Population pharmacokinetic (pop-PK) models constructed for model-informed precision
dosing often have limited utility due to the low number of patients recruited. To
augment such models, an approach is presented for generating fully artificial quasi-models
which can be employed to make individual estimates of pharmacokinetic parameters.
Based on 72 concentrations obtained in 12 patients, one- and two-compartment pop-PK
models with or without creatinine clearance as a covariate were generated for piperacillin
using the nonparametric adaptive grid algorithm. Thirty quasi-models were subsequently
generated for each model type, and nonparametric maximum a posteriori probability
Bayesian estimates were established for each patient. A significant difference in
performance was found between one- and two-compartment models. Acceptable agreement
was found between predicted and observed piperacillin concentrations, and between
the estimates of the random-effect pharmacokinetic variables obtained using the so-called
support points of the pop-PK models or the quasi-models as priors. The mean squared
errors of the predictions made using the quasi-models were similar to, or even considerably
lower than those obtained when employing the pop-PK models. Conclusion: fully artificial
nonparametric quasi-models can efficiently augment pop-PK models containing few support
points, to make individual pharmacokinetic estimates in the clinical setting.
