Population pharmacokinetics data analysis

Fig. 3.12 Data set for a population pharmacokinetic data analysis. Individual data points from one individual are connected by thin lines. The thick line represents the population time course.

Intermediate workshop in population pharmacokinetic data analysis using the NONMEM system. Regents of the University of California, 1992. 

Duffull, S., Kirkpatrick, C., Green, B., and Holford, N., Analysis of population pharmacokinetic data using NONMEM and WinBUGS, Journal of Biopharmaceutical Statistics, Vol. 15, No. 1, 2005, pp. 53-73. 

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

There are many approaches used for PPK model development in the literature. These range from modeling population pharmacokinetic data without exploratory data analysis to approaches that incorporate the latter. Excellent examples of population pharmacokinetic model development, which incorporate exploratory data analysis into population pharmacokinetic model development, can be found in the articles by Ette and Ludden and Mandema, Verotta, and Sheiner Excellent reviews on the validation of PPK models are available in the literature. Thus, validation will not be discussed. 

S. B. Duffull, C. M. J. Kirkpatrick, B. Green, and N. H. G. Holford, Analysis of population pharmacokinetic data using NONMEM and WinBUGS. I Biopharm Stat 15 53-73 (2005). 

WeU accepted Provides rich and high quahty data Can establish a causal link between altered pharmacokinetics and the variable of interest Early results from specific studies enable expansion of patient population in Phase 3 studies not usually difficult to perform Relatively straightforward and simple data analysis Not usually useful for screening Frequent sampling is very difficult in patients in large clinical trials or in children Relationship between altered pharmacokinetics and clinical response may not be established Study sample usually does not represent the target population Small sample may fail to elicit extremes of altered kinetics... 

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. 

A data structure was created by combining PK and PD data from three clinical studies. In double-blind, randomized, placebo-controlled, parallel-group, 2 week Phase III studies performed in the target patient population, sparse blood samples were collected for population pharmacokinetic analysis (Table 6). 

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... 

The analysis of clinical pharmacokinetic data offers additional challenges. Typically, the number of samples available from an individual patient can be limited. In some cases, only one or two samples may be available. If population-based pharmacokinetic values are available, it may still be possible to analyze this limited clinical information using a Bayesian approach. Using patient and population information, the objective function becomes a function of both the residual between the observed and calculated data (as in weighted least squares) and the residual between the population and the calculated values of the parameters, as shown in Eq. (23) ... 

A few programs are now available that allow the efficient simultaneous data analysis from a population of subjects. This approach has the significant advantage that the number of data points per subject can be small. However, using data from many subjects, it is possible to complete the analyses and obtain both between- and within-subject variance information. These programs include NONMEM and WinNON-MIX for parametric (model dependent) analyses and NPEM when non-parametric (model independent) analyses are required. This approach nicely complements the Bayesian approach. Once the population values for the pharmacokinetic parameters are obtained, it is possible to use the Bayesian estimation approach to obtain estimates of the individual patient s pharmacokinetics and optimize their drug therapy. 

Naive Pooled Approach. The naive pooled approach, proposed by Sheiner and Beal, involves pooling all the data from all individuals as if they were from a single individual to obtain population parameter estimates.Generally, the naive pooled approach performs well in estimating population pharmacokinetic parameters from balanced pharmacokinetic data with small between-subject variations, but tends to confound individual differences and diverse sources of variability, and it generally performs poorly when dealing with imbalanced data. Additionally, caution is warranted when applying the naive pooled approach for PD data analysis because it may produce a distorted picture of the exposure-response relationship and thereby could have safety implications when applied to the treatment of individual patients. ... 

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 ... 

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. 

Sheiner, L.B. Beal, S.L. Estimation of pooled pharmacokinetic parameters describing populations. In Kinetic Data Analysis Endrenyi, L., Ed. Plenum Press, Newyork, 1981 271-284. 

P. Girard, L. B. Sheiner, H. Kastrissios, and T. F. Blascke, Do we need full compliance data for population pharmacokinetic analysis J Pharmacokinet Pharmacodyn 24 265-282 (1996). 

M. O. Karlsson, E. N. Jonsson, C. G. WUtse, and J. R. Wade, Assumption testing in population pharmacokinetic models illustrated with an analysis of moxonidine data from congestive heart failure patients. J Pharmacokinet Biopharm 26 207-246 (1998). 

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. 

L. B. Sheiner and S. L. Beal, Estimation of pooled pharmacokinetic parameters describing populations, in Kinetic Data Analysis, L. Endrenyi (Ed.). Plenum Press, New York, 1981, pp. 271-284. 

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