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Aim: To establish a population pharmacokinetics (PPK) model for lamotrigine (LTG) in Chinese children with epilepsy in order to formulate an individualized dosage guideline. Methods: LTG steady-state plasma concentration data from therapeutic drug monitoring (TDM) were collected retrospectively from 284 patients, with a total of 404 plasma drug concentrations. LTG concentrations were determined using a HPLC method. The patients were divided into 2 groups: PPK model group (n=116) and PPK valid group (n=168). A PPK model of LTG was established with NON-MEM based on the data from PPK model group according to a one-compartment model with first order absorption and elimination. To validate the basic and final model, the plasma drug concentrations of the patients in PPK model group and PPK valid group were predicted by the two models. Results: The final regression model for LTG was as follows: CL (L/h)=1.01*(TBW/27.87) 0.635 *e -0.753*VPA *e 0.868*CBZ *e 0.633*PB , Vd (L)= 16.7*(TBW/27.87). The final PPK model was demonstrated to be stable and effective in the prediction of serum LTG concentrations by an internal and external approach validation. Conclusion: A PPK model of LTG in Chinese children with epilepsy was successfully established with NONMEM. LTG concentrations can be predicted accurately by this model. The model may be very useful for establishing initial LTG dosage guidelines.
Aim: To establish a population pharmacokinetics (PPK) model for lamotrigine (LTG) in Chinese children with epilepsy in order to formulate an individualized dosage guideline. Methods: LTG steady-state plasma concentration data from therapeutic drug monitoring (TDM) were collected retrospectively from 284 patients, with a total of 404 plasma drug concentrations. LTG concentrations were determined using a HPLC method. The patients were divided into 2 groups: PPK model group (n=116) and PPK valid group (n=168). A PPK model of LTG was established with NON-MEM based on the data from PPK model group according to a one-compartment model with first order absorption and elimination. To validate the basic and final model, the plasma drug concentrations of the patients in PPK model group and PPK valid group were predicted by the two models. Results: The final regression model for LTG was as follows: CL (L/h)=1.01*(TBW/27.87) 0.635 *e -0.753*VPA *e 0.868*CBZ *e 0.633*PB , Vd (L)= 16.7*(TBW/27.87). The final PPK model was demonstrated to be stable and effective in the prediction of serum LTG concentrations by an internal and external approach validation. Conclusion: A PPK model of LTG in Chinese children with epilepsy was successfully established with NONMEM. LTG concentrations can be predicted accurately by this model. The model may be very useful for establishing initial LTG dosage guidelines.
Using therapeutic drug monitoring (TDM) data from our laboratory, we have studied the concentration/dose relationship for lamotrigine and the influence of drug interactions in clinical practice. One hundred forty-nine lamotrigine samples from 104 adult patients were included in the study. The samples were collected as steady-state trough values, 9-16 hours after dose intake. Concomitant drug treatment was specified on the analysis request form. Lamotrigine serum concentrations were determined by high-performance liquid chromatography (HPLC). In 20 patients in monotherapy, the concentration/dose (C/D) ratio was 65 (range: 50-84) nmol/L/mg (mean and 95% confidence interval, antilog from lognormal distribution). In 37 patients with concomitant carbamazepine treatment, the C/D ratio was less than half that of the patients in monotherapy; 31 (2146) nmol/L/mg, and in 14 patients with phenytoin, it was even lower; 17 (13-23) nmol/L/mg. Valproic acid significantly increased the C/D to 251 (200-320) nmol/L/mg in 13 patients. Triple therapy with valproic acid and either carbamazepine or phenytoin (23 patients) yielded a C/D slightly above that of monotherapy, whereas a few patients on phenobarbital had a C/D slightly below that of monotherapy. The within-group C/D variation was comparatively small. The C/D ratio in a mixed lamotrigine TDM material shows a widespread intra- and interindividual variation, which can largely be explained by pharmacokinetic interactions with concomitantly used antiepileptic drugs. These results support the use of TDM in lamotrigine therapy.
This study develops a population pharmacokinetic model for lamotrigine (LTG) in Spanish and German patients diagnosed with epilepsy. LTG steady-state plasma concentration data from therapeutic drug monitoring were collected retrospectively from 600 patients, with a total of 1699 plasma drug concentrations. The data were analyzed according to a one-compartment model using the nonlinear mixed effect modelling program. The influences of origin (Germany or Spain), sex, age, total body weight, and comedication with valproic acid (VPA), levetiracetam, and enzyme-inducing antiepileptic drugs (phenobarbital [PB], phenytoin [PHT], primidone [PRM], and carbamazepine [CBZ]) were investigated using step-wise generalized additive modelling. The final regression model for LTG clearance (CL) was as follows: CL(L/h) = 0.028*total body weight*e(-0.713*VPA)*e0.663*PHT*e0.588*(PB or PRM)*e0.467*CBZ*e0.864*IND, where IND refers to two or more inducers added to LTG treatment; this factor as well as VPA, PHT, PB, PRM, and CBZ take a value of zero or one according to their absence or presence, respectively. The administration of inducers led to a significant increase in mean LTG CL (values of 0.045-0.070 L/h/kg vs. 0.028 L/h/kg being reached in monotherapy), whereas VPA led to a significant decrease in CL (0.014 L/h/kg). Thus, comedication with these analyzed drugs can partly explain the interindividual variability in population LTG CL, which decreased from the basic model by more than 40%. The proposed model may be very useful for clinicians in establishing initial LTG dosage guidelines. However, the interindividual variability remaining in the final model (clearance coefficient of variation close to 30%) make these a priori dosage predictions imprecise and justifies the need for LTG plasma level monitoring to optimize dosage regimens. Thus, this final model allows easy implementation in clinical pharmacokinetic software and its application in dosage individualization using the Bayesian approach.
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