Population pharmacokinetics analysis methods

Recording details of the studies, including the models used and associated parameter values reported, is an obvious starting place. Additional details include the chemical analysis method, the pharmacokinetic analysis method, the studied population (specifically subpopulations), number of healthy volunteers or patients, number of pharmacokinetic samples per patient, the dose, the formulation, and the route of administration. If one publication includes several groups of patients (or the same patient received two different formulations/concomitant medications), then each cohort may need to be treated as a repeated measure of the same study or within the same study, which may be indexed according to a study or patient covariate. 

Still, even with following Wagner s guidelines it may be that many different models fit the data equally well. The situations becomes more complex when variables other than time are included in the model, such as in a population pharmacokinetic analysis. Often then it is of interest to compare a number of different models because the analyst is unclear which model is the more appropriate model when different models fit almost equally to the same data. For example, the Emax model is routinely used to analyze hyperbolic data, but it is not the only model that can be used. A Weibull model can be used with equal success and justification. One method that can be used to discriminate between rival models is to run another experiment, changing the conditions and seeing how the model predictions perform, although this may be unpractical due to time or fiscal constraints. Another alternative is to base model selection on some a priori criterion. 

Although population pharmacokinetic parameters have been estimated either by fitting all individuals data together as if there were no kinetic differences, or by fitting each individual s data separately and then combining the individual parameter estimates, these methods have certain theoretical problems that can only be aggravated when the deficiencies of typical clinical data are present. The nonlinear mixed-effect analysis avoids many of these deficiencies and... 

Sheiner LB. The population approach to pharmacokinetic data analysis Rationale and standard data analysis methods. Drug Me tab Rev 1984 15 153-71. 

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,... 

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. 

Over the past 25 years a variety of methods have been proposed for the characterization of the population pharmacokinetics of drugs. In this chapter, the statistical framework for estimating population pharmacokinetics in terms of individual and population models is discussed as a prelude to discussing some of the methods used in estimating population pharmacokinetics. In doing so we have adopted a user-friendly approach described previously (14). The goals of a PPK analysis and the data type (1) will determine the method selected for the analysis. 

Although, during the early applications of therapeutic mAbs, pharmacokinetic modeling was rarely applied, a variety of analytical techniques has been used over the years to characterize the pharmacokinetics of this class of compounds. The application and information derived from three different methods of noncompart-mental analysis, individual compartmental analysis, and population analysis will be discussed in the following sections. 

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 ... 

---