Population pharmacokinetics modeling of levetiracetam in Chinese children with epilepsy
Abstract
Aim: To establish a population pharmacokinetics (PPK) model of levetiracetam in Chinese children with epilepsy.
Methods: A total of 418 samples from 361 epileptic children in Peking University First Hospital were analyzed. These patients were divided into two groups: the PPK model group (n=311) and the PPK validation group (n=50). Levetiracetam concentrations were determined by HPLC. The PPK model of levetiracetam was established using NONMEM, according to a one-compartment model with first-order absorption and elimination. To validate the model, the mean prediction error (MPE), mean squared prediction error (MSPE), root mean-squared prediction error (RMSPE), weight residues (WRES), and the 95% confidence intervals (95% CI) were calculated.
Results: A regression equation of the basic model of levetiracetam was obtained, with clearance (CL/F)=0.988 L/h, volume of distribution (V/F)=12.3 L, and Ka=1.95 h−1. The final model was as follows: Ka=1.56 h−1, V/F=12.1 (L), CL/F=1.04×(WEIG/25)0.583 (L/h). For the basic model, the MPE, MSPE, RMSPE, WRES, and the 95%CI were 9.834 (−0.587–197.720), 50.919 (0.012–1286.429), 1.680 (0.021–34.184), and 0.0621 (−1.100–1.980). For the final model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were 0.199 (−0.369–0.563), 0.002082 (0.00001–0.01054), 0.0293 (0.001−0.110), and 0.153 (−0.030–1.950).
Conclusion: A one-compartment model with first-order absorption adequately described the levetiracetam concentrations. Body weight was identified as a significant covariate for levetiracetam clearance in this study. This model will be valuable to facilitate individualized dosage regimens.
Keywords:
Methods: A total of 418 samples from 361 epileptic children in Peking University First Hospital were analyzed. These patients were divided into two groups: the PPK model group (n=311) and the PPK validation group (n=50). Levetiracetam concentrations were determined by HPLC. The PPK model of levetiracetam was established using NONMEM, according to a one-compartment model with first-order absorption and elimination. To validate the model, the mean prediction error (MPE), mean squared prediction error (MSPE), root mean-squared prediction error (RMSPE), weight residues (WRES), and the 95% confidence intervals (95% CI) were calculated.
Results: A regression equation of the basic model of levetiracetam was obtained, with clearance (CL/F)=0.988 L/h, volume of distribution (V/F)=12.3 L, and Ka=1.95 h−1. The final model was as follows: Ka=1.56 h−1, V/F=12.1 (L), CL/F=1.04×(WEIG/25)0.583 (L/h). For the basic model, the MPE, MSPE, RMSPE, WRES, and the 95%CI were 9.834 (−0.587–197.720), 50.919 (0.012–1286.429), 1.680 (0.021–34.184), and 0.0621 (−1.100–1.980). For the final model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were 0.199 (−0.369–0.563), 0.002082 (0.00001–0.01054), 0.0293 (0.001−0.110), and 0.153 (−0.030–1.950).
Conclusion: A one-compartment model with first-order absorption adequately described the levetiracetam concentrations. Body weight was identified as a significant covariate for levetiracetam clearance in this study. This model will be valuable to facilitate individualized dosage regimens.