Dosing Optimization of Lamotrigine in Peripregnancy Epilepsy Through PopPK Modelling and Simulation

Lamotrigine demonstrates substantial interindividual pharmacokinetic variability during pregnancy, though the underlying mechanisms remain incompletely understood. The study aimed to develop a population pharmacokinetic model of lamotrigine in Chinese epileptic patients during the peripregnancy period, in order to identify the key influencing factors and thereby assist in providing a solution for the individualized administration of lamotrigine.

One hundred and twenty-eight perigestational epilepsy patients (293 plasma concentrations) from two Chinese hospitals between January 1, 2015 and May 31, 2024 were enrolled in the study. A nonlinear mixed-effects model was developed using Phoenix NLME™ (v8.3) with stepwise covariate selection. Model validation encompassed goodness-of-fit analysis, bootstrap analysis (n = 1000), prediction-corrected visual predictive checks, and normalized prediction distribution error evaluation. Optimal dosing regimens were derived through Monte Carlo simulations accounting for gestational/postpartum physiological changes.

A one - compartment model combined with an additive and proportional error model was employed to characterize the pharmacokinetics of lamotrigine. Each factor's impact on CL/F was systematically examined using a stepwise approach, resulting in the establishment of the final model: [Formula: see text]. CL/F was identified as the primary pharmacokinetic parameter, serving as the main outcome measure and foundational basis for dosing recommendations. The model demonstrated satisfactory accuracy and commendable predictability. LTG CL/F increased significantly from 5 weeks of gestation, with CL/F in stages 2, 3, and 4 being 131%, 193%, and 199% of stage 1 (<5 weeks), respectively, and declining sharply to 68% of stage 1 postpartum.

The CL/F of lamotrigine was found to be increased significantly from 5 weeks of gestation and dropped sharply postpartum. Weight, pregnancy stage, and co-administration with valproic acid were identified as significant influencing factors. The internal verification results of the model were generally satisfactory, however, prospective multicenter cohorts incorporating larger-scale external datasets are imperative to establish robust generalizability, ultimately enabling precise personalized dose optimization strategies for peripregnancy epileptic patients.