Approach population

By contrast, in the population approach, the raw data set that is analysed consists of concentration-time points (and other necessary data such as demographic information) taken from a large number (up to hundreds to thousands) of patients in Phase 11 and/or Phase 111 trials. The number of plasma samples per subject may be sparse but it is possible to estimate the individual pharmacokinetic characteristics of each subject and hence a measure of the mean parameters and their variability can be assessed. Relationships can be sought between patient characteristics (demographics, chnical status) and pharmacokinetic values is found, its consequence may be examined by looking for altered efficacy or safety which may not be possible in a traditional volunteer study. This might lead to demonstration of a therapeutic concentration range. 

A summary of some of the advantages and difficulties of the population approach is given in Table 5.4. 

Table 5.4 Advantages and cautions/difficulties of the population approach...

Steimer JL, Vozeh S, Racine-Poon A, et al. The population approach rationale, methods and applications in clinical pharmacology and... 

The conditions under which a population approaches a stationary, i.e. time independent, mutant distribution were derived from the kinetic differential equations. In this stationary distribution called quasispecies, the most frequent genotype of highest fitness, the master sequence, is surrounded by closely related mutants1 (Figure 10). 

A major physiological function of the kidney is to remove toxic metabolic wastes and excess fluids from the blood stream. This function may be impaired through chronic degeneration or as a result of injury. Hemodialysis is the most commonly prescribed means of blood purification for end-stage renal disease. The worldwide dialysis patient population approached 1 million at the end of the 20th century. 

Piotrovsky, V. Drug efficacy analysis as an exercise in dynamic (indirect-response) population PK-PD modelling. Population Approach Group... 

The so-called population approach is a standard technique, which is currently used to model data from clinical trials. It can be defined as a tool to study the variability in drug concentrations and/or pharmacological effects between individuals [8]. 

In addition to the STS and the NLME approach, other population approaches are available, including the Bayesian and the nonparametric modeling methods. These techniques are less frequently applied in drug development. Thus, the following section will refer to the NLME approach. 

The combined residual variability model is another widely used residual variability model for the population approach. This residual variability model contains a proportional and an additive component ... 

A full empirical PK model using the population approach contains, as described in Section 17.4, three sub-models the structural, the statistical, and the covariate. 

Overall, these models are closer to the empirical PD models, but major elements of the biological system are implemented. Semi-mechanistic models are mostly developed using the population approach and consequently they are data-driven and parameters are estimated from the data available. Parameters which cannot be estimated might be either fixed to biologically meaningful values or they are explored by other studies, including in vitro or preclinical in vivo studies. Overall, the number of parameters is still small, compared with mechanistic PD models and the majority of the parameters are estimated. 

Aarons, L, Balant, 1.. P., Mentre, F., et al. Population approaches in drug development. Report on an expert meeting to discuss population pharmacoki-netic/pharmacodynamic software. Eur J Clin Pharmacol 1994, 46 389-391. 

The population approach is a new point of view in clinical drug evaluation and therapy. It emphasizes the estimation of parameters describing the dose-concentration-response relationship both between and within patients (including average behavior and variability). The population approach recognizes vari-... 

According to the population approach, the analysis of collected data requires an explicit mathematical model, including parameters quantifying population mean profiles, interindividual variability, and residual variability including intraindividual variability and measurement error [460]. 

---