Population pharmacokinetics of levetiracetam in Chinese adult epilepsy patients with varying renal function: exposure simulation and individualized dosing adjustments.

Levetiracetam (LEV) has become a first-line treatment option for various types of epilepsy with a broad spectrum of efficacy and favorable pharmacokinetic profile. We aimed to develop a population pharmacokinetic (PPK) model for LEV and devise a model-based dosing guideline specific to Chinese adult epilepsy patients. Employing Phoenix NLME 7.0 software, we utilized the first-order conditional estimation and extended least squares method to establish the PPK model. The PK of LEV was effectively characterized using a one-compartment model. Monte Carlo simulations were then performed to generate dosing guidelines suitable for various patient groups. The Bayesian feedback method was employed to develop the clinical individual predictive model. Data from 80 Chinese adult patients yielded 148 LEV plasma concentrations for analysis. In the final model, the absorption rate constant was fixed at 2.44. The apparent volume of distribution and the apparent clearance (CL/F) had population typical values of 35.34 L and 3.24 L/h, respectively. CL/F of LEV was significantly influenced by creatinine clearance (CrCL), identified as a major covariate. Monte Carlo simulations indicated that regimens of 0.5 g, 0.75 g, 1.0 g, 1.5 g, 2.0 g, 2.5 g, and 3.5 g twice daily were associated with the highest probability of target attainment (PTA) in patients with different renal function levels. Accordingly, a user-friendly dose recommendation was formulated for these patients. The individual predictive model accurately matched the observed concentrations and managed to guide the personalized dose adjustment. The PPK model linked CL/F to CrCL. Model-based simulations suggest that higher dosage adjustments may be necessary for those with augmented renal function. The developed clinical individual predictive model could effectively guide personalized dose adjustments, potentially reducing the need for frequent drug concentration measurements.