Population pharmacokinetics covariate model development

Various methods are available to estimate population parameters, but today the nonlinear mixed effects modeling approach is the most common one employed. Population analyses have been performed for mAbs such as basiliximab, daclizu-mab and trastuzumab, as well as several others in development, including clenolixi-mab and sibrotuzumab. Population pharmacokinetic models comprise three submodels the structural the statistical and covariate submodels (Fig. 3.13). Their development and impact for mAbs will be discussed in the following section. 

Objective The objective of this analysis was to develop a population pharmacokinetic model for NS2330 and its major metabolite Ml, based on data from a 14-week proof of concept study in Alzheimer s disease patients, including a screening for covariates that might influence the pharmacokinetic characteristics of the drug and/or its metabolite. Subsequently, several simulations should be performed to assess the influence of the covariates on the plasma concentration-time profiles of NS2330 and its metabolite. 

Population pharmacokinetics can be extended to pharmacodynamics and PK/PD modeling using a link model like an effect compartment (Sheiner et al. 1979). In huge clinical trials only a limited number of patients can be included in a pharmacokinetic satellite study. The model is developed in this satellite. Knowing the demographic covariates of the patients in the whole study, concentration time curves and even effect time curves can be predicted. 

There are several approaches to population model development that have been discussed in the literature (7, 9, 15-17). The traditional approach has been to make scatterplots of weighted residuals versus covariates and look at trends in the plot to infer some sort of relationship. The covariates identified with the scatterplots are then tested against each of the parameters in a population model, one covariate at a time. Covariates identified are used to create a full model and the final irreducible, given the data, is obtained by backward elimination. The drawback of this approach is that it is only valid for covariates that act independently on the pharmacokinetic (PK) or pharmacokinetic/pharmacodynamic (PK/PD) parameters, and the understanding of the dimensionality of the covariate diata is not taken into account. 

One goal of population pharmacokinetic models is to relate subject-specific characteristics or covariates, e.g., age, weight, or race, to individual pharmacokinetic parameters, such as clearance. There are many different methods to determine whether such a relationship exists, some of which were discussed previously in the chapter, and they can be characterized as either manual or automated in nature. With manual methods, the user controls the model development process. In contrast, automated methods proceed based on an algorithm defined by the user a priori and a computer, not the user, controls the model development process. Consequently, the automated methods are generally considered somewhat less subjective than manual procedures. The advantage of the automated method is its supposed lack of bias and ability to rapidly test many different models. The advantage of the manual method is that the user... 

This example in population pharmacokinetics illustrates the process of starting with a data set and then moving through model development, ultimately leading to a model that can explain the data in terms of a few pharmacokinetic parameters and patient covariates. Once a model is developed, it can be used for many purposes, including answering questions to which no answer might be readily available or to just explain data. 

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