Fixed parameters in the population pharmacokinetic modeling of valproic acid might not be suitable: external validation in Chinese adults with epilepsy or after neurosurgery.

Abstract

Purpose: This study aims to assess the predictive performance of published valproic acid (VPA) population pharmacokinetic (PPK) models using an external data set in Chinese adults with epilepsy or after neurosurgery.

Methods: A total of 384 concentrations from 290 Chinese adults with epilepsy or after neurosurgery were used for external validation. Data on published VPA PPK models were extracted from the literature. Prediction-based diagnostics (such as F20 and F30), simulation-based diagnostics, and Bayesian forecasting were used to evaluate the predictability of models.

Results: The results of prediction-based diagnostics of all models were unsatisfactory. Models B, F, and H showed the best prediction performance in simulation-based diagnostics and Bayesian forecasting, demonstrating superior precision and accuracy. Bayesian forecasting demonstrated significant improvements in the model predictability.

Conclusion: The published PPK models showed extensive variation in predictive performance for extrapolation among Chinese adults with epilepsy or after neurosurgery patients. Fixed parameters of Vd and Ka in the PPK modeling of VPA might be the reason for the unsatisfied predictive performance. Bayesian forecasting significantly improved model predictability and may help to individualize VPA dosing.