Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making.

In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.

A population pharmacokinetic model of gabapentin developed in nonparametric adaptive grid and nonlinear mixed effects modeling.

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.