Physiologically based pharmacokinetic examples

Recent publications discuss the utility of physiologically based pharmacokinetic models to predict the pharmacokinetics of discovery compounds in the rat [7, 9]. The folio vfing examples show the utility of applying these methods at an even earlier stage. 

For the sake of simplicity, simple monophasic pharmacokinetics (one compartment and one half-life) was assumed in the above example and in many other examples in this report. In real life, most chemicals express biphasic or polyphasic pharmacokinetics (several compartments and several half-lives). Squeezing a polyphasic pharmacokinetic behavior into a one-compartment model by assuming a single half-life may lead to negligible errors for some chemicals and serious misinterpretation of biomarker concentrations for others. The same can be said about nonlinear processes, such as metabolic induction, inhibition, and saturation. A good way to check the accuracy of a simple pharmacokinetic model is to verify its performance by comparing with a physiologically based pharmacokinetic (PBPK) model that may encompass the mentioned factors. 

The UEL for reproductive and developmental toxicity is derived by applying uncertainty factors to the NOAEL, LOAEL, or BMDL. To calculate the UEL, the selected UF is divided into the NOAEL, LOAEL, or BMDL for the critical effect in the most appropriate or sensitive mammalian species. This approach is similar to the one used to derive the acute and chronic reference doses (RfD) or Acceptable Daily Intake (ADI) except that it is specific for reproductive and developmental effects and is derived specifically for the exposure duration of concern in the human. The evaluative process uses the UEL both to avoid the connotation that it is the RfD or reference concentration (RfC) value derived by EPA or the ADI derived for food additives by the Food and Drug Administration, both of which consider all types of noncancer toxicity data. Other approaches for more quantitative dose-response evaluations can be used when sufficient data are available. When more extensive data are available (for example, on pharmacokinetics, mechanisms, or biological markers of exposure and effect), one might use more sophisticated quantitative modeling approaches (e.g., a physiologically based pharmacokinetic or pharmacodynamic model) to estimate low levels of risk. Unfortunately, the data sets required for such modeling are rare. 

Poulin and Theil have developed a mechanistic model for estimating the Vd based on physiologically based pharmacokinetics (PBPK). For this method, the tissue plasma partition coefficient for each organ of the body is calculated by consideration of the volume fraction of neutral and phospholipids and water found in the tissues of a particular organ. For example, the volume fraction of neutral lipids in human adipose tissue is 0.79 whereas the volume fraction of neutral lipids in cardiac tissue is 0.0115. By contrast the volume fraction of water in adipose and heart are 0.18 and 0.76 respectively. Combined with the P, these volume fractions are used to estimate the distribution of a drag molecule into each tissue. Summation of the product of tissue volume and tissue/plasma partition coefficient produces the estimate of Vd. ... 

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. 

Overall, the illustrated example of modeling metabolic lability shows that global in silico ADMET models are successful tools to optimize ADMET parameter. Future predictions of ADMET models might be added into complex tools such as physiological-based pharmacokinetic models yielding an in silico engine for ADMET support [98]. 

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... 

The choice of model should be based on biological, physiological, and pharmacokinetic plausibility. For example, compartmental models may be used because of their basis in theory and plausibility. It is easy to conceptualize that a drug that distributes more slowly into poorly perfused tissues than rapidly perfused tissues will show multi-phasic concentration-time profiles. Alternatively, the Emax equation, one of the most commonly used equations in pharmacodynamic modeling, can be developed based on mass balance principles and receptor binding kinetics. 

Theophylline is not only characterized by a narrow therapeutic index and distinct relationships between serum concentration and therapeutic and toxic effects, but also by a high interindividual pharmacokinetic variabihty. This variability is predominantly based on a high patient-to-patient variability in the metabolic clearance of theophylline that is confounded by numerous additional physiological, pathophysiological, and enviromnental factors. Intravenous theophylhne (given as aminophyl-line), for example, has been shown to result in considerable variations in serum concentrations among patients despite the same dose, and theophylline dose requirements to maintain serum concentration in the range of 10 to 20 lig/ml varied from 400 to 3200 mg/day. ... 

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