Population pharmacokinetic model of valproate and prediction of valproate serum concentrations in children with epilepsy
Abstract
AIM:
Using sparse data of valproate (VPA) serum concentrations to build a population pharmacokinetic (PPK) model of VPA in Chinese children with epilepsy and to predict serum concentrations for new patients using a Bayesian approach.
METHODS:
Two hundred epileptic children, whose VPA serum concentrations were collected, were divided randomly into two groups (A and B, n=100 each). The PPK parameter values of group A were calculated to establish a PPK Model by using the NPEM Program of USC*PACK software. Based on it, VPA serum concentrations of group B were predicted with the Bayesian Fitting Program of the USC*PACK software. To assess the accuracy and precision of prediction, a paired-comparisons t-test was run between predicted and observed concentrations, and then the mean prediction error (MPE), mean square prediction error (MSPE), root mean square prediction error (RMSPE), and coincidence rates for different percentages of prediction error were all calculated.
RESULTS:
Optimum PPK parameters were: Ka, 2.522+/-2.743 h(-1); Vs, 0.329+/-0.496 L/kg; and Kel, 0.0438+/-0.0384 h(-1). For group B, there was no significant difference between predicted and observed concentrations. MPE was -0.43 mg/L, MSPE was 115.40 (mg/L)2, and RMSPE was 5.47 mg/L. The coincidence rates for percentages of prediction error, which were less than 5 %, 10 %, 15 %, 20 %, 25 %, and 30 %, were 62 %, 74 %, 82 %, 85 %, 89 %, and 93 %, respectively.
CONCLUSION:
A PPK model of VPA in epileptic children was successfully established. Based on it, VPA serum concentrations can be predicted accurately with a Bayesian approach.
Keywords:
Using sparse data of valproate (VPA) serum concentrations to build a population pharmacokinetic (PPK) model of VPA in Chinese children with epilepsy and to predict serum concentrations for new patients using a Bayesian approach.
METHODS:
Two hundred epileptic children, whose VPA serum concentrations were collected, were divided randomly into two groups (A and B, n=100 each). The PPK parameter values of group A were calculated to establish a PPK Model by using the NPEM Program of USC*PACK software. Based on it, VPA serum concentrations of group B were predicted with the Bayesian Fitting Program of the USC*PACK software. To assess the accuracy and precision of prediction, a paired-comparisons t-test was run between predicted and observed concentrations, and then the mean prediction error (MPE), mean square prediction error (MSPE), root mean square prediction error (RMSPE), and coincidence rates for different percentages of prediction error were all calculated.
RESULTS:
Optimum PPK parameters were: Ka, 2.522+/-2.743 h(-1); Vs, 0.329+/-0.496 L/kg; and Kel, 0.0438+/-0.0384 h(-1). For group B, there was no significant difference between predicted and observed concentrations. MPE was -0.43 mg/L, MSPE was 115.40 (mg/L)2, and RMSPE was 5.47 mg/L. The coincidence rates for percentages of prediction error, which were less than 5 %, 10 %, 15 %, 20 %, 25 %, and 30 %, were 62 %, 74 %, 82 %, 85 %, 89 %, and 93 %, respectively.
CONCLUSION:
A PPK model of VPA in epileptic children was successfully established. Based on it, VPA serum concentrations can be predicted accurately with a Bayesian approach.