NONMEM, population pharmacokinetics

Jonsson, E.N. and M.O. Karlsson. 1999. Xpose - an S-PLUS-based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comp. Methods Prog. Biomed. 58 51-64. 

Duffull, S., Kirkpatrick, C., Green, B., and Holford, N., Analysis of population pharmacokinetic data using NONMEM and WinBUGS, Journal of Biopharmaceutical Statistics, Vol. 15, No. 1, 2005, pp. 53-73. 

Population pharmacokinetic data were pooled and a structural model fitted to pooled data using NONMEM. Individual pharmacokinetic parameter estimates were obtained from the base structural model. Individual steady state AUCs for the 24-hour efficacy score interval were then determined. 

The NONMEM (nonlinear mixed-effects modeling) software (Beal et al. 1992), mostly used in population pharmacokinetics, was developed at the University of California and is presently distributed by Globomax. For data management, post processing and diagnostic plots, the software S-plus (Mathsoft) is frequently used. 

This approach is called the first order (FO) method in NONMEM. This is the most widely used approach in population pharmacokinetic and pharmacodynamic data analysis, and has been evaluated by simulation. The use of the first-order Taylor series expansion to approximate the non-linear model in r], and, possibly,... 

White, D.B. Walawander, C.A. Tung, Y. Grasela, T.H. An evaluation of point and interval estimates in Population pharmacokinetics using NONMEM analysis. J. Pharmacokinet Biopharm 1991,19, 87-112. 

Vermes A et al. Population pharmacokinetics of flucytosine comparison and validation of three models using STS, NPEM, and NONMEM. Therapeutic Drug Monitoring, 2000, 22 676-687. 

S. B. Duffull, C. M. J. Kirkpatrick, B. Green, and N. H. G. Holford, Analysis of population pharmacokinetic data using NONMEM and WinBUGS. I Biopharm Stat 15 53-73 (2005). 

P. O. Maitre, M. Buhrer, D. Thomson, and D. R. Stanski, A three-step approach combining Bayesian regression and NONMEM population analysis application to midazolam. J Pharmacokinet Biopharm 19 377-384 (1991). 

N. M. Idkaidek, G. L. Amidon, D. E. Smith, N. M. Najib, and M. M. Hassan, Determination of the population pharmacokinetic parameters of sustained-release and enteric-coated oral formulations, and the suppository formulation of diclofenac sodium by simultaneous data fitting nsing NONMEM. Biopharm Drug Dispos 19 169-174 (1998). 

A. Rousseau, F. Leger, Y. Le Meur, F. Saint-Marcoux, G. Paintaud, M. Buchler, and P. Marquet, Population pharmacokinetic modeling of oral cyclosporin using NONMEM comparison of absorption pharmacokinetic models and design of a Bayesian estimator. 

TABLE 15.2 Bootstrap Estimates for Various Population Pharmacokinetic Parameters Compared to the NONMEM Generated Parameters... 

Intermediate workshop in population pharmacokinetic data analysis using the NONMEM system. Regents of the University of California, 1992. 

As stated in the introduction, the submodels may differ in fixed or random effects. The task is to learn how to communicate our ideas about the submodels to NONMEM. Suppose that during a population pharmacokinetic (PK) analysis of an orally administered drug, a model is used where the absorption and elimination rates are first order. The model is parameterized in terms of elimination rate (K), apparent volume of distribution (Vd) and absorption rate (KA), such that both K and KA are allowed to vary between subjects. Specifically, the values of K and KA for the th subject (Kj and KAj) are specified as follows ... 

C. E. Staatz and S. E. Tett, Comparison of two population pharmacokinetic programs, NONMEM and P-PHARM, for tacrolimus. Eur J Clin Pharmacol 58 597-605 (2002). 

Gibiansky, E., Gibiansky, L., and Bramer, S. Comparison of NONMEM, bootstrap, jackknife, and profiling parameter estimates and confidence intervals for the aripiprazole population pharmacokinetic model. Presented at American Association of Pharmaceutical Scientists Annual Meeting, Boston MA, 2001. 

The NONMEM package continues to be the most widely used software for population-based PK/PD analysis. Its limitations lie mostly with its user interface, which, despite the numerous modifications to the code including the NM-TRAN preprocessor, remains a warehouse of FORTRAN 77 subroutines. Several alternatives to NONMEM are currently available and others are still under development. It is beyond the scope of this chapter to compare the details of the software itself, nor is it practical given short lifetime of each release. Aarons - has recently reviewed and critiqued the currently available software for population pharmacokinetic analysis. 

The nonlinear mixed-effects method is depicted in Figure 10.4 and is described here using the conventions of the NONMEM software (2, 3) and the description by Vozeh ef a/. (3). It is based on the principle that the individual pharmacokinetic parameters of a patient population arise from a distribution that can be described by the population mean and the interindividual variance. Each individual pharmacokinetic parameter can be expressed as a population mean and a deviation, typical for an individual. The deviation is the difference between the population mean and the individual parameter and is assumed to be... 

A few programs are now available that allow the efficient simultaneous data analysis from a population of subjects. This approach has the significant advantage that the number of data points per subject can be small. However, using data from many subjects, it is possible to complete the analyses and obtain both between- and within-subject variance information. These programs include NONMEM and WinNON-MIX for parametric (model dependent) analyses and NPEM when non-parametric (model independent) analyses are required. This approach nicely complements the Bayesian approach. Once the population values for the pharmacokinetic parameters are obtained, it is possible to use the Bayesian estimation approach to obtain estimates of the individual patient s pharmacokinetics and optimize their drug therapy. 

First-Order (NONMEM) Method. The first nonlinear mixed-effects modeling program introduced for the analysis of large pharmacokinetic data was NONMEM, developed by Beal and Sheiner. In the NONMEM program, linearization of the model in the random effects is effected by using the first-order Taylor series expansion with respect to the random effect variables r], and Cy. This software is the only program in which this type of linearization is used. The jth measurement in the ith subject of the population can be obtained from a variant of Eq. (5) as follows ... 

S. P. Riley, Pharmacokinetic model selection within a population analysis using NONMEM and WinBUGS, in AAPS Workshop on Bayesian Primer. AAPS, Sait Lake City, UT, 2003. 

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