impact of random and fixed sparse sampling approach on the Bayesian estimation of clearance.

Three drugs were selected for this study. Two sparse sampling methods (random or fixed) using Bayesian approach were used to assess clearance in healthy subjects following a single oral dose. The initial estimates of the model parameters and inter- and intra-subject variabilities were obtained from the previous pharmacokinetic studies conducted in healthy volunteers. The predicted clearance values using sparse sampling (1, 2 or 3 blood samples per subject) were compared with the clearance values obtained by extensive sampling.

The results indicated that both random and fixed sampling approaches, irrespective of number of blood samples, can be used to estimate mean population as well as post hoc predicted individual clearance (Bayesian) with accuracy. However, the precision of the prediction of clearance was found to be better with fixed rather random blood sampling approach.