Physiologically-based pharmacokinetic modeling and simulation for initial dose optimization of levetiracetam in pediatrics
Year: 2025
Type: article
Abstract: Introduction Optimizing levetiracetam (LEV) dosing in children is challenging due to high pharmacokinetic variability, which often necessitates empirical dose titration. This study aimed to develop and verify a physiologically-based pharmacokinetic (PBPK) modeling and simulation to guide and optimize initial LEV dose selection in pediatric patients. Methods A whole-body PBPK model for LEV was developed and verified in adults, then scaled and verified in a pediatric population (0.5–12 years). This model was used to simulate various dosing regimens. Subsequently, a multivariate linear regression (MLR) analysis correlated key covariates (dose, regimen, body weight, and glomerular filtration rate) with simulated steady-state peak (C max ) and trough (C tr ) concentrations to create a practical dosing tool. Results The MLR model successfully explained over 90% of the variance (R 2 > 0.9) between covariates and simulated plasma concentrations. For a twice-daily regimen, daily doses of 40–60 mg/kg were required to achieve concentrations within a target therapeutic window ( e.g., C max of 20–46 mg/L). A three-times-daily regimen allowed for a broader effective dose range of 50–80 mg/kg/day, enabling higher total daily doses while maintaining C max within a safe range. Conclusion The combined PBPK-MLR approach provides a robust, data-driven framework to support rational first-dose prescriptions of LEV in children. This tool has the potential to accelerate therapeutic effects while enhancing treatment individualization. Prospective clinical validation is required to confirm the model predictive performance for drug exposure and, consequently, its impact on therapeutic efficacy.
Source: Frontiers in Pharmacology
Authors: Julia Macente, Rodolfo Hernandes Bonan, Edilainy Rizzieri Caleffi-Marchesini, Leonardo Régis Leira Pereira, Priscila De Freitas Lima, Pieter Annaert, Karel Allegaert, Andrea Diniz
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Award: G2823776494
OpenAlex ID: W4416976901
