A Population Pharmacokinetic Model of Gabapentin Developed in Nonparametric Adaptive Grid and Nonlinear Mixed Effects Modeling

Abstract

Gabapentin is used in analgesic treatment of neuropathic pain, and large interindividual variation has been observed in the pharmacokinetics (PK) of the drug. The aim of this study was to develop a population PK model for gabapentin appropriate for monitoring patients with neuropathic pain and for individualizing their dose regimens. Steady-state serum concentrations of gabapentin, distributed over a dosage interval, were obtained from 16 adult patients. Data were analyzed with an iterative 2-stage Bayesian and a nonparametric adaptive grid algorithm (NPAG) (USC*PACK) and with nonlinear mixed effects modeling (NONMEM). Compartmental population models for gabapentin PK were developed in NPAG and NONMEM using creatinine clearance and body weight as covariates. Bioavailability was included in the models as a function of dose by using a hyperbolic function derived from data previously reported in the literature. The mean population parameter estimates from the final NPAG model predicted individual serum concentrations reasonably well. The models developed in NONMEM provided additional information about the relevance of the various possible covariates and also allowed for further evaluation by simulation from the model. The population PK model may be utilized in the MM-USCPACK monitoring software (MM: multiple model dosage design) for predicting and achieving individually optimized steady-state serum concentrations of gabapentin.