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Aims: Vigabatrin is an antiepileptic drug used to treat some forms of severe epilepsy in children. The main adverse effect is ocular toxicity, which is related to the cumulative dose. The aim of the study is to identify an acceptable exposure range, both through the development of a population pharmacokinetic model of vigabatrin in children enabling us to calculate patient exposure and through the study of therapeutic response., Methods: We performed a retrospective study including children with epilepsy followed at Necker-Enfants Malades hospital who had a vigabatrin assay between January 2019 and January 2022. The population pharmacokinetic study was performed on Monolix2021 using a nonlinear mixed-effects modelling approach. Children treated for epileptic spasms were classified into responder and nonresponder groups according to whether the spasms resolved, in order to identify an effective plasma exposure range., Results: We included 79 patients and analysed 159 samples. The median age was 4.2 years (range 0.3-18). A 2-compartment model with allometry and creatinine clearance on clearance best fit our data. Exposure analysis was performed on 61 patients with epileptic spasms. Of the 22 patients who responded (36%), 95% had an AUC 0-24 between 264 and 549 mg.h.L -1 ., Conclusions: The population pharmacokinetic model allowed us to identify bodyweight and creatinine clearance as the 2 main factors explaining the observed interindividual variability of vigabatrin. An acceptable exposure range was defined in this study. A target concentration intervention approach using this pharmacokinetic model could be used to avoid overexposure in responder patients., (© 2024 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Levetiracetam is a broad-spectrum antiepileptic drug that exhibits high interindividual variability in serum concentrations in children. A population pharmacokinetic approach can be used to explain this variability and optimize dosing schemes. The objectives are to identify the best predictive population pharmacokinetic model for children and to evaluate recommended doses using simulations and Bayesian forecasting. A validation cohort included children treated with levetiracetam who had a serum drug concentration assayed during therapeutic drug monitoring. We assessed the predictive performance of all the population pharmacokinetic models published in the literature using mean prediction errors, root mean squared errors, and visual predictive checks. A population model was finally constructed on the data, and dose simulations were performed to evaluate doses. We included 267 levetiracetam concentrations ranging from 2 to 69 mg/L from 194 children in the validation cohort. Six published models were externally evaluated. Most of the models underestimated the variability of our population. A 1-compartment model with first-order absorption and elimination with allometric scaling was finally fitted on our data. In our cohort, 57% of patients had a trough concentration <12 mg/L and 12% <5 mg/L. To reach a trough concentration >5 mg/L, doses ≥30 mg/kg/d for patients ≤50 kg and ≥2000 mg/d for patients >50 kg are required. In our population, a high percentage of children had low trough concentrations. Our population pharmacokinetic model could be used for therapeutic drug monitoring of levetiracetam in children., (© 2021, The American College of Clinical Pharmacology.)

Lamotrigine is a broad-spectrum antiepileptic drug with high interindividual variability in serum concentrations in children. The aims of this study were to evaluate the predictive performance of pediatric population pharmacokinetic (PPK) models published on lamotrigine, to build a new model with our monitoring data and to evaluate the current recommended doses. A validation cohort included patients treated with lamotrigine who had a serum level assayed during therapeutic drug monitoring (TDM). PPK models published in the literature were first applied to the validation cohort. We assessed their predictive performance using mean prediction errors, root mean squared errors, and visual predictive checks. A new model was then built using the data. Dose simulations were performed to evaluate the doses recommended. We included 270 lamotrigine concentrations ranging from 0.5 to 17.9 mg/L from 175 patients. The median (range) age and weight were 11.8 years (0.8-18 years) and 32.7 kg (8-110 kg). We tested 6 PPK models; most had acceptable bias and precision but underestimated the variability of the cohort. We built a 1-compartment model with first-order absorption and elimination, allometric scaling, and effects of inhibitor and inducer comedications. In our cohort, 22.6% of trough concentrations were below 2.5 mg/L. In conclusion, we proposed a PPK model that can be used for TDM of lamotrigine in children. In our population, a high percentage of children had low trough concentrations of lamotrigine. As the intervals of recommended doses are large, we suggest aiming at the higher range of doses to reach the target concentration., (© 2020, The American College of Clinical Pharmacology.)

Valproate is an old-generation antiepileptic drug often used in children. The pharmacokinetics of valproate are noteworthy for a large and difficult to predict interindividual variability in measured serum concentrations and for saturable protein binding. A model-based approach to personalize valproate treatment could be relevant in pediatric patients. The aims of this study were to review all published valproate population pharmacokinetic models in children and assess them by external validation to determine their predictive performance. Through simulations with the best model, we evaluated dosing regimen. A validation data set included valproate serum concentrations assayed during routine therapeutic drug monitoring of epileptic children. We applied to our population 11 published pediatric population pharmacokinetic models. For each model, predictive performance was assessed by external validation, using bias and precision calculations as well as goodness-of-fit plots. Dose simulations were conducted with the best predictive model to evaluate dosing regimen. The validation data set contained 178 valproate concentrations ranging from 13.4 to 128 mg/L from 114 patients. The best model exhibited a mean prediction error of 6.6 mg/L and a root mean squared error of 25.1 mg/L, with no model misspecification evidenced by visual predictive check. In our cohort, half the patients had a trough concentration <50 mg/L. Simulations suggested increasing doses, especially for children ≤40 kg. External evaluation of published valproate pharmacokinetic models enabled us to identify a suitable model for simulations and Bayesian forecasting. Dosing regimen should be adjusted to weight, with decreasing doses with increasing weight., (© 2018, The American College of Clinical Pharmacology.)