Population pharmacokinetics base model development

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. 

D. Bootstrapped data or samples (had 323 subjects (i = 1, 2, 3,..., 323) subjects data) (Do), on which the developed population pharmacokinetic model was based) were drawn with replacement from the observed data (Do) observed data could either appear in the bootstrap samples (D,) once, more than one time, or not at all. For each bootstrap data set, the structure was retained but the coefficients and the intercept were reestimated. 

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... 

In an external validation, data from a study other than that used to build the model are used as the validation or test data. The FDA guidance states that this is the most stringent means of validating a population pharmacokinetic model. Indeed, the performance of the hnal model developed on a given data set when projected onto data from a separate study goes a long way toward the establishment of a credible model and the conhdence in the recommendations based on such models. 

Pharmacokinetic models have been developed that attempt to relate environmental levels to PbB levels (Leggett 1993 O Flaherty 1995). The Integrated Exposure Uptake Biokinetic Model (IEUBK) developed by EPA is one of the most extensive efforts to date to make population-based predictions of PbB levels based upon environmental data. The model incorporates both exposure/uptake parameters and a biokinetic component to estimate the PbB distribution in the exposed population (EPA 1994). 

Jonsson F. Development of Bayesian population models. In Marklund S, editor. Physiologically Based Pharmacokinetic Modeling in Risk Assessment. 

Jonsson F. Physiologically based pharmacokinetic modeling in risk assessment. In Development of Bayesian population methods. Stockholm Uppsala University, 2001. 

Willmann, S., Hohn, K., Edginton, A., Sevestre, M., Solodenko, J., Weiss, W., Lippert, J. and Schmitt, W. (2007) Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. Journal of Pharmacokinetics and Pharmacodynamics, 34 (3), 401-431. 

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