Population pharmacokinetics model applications

Jen J, Laughlin M, Chung C, Heft S, Affrime MB, Gupta SK, Glue P, Hajian G (2002) Ribavirin dosing in chronic hepatitis C application of population pharmacokinetic-pharmacodynamic models, Clin Pharmacol Ther 72 349-361... 

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. 

Yang RSH, Mayeno AN, Lyons M, Reisfeld B. 2010. The application of physiologically-based pharmacokinetics (PBPK), Bayesian population PBPK modeling, and biochemical reaction network (BRN) modeling to chemical mixture toxicology. In Mumtaz M, editor, Principles and practices of mixture toxicology. Hoboken (NJ) John Wiley Sons. 

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. 

S. Retout, F. Mentre, and R. Bruno, Fisher information matrix for non-linear mixed-effects models evaluation and application for optimal design of enoxaparin population pharmacokinetics. StatMed 21 2623-2639 (2002). 

Ette (1) provides an example of the application of bootstrapping to PMM budding, specifically to a population pharmacokinetic (PPK) model. In this study it was desired that the deterministic model (one-compartment versus two-compartment) and the covariates for inclusion be known with a high degree of certainty (1, 3). 

There is at least one major area of activity pertaining directly to the environment for which the reader will seek in vain. The complexity of environmental problems and the availability of personal computers have led to extensive studies on models of varying sophistication. A discussion and evaluation of these lie well beyond the competence of an old-fashioned experimentalist this gap is left for others to fill but attention is drawn to a review that covers recent developments in the application of models to the risk assessment of xenobiotics (Barnthouse 1992), a book (Mackay 1991) that is devoted to the problem of partition in terms of fugacity — a useful term taken over from classical thermodynamics — and a chapter in the book by Schwarzenbach et al. (1993). Some superficial comments are, however, presented in Section 3.5.5 in an attempt to provide an overview of the dissemination of xenobiotics in natural ecosystems. It should also be noted that pharmacokinetic models have a valuable place in assessing the dynamics of uptake and elimination of xenobiotics in biota, and a single example (Clark et al. 1987) is noted parenthetically in another context in Section 3.1.1. In similar vein, statistical procedures for assessing community effects are only superficially noted in Section 7.4. Examples of the application of cluster analysis to analyze bacterial populations of interest in the bioremediation of contaminated sites are given in Section 8.2.6.2. 

Cross-validation is a leave-one-out or leave-some-out validation technique in which part of the data set is reserved for validation. Essentially, it is a data-splitting technique. The distinction lies within the manner of the split and the number of data sets evaluated. In the strict sense a -fold cross-validation involves the division of available data into k subsets of approximately equal size. Models are built k times, each time leaving out one of the subsets from the build. The k models are evaluated and compared as described previously, and a hnal model is dehned based on the complete data set. Again, this technique as well as all validation strategies offers flexibility in its application. Mandema et al. successfully utilized a cross-validation strategy for a population pharmacokinetic analysis with oxycodone in which a portion of the data was reserved for an evaluation of predictive performance. Although not strictly a cross-validation, it does illustrate the spirit of the approach. 

Beyond pharmacokinetics and pharmacodynamics, population modeling and parameter estimation are applications of a statistical model that has general validity, the nonlinear mixed effects model. The model has wide applicability in all areas, in the biomedical science and elsewhere, where a parametric functional relationship between some input and some response is studied and where random variability across individuals is of concern [458]. 

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