Predictive ability of published population pharmacokinetic models of valproic acid in Thai manic patients

Population pharmacokinetic (PopPK) models of valproic acid (VPA) have been developed to aid individualized drug dosing, but most of these have been based on the treatment of epileptic patients and recent evidence shows that VPA clearance (CL_VPA ) in manic patients differs from that in epileptic patients. In the light of this, the predictive ability of selected VPA PopPK models based on epileptic patients was assessed to determine whether they could be used with patients with mania.

VPA PopPK models that were based on the treatment of epileptic patients and developed using a non-linear mixed-effect approach with a one-compartment structure were selected and used to predict the VPA concentrations of a validation data set. The mean absolute prediction error (MAPE) and root mean square error (RMSE) were used to assess the accuracy and precision of the model.

The validation data set consisted of 235 Thai manic patients with a mean age of 39.6 years and a mean weight of 62.8 kg. Five models were selected to predict VPA concentrations in patients suffering from mania, and these were labelled A, C, E, F and G. The results showed that all models sufficiently predicted VPA concentrations in patients with mania, and of the models studied, G provided the most accurate and precise predictions, with MAPE and RMSE of 23% and 29.75, respectively.

VPA PopPK models developed using patients with epilepsy can also be used for individualized dosing of patients with mania, but before implementation, the accuracy of these models' predictions should be assessed in the target population.