Physiologically based pharmacokinetic data analysis

G. L., Gargas, M. L, Strother, D. E. Improving cancer dose-response characterization by using physiologically based pharmacokinetic modeling An analysis of pooled data for acrylonitrile-induced brain tumors to assess cancer potency in the rat. Risk Anal 2000, 20 135-151. 

A recent shift in emphasis has been from simple DMPK parameters to a more physiological interrogation of the data. Such physiological based pharmacokinetic modelling may provide mechanistic links to understand the influence of drug delivery formulations, or even the relationship between efficacy and toxicity at the tissue level. However literature examples of the benefit of such detailed analysis are sparse, or even lacking. 

The two most commonly used methods for characterizing pharmacokinetic data are noncompartmental analysis and the fitting of compartmental models. The latter technique can range from simple one to three well-stirred compartments to physiologically-based pharmacokinetic (PBPK) models, which are covered in the next section. The choice of which method to utilize will be largely dictated by the goals and objectives of the analysis. For example, descriptions of major pharmacokinetic parameters for linear systems (i.e., net systemic exposure is dose-proportional) can be easily calculated from a noncompartmental... 

While ejqierimental methods always require sufficient amount of chemicals for the estimation of drag absorption, computational in silico) methods can lead to the prediction of intestinal absorption based on chemical structure, and can thus be used before synthesis of compoimds. In silico predictions could be based both on relatively simple quantitative structure-activity relationships (QSAR) analysis and more complex physiologically based pharmacokinetic and/or pharmacodynamic models. Whichever the approach used for model building, computational methods should be based on experimental data that were obtained for a wide range of structurally diverse compoimds (training set). It should be noted, however, that current in silico methods, are not as reliable as experimental models. 

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