Modeling and simulations to support dose selection for eslicarbazepine acetate therapy in pediatric patients with partial-onset seizures

Abstract

Modeling and simulations were used to support body weight-based dose selection for eslicarbazepine acetate (ESL) in pediatric subjects aged 4–17 years with partial-onset seizures. A one-compartment pediatric population pharmacokinetic model with formulation-specific first-order absorption, first-order elimination, and weight-based allometric scaling of clearance and distribution volume was developed with PK data from subjects 2–18 years of age treated with ESL 5–30 mg/kg/day. Covariate analysis was performed to quantify the effects of key demographic and clinical covariates (including body weight and concomitant use of carbamazepine, levetiracetam, and phenobarbital-like antiepileptic drugs [AEDs]) on variability in PK parameters. Model evaluation performed using a simulation-based visual predictive check and a non-parametric bootstrap procedure indicated no substantial bias in the overall model and in the accuracy of estimates. The model estimated that concomitant use of carbamazepine or phenobarbital-like AEDs with ESL would decrease the exposure of eslicarbazepine, and that concomitant use of levetiracetam with ESL would increase the exposure of eslicarbazepine, although the small effect of levetiracetam may not represent a true difference. Model-based simulations were subsequently performed to apply target exposure matching of selected ESL doses for pediatric subjects (aged 4–17 years) to attain eslicarbazepine exposures associated with effective and well-tolerated ESL doses in adults. Overall, model-based exposure matching allowed for extrapolation of efficacy to support pediatric dose selection as part of the submission to obtain FDA approval for ESL (adjunctive therapy and monotherapy) in subjects aged 4–17 years, without requiring an additional clinical study.

Handling Delayed or Missed Dose of Antiseizure Medications

“…41 In all 16 population PK studies, 10 were prospective clinical studies. 29,31,[34][35][36][37][39][40][41][42] In addition, studies for carbamazepine, eslicarbazepine, levetiracetam, topiramate, and valproic acid were conducted in multicenters, 29,31,36,[39][40][41][42] in which studies for eslicarbazepine and levetiracetam contained intensive sampling data. 31,40,42 For population PK models of ASMs described by a 1-compartment model with first-order absorption and elimination, the concentration at time t (C t ; mg/L) could be estimated according to equation 1 (Eq.1).…”

“…Population PK Characteristics of ASMs A total of 16 population PK models for 11 ASMs were identified. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] The enrolled patients ranged from 44 in the study of levetiracetam for children 42 to 902 in the study of valproic acid for children. 41 In all 16 population PK studies, 10 were prospective clinical studies.…”

Use of Real‐World Data and Pharmacometric Modeling in Support of Lacosamide Dosing in Pediatric Patients Under 4 Years of Age

“…This approach allowed us to characterize the population pharmacokinetics of lacosamide in young pediatric patients suffering from epilepsy who receive lacosamide therapy in an off-label environment and to derive potential dosing recommendations in this understudied age group. The applied exposure matching approach has extensively been used by the FDA and others to facilitate extrapolation of adult clinical pharmacology data to pediatric populations [22][23][24][25] and is supported by the recent findings of the Pediatric Epilepsy Academic Consortium for Extrapolation. 6 The pharmacokinetics of lacosamide in pediatric patients >1 month of age could adequately be described by weight-based allometric modeling without the need for any further age-associated maturation function.…”

Model‐Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children

“…The benefits of the increased use of modeling and simulation in terms of improved pediatric dose finding during drug development, combined with bridging information from adolescent and adult studies have started to become well documented 20,21 . In addition, several pediatric use case examples highlighting the application of MIDD have found their way into the literature 22–26 . An exciting evolution of MIDD focused on pediatric drug development is that these use cases are not reliant on a single methodology or approach and more often than not are reliant on multiple and complementary approaches to inform critical decision and support a pediatric development strategy, as outlined in Table 2.…”