Predictive capacity of carbamazepine pharmacokinetic parameters in a Portuguese outpatient population

Development of a population pharmacokinetic model for carbamazepine based on sparse therapeutic monitoring data from pediatric patients with epilepsy

BACKGROUND

Population models can be important extensions of therapeutic drug monitoring (TDM), as they allow estimation of individual pharmacokinetic parameters based on a small number of measured drug concentrations.

OBJECTIVE

This study used a Bayesian approach to explore the utility of routinely collected and sparse TDM data (1 sample per patient) for carbamazepine (CBZ) monotherapy in developing a population pharmacokinetic (PPK) model for CBZ in pediatric patients that would allow prediction of CBZ concentrations for both immediate- and controlled-release formulations.

METHODS

Patient and TDM data were obtained from a pediatric neurology outpatient database. Data were analyzed using an iterative 2-stage Bayesian algorithm and a nonparametric adaptive grid algorithm. Models were compared by final log likelihood, mean error (ME) as a measure of bias, and root mean squared error (RMSE) as a measure of precision.

RESULTS

Fifty-seven entries with data on CBZ monotherapy were identified from the database and used in the analysis (36 from males, 21 from females; mean [SD] age, 9.1 [4.4] years [range, 2-21 years]). Preliminary models estimating clearance (Cl) or the elimination rate constant (K(el)) gave good prediction of serum concentrations compared with measured serum concentrations, but estimates of Cl and K(el) were highly correlated with estimates of volume of distribution (V(d)). Different covariate models were then tested. The selected model had zero-order input and had age and body weight as covariates. Cl (L/h) was calculated as K(el) . V(d), where K(el) = [K(i) - (K(s) . age)] and V(d) = [V(i) + (V(s) . body weight)]. Median parameter estimates were V(i) (intercept) = 11.5 L (fixed); V(s) (slope) = 0.3957 L/kg (range, 0.01200-1.5730); K(i) (intercept) = 0.173 h(-1) (fixed); and K(s) (slope) = 0.004487 h(-1) . y(-1) (range, 0.0001800-0.02969). The fit was good for estimates of steady-state serum concentrations based on prior values (population median estimates) (R = 0.468; R(2) = 0.219) but was even better for predictions based on individual Bayesian posterior values (R(2) = 0.991), with little bias (ME = -0.079) and good precision (RMSE = 0.055).

CONCLUSIONS

Based on the findings of this study, sparse TDM data can be used for PPK modeling of CBZ clearance in children with epilepsy, and these models can be used to predict Cl at steady state in pediatric patients. However, to estimate additional pharmacokinetic model parameters (eg, the absorption rate constant and V(d)), it would be necessary to combine sparse TDM data with additional well-timed samples. This would allow development of more informative PPK models that could be used as part of Bayesian dose-individualization strategies.

Bayesian estimation of six different sets of carbamazepine pharmacokinetic parameters in Egyptian adult epileptic patients

The individualization of carbamazepine (CBZ) dosage regimen based on estimation of pharmacokinetic (PK) parameters and measurement of serum drug concentration in epileptic patients can help to control epilepsy. In a retrospective study, the predictive performance of six different sets of CBZ PK parameters selected according to the literature was evaluated in 60 adult epileptic patients. Patients were administered controlled release CBZ (dose range: 200-1200 mg day(-1)) as monotherapy and one steady state serum concentration of the drug was available for each patient. The Bayesian Program of Abbott (PKS system; Abbott Laboratories, Wiesbaden, Germany) was used in the prediction process. Predictive measures included estimation of mean prediction error (mpe) for bias, mean squared prediction error (mspe) and root mean squared prediction error (rmspe) for precision. The analysis showed that three of the investigated six sets achieved the best predictive performance in Egyptian patients and consequently, the PK parameters of any of these three sets can be used by the Bayesian approach as prior information for CBZ dose optimization among the Egyptian adult population.