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

Effect of CYP3A5*3 genotype on serum carbamazepine concentrations at steady-state in Korean epileptic patients

BACKGROUND AND OBJECTIVE

Carbamazepine (CBZ) is metabolized mainly by the CYP3A family of enzymes, which includes CYP3A4 and CYP3A5. Several studies have suggested that the CYP3A5*3 genotype influences the pharmacokinetics of CYP3A substrates. The present study aimed to assess the effect of the CYP3A5*3 genotype on serum concentration of CBZ at the steady-state in Korean epileptic patients.

METHOD

The serum concentrations of CBZ in 35 Korean epileptic patients were measured and their CYP3A5 genotype was determined. Fourteen patients were CYP3A5 expressors (two for CYP3A5*1/*1 and 12 for CYP3A5*1/*3) and 21 patients were CYP3A5 non-expressors (CYP3A5*3/*3). Dose-normalized concentrations (mean +/- SD) of CBZ were 9.9 +/- 3.4 ng/mL/mg for CYP3A5 expressors and 13.1 +/- 4.5 ng/mL/mg for CYP3A5 non-expressors (P = 0.032). The oral clearance of CBZ was significantly higher in CYP3A5 non-expressors than that of CYP3A5 expressors (0.056 +/-0.017 L/h/kg vs. 0.040 +/- 0.014 L/h/kg, P = 0.004). The CYP3A5 genotype affected the CBZ concentrations in Korean epileptic patients and is a factor that may contribute to inter-individual variability in CBZ disposition in epileptic patients.

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.