Population pharmacokinetics covariates

J. Ribbing and E. N. Jonsson, Power, selection bias and predictive performance of the population pharmacokinetic covariate model. J Pharmacokinet Pharmacodyn 31 109-134 (2004). 

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. 

The population pharmacokinetic aproach assesses the impact of various covariates on the pharmacokinetic of a drug. Nonlinear mixed effects modeling may be used to model the relationship between various covariates and pharmacokinetic parameters. Age or age group may be one of the covariates. This type of approach has its advantages as it involves assessment of the effect of age on the pharmacokinetics in the target population. 

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. 

Last, population pharmacokinetics of sibrotuzumab, a humanized monoclonal antibody directed against fibroblast activation protein (FAP), which is expressed in the stromal fibroblasts in >90% of malignant epithelial tumors, were analzyed in patients with advanced or metastatic carcinoma after multiple IV infusions of doses ranging from 5 mg/m to a maximum of 100 mg (78). The PK model consisted of two distribution compartments with parallel first-order and Michaelis-Menten elimination pathways from the central compartment. Body weight was significantly correlated with both central and peripheral distribution volumes, the first-order elimination clearance, and ymax of the Michaelis-Menten pathway. Of interest was the observation that body surface area was inferior to body weight as a covariate in explaining interpatient variability. 

A second type of study involves applying food as a covariate in population pharmacokinetic models. "" ... 

MaUet, A. Mentre, F. Gilles, J. Kelman, A.W. Thomson, A.N. Bryson, S.M. Whiting, B. Handling covariates in Population pharmacokinetics with an application to gentamicin. Biomed. Meas. Inform. Contr. 1988, 2, 673-683. 

Mandema, J.W. Verotta, D. Sheiner, L.B. Building population pharmacokinetic—pharmacokinetic models. I. Models for covariate effects. J. Pharmacokinet Biopharm 1992, 20, 511-528. 

Here 0 is a vector of mean population pharmacokinetic parameters and Q is the variance-covariance matrix of between-subject random variability. Np represents a p-dimensional multivariate normal distribution, where p is the number of parameters. It is often more useful to consider the values of the parameters for the individual to be related to the population parameters via a covariate relationship, in which case the expression may be written as... 

U. Wahlby, A. H. Thomson, P. A. MiUigan, and M. O. Karlsson, Models for time-varying covariates in population pharmacokinetic-pharmacodynamic analysis. Br J Clin Pharmacol 58 367-377 (2004). 

A. Mallet, F. Mentre, J. Gilles, A. W. Kelrnan, A. N. Thomson, S. M. Bryson, and B. Whiting, Handling covariates in population pharmacokinetics with an application to gentamicin. Biomed Meas Inform Contr 2 673-683 (1988). 

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). 

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... 

Alternatively, instead of using the EBE of the parameter of interest as the dependent variable, an estimate of the random effect (t ) can be used as the dependent variable, similar to how partial residuals are used in stepwise linear regression. Early population pharmacokinetic methodology advocated multiple linear regression using either forward, backwards, or stepwise models. A modification of this is to use multiple simple linear models, one for each covariate. For categorical covariates, analysis of variance is used instead. If the p-value for the omnibus F-test or p-value for the T-test is less than some cut-off value, usually 0.05, the covariate is moved forward for further examination. Many reports in the literature use this approach. 

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. 

De Alwis, D.P., Aarons, L., and Palmer, J.L. Population pharmacokinetics of ondansetron A covariate analysis. British Journal of Clinical Pharmacology 1998 46 117-125. 

Bonate, P.L. Covariate detection in population pharmacokinetics using partially linear mixed effects models. Pharmaceutical Research 2005 22 541-549. 

Wade, J.R., Beal, S.L., and Sambol, N.C. Interaction between structural, statistical, and covariate models in population pharmacokinetic analysis. Journal of Pharmacokinetics and Biopharmaceutics 1994 22 165-177. 

Wahlby, U., Jonsson, E.N., and Karlsson, M.O. Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis. AAPS PharmSci 2002 4 Article 27. 

Kovarki J M, Nashan B, Neuhaus P, et al. (2001). A population pharmacokinetic screen to identify demographic-clinical covariates of basiliximab in liver transplantation. Clin. Pharmacol. Ther. 69 201-209. 

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