Kinetic profile of carbamazepine in an adult 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.

Population pharmacokinetic modeling of steady state clearance of carbamazepine and its epoxide metabolite from sparse routine clinical data

OBJECTIVE

To develop a population pharmacokinetic model to evaluate the effects of variety of covariates on clearance of carbamazepine (CBZ) and its main metabolite carbamazepine-10,11-epoxide (CBZE) in Chinese population.

METHODS

Serum samples at steady trough state (n = 459) were collected prospectively from 408 compliant outpatients during their routine clinical care. CBZ and CBZE concentrations were simultaneously determined by high performance liquid chromatography. Population clearance (CL) of CBZ and CBZE were estimated by non-linear mixed effect modeling and NONMEM program with a one-compartment model of first-order absorption and elimination.

RESULTS

Total body weight (TBW), dose and concomitant medication were all important determinants of CL of CBZ and CBZE. The final regression model for CBZ was: where VPA = 1 for patients comedicated with valproic acid and its dose greater than 18 mg/kg, otherwise VPA = 0; PHT = 1 for patients comedicated with phenytoin, otherwise PHT = 0; PB = 1 for patients comedicated with phenytoin, otherwise PB = 0. The final regression CL model for the CBZE was: where VPA = 1 for patients comedicated with valproic acid, otherwise VPA = 0.

CONCLUSION

The current models, which describe CL of CBZ and CBZE in terms of patient specific details, can be used as a reference to optimize CBZ therapy in Chinese epilepsy patients.