The Population Approach

Data destined for pharmacokinetic analysis consist of one or more drug concentration vs. time observations, while pharmacodynamic data consist of specific concentration levels corresponding to a specific therapeutic effect or its validated biomarker. One distinguishes two types of data  

It should be emphasized that in the collected data, several responses may be measured (e.g., drug plasma concentration, arterial blood pressure), and diverse administration schedules (single dose and chronic dosing) may be considered. 

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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 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. 

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]. 

The knowledge of population kinetic parameters has been proved important, and up to the present, the population approach has had a wide spectrum of applications ... 

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

Flolford NHG. Population models for AlzheimePs and Parkinson s disease. In Aarons L, Balant LP, editors. The population approach Measuring and managing variability in response, concentration and dose. Brussels COST B1 European Commission 1997. p. 97-104. 

Steimer, J.L. Population models and methods, with emphasis on pharmacokinetics. In New Strategies in Drug Development and Clinical Evaluation The Population Approach Rowland, M., Aarons, L., Eds. Commission of European Communities Luxembourg, 1992 31 0. 

Ette, E.I. Miller, R. Gillespie, W.R., et al. The population approach FDA experience. In The Population Approach Measuring and Managing Variability in Response, Concentration and Dose Aarons, L., Balant, L.P., Danhof, M., Eds. Commission of the European Communities, European Cooperation in the Field of Scientific and Technical Research Brussels, 1997 271-275. 

Mentre, F. Ebelin, M.E. Validation of population pharmacokinetic/pharmacodynamic analyses review of proposed approaches. In The Population Approach Measuring and Managing Variability in Response, Concentration and Dose Aarons, L., Balant, L.P., Danhof M., Gex-Fabry, M., Gundert-Remy, U.A., Karlsson, M.O., Mentre, F., Morselli, R.L., Rombut, F., Rowland, M., Steimer, J.-L., Voseh, S., Eds. Commission of the European Communities, European Cooperation in the Eield of Scientific and Technical Research Brussels, 1997 147-160. Ette, E.I. Williams, P. Sun, H. Fadiran, E. Ajayi, F.O. Onyiah, L.C. The process of knowledge discovery from large pharmacokinetic data sets. J. Clin. Pharmacol. 2001, 41 (1), 25-34. 

Figure 5.6 The population approach plasma drug concentration-time data obtained from a large population of patients.

Despite these attractions, there are disadvantages in using the population approach. The extra resource required to collect, transport and assay large number of samples (may be thousands) is considerable. An extremely demanding aspect is that for a meaningful analysis to be performed, exact... 

At still larger r, say r = 3.5, the population approaches a cycle that now repeats every four generations the previous cycle has doubled its period to period-4 (Figure 10.2.4). 

PK and PD have been linked by many models, sometimes mechanistic and at other times empirical. These models are especially useful in better understanding the dose strategy and response, especially when applied by stochastic simulation. The population approach can be applied to multiple types of data—for example, both intensely and sparsely sampled data and preclinical to Phase 4 clinical data— and therefore has found great utility when applied to PK/PD modeling. 

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