Population pharmacokinetics software

Aarons, L. (1999). Software for population pharmacokinetics and pharmacodynamics. Clin. Pharmacokinet. 36 255-264. 

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. 

The definitions and statistical theory of PPK, advantages, and disadvantages of PPK have been discussed in this chapter. Models, data type, methods, and software programs for estimating population pharmacokinetic parameters, design, and analysis of population pharmacokinetic studies have been reviewed, as well as its application in biopharmaceutics. The use of population methods continues to increase while there is a shortage of those who can implement the approach. 

The population pharmacokinetics of enoxaparin was described by a one-compartment IV bolus model, the parameters of which are presented in Table 12.1. The interindividual variability parameters specify variances in the log-scale of the lognormaUy distributed PK parameters, and the residual error parameter specihes the variance of the proportional error. This model was implemented in ADAPT II using the Fortran code, provided in Appendix 12.1, which is identical to the ICOMPCL.FOR code provided as part of the software distribution, except for the residual error model. 

Linear mixed effects models are primarily used in pharmacodynamic analysis or in the statistical analysis of pharmacokinetic parameters. Linear mixed effects models could also be used to analyze concentrationtime data from a 1-compartment model with bolus administration after Ln-transformation. The advantages to using mixed effects in an analysis are that observations within a subject may be correlated and that in addition to estimation of the model parameters, between- and within-subject variability may be estimated. Also, the structural model is based on the population, not on data from any one particular subject, thus allowing for sparse sampling. Most statistical packages now include linear mixed effects models as part of their analysis options, as do some pharmacokinetic software (Win-Nonlin). While linear mixed effects models are not cov-... 

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. 

Cobelli, C.. and Saccomani, M. P. (1990). Unappreciation of a priori identifiability in software packages causes ambiguities in numerical estimates. Am. J. Physiol. 2S8, E10S8-E10S9. Davidian, M., and Gallant, A. R. (1992). Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine. J. Pharmacokinet. Biopharm. 20, 529-556. 

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... 

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 ... 

D. J. Lunn, N. Best, A. Thomas, J. Wakefield, and D. Spiegelhalter, Bayesian analysis of population PK/PD models general concepts and software. I Pharmacokinet Pharmacodyn 29 271-307 (2002). 

It should be pointed out that not all software programs lead to the same model structural model parameter estimates and variance components. Roe (1997) compared the simulated pharmacokinetics of a drug having monoexponential kinetics where clearance was a function of saturable protein binding and renal function and volume of distribution was a function of saturable protein binding only. The basis for the simulated concentrations was a population analysis of 361 quinidine concentration-time measurements from 136 male patients who had experienced cardiac arrhythmia (Verme et al., 1992). The same distribution of simulated observations (e.g., 46 patients had only one sample collected, 33 patients had two samples collected) was used as in the actual study. She and many other participants on the project analyzed the dataset with seven different... 

Lunn, D.J., Best, N., Thomas, A., Wakefield, J., and SpiegeUialter, D. 2002. Bayesian analysis of population PK/PD models general concepts and software. /. Pharmacokinet. Pharmacodynam. 29 271-307. Magni, R, BeUazzi, R., Nauti, A., Patrini, C., and Rindi, G. 2001. Compartmental model identification based on an empirical Bayesian approach the case of thiamine kinetics in rats. Med. Biol Eng. Comput. 39 700-706. 

Lunn, D.J., Best, N., Thomas, A., Wakefield, J., and Spiegelhalter, D. 2002. Bayesian analysis of population PK/PD models General concepts and software. /. Pharmacokinet. Pharmacodynam. 29 271-307. 

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