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  • Describe the differences between empirical and mechanistic models.

  • Understand the differences between different types of compartmental analyses.

  • Describe the physiologic pharmacokinetic model with equations and underlying assumptions.

  • List the differences in data analysis between the physiologic pharmacokinetic model, the classical compartmental model, and the noncompartmental approaches.

  • Describe interspecies scaling and its application in pharmacokinetics and toxicokinetics.

  • Describe the statistical moment theory and explain how it provides a unique way to study time-related changes in macroscopic events.

  • Define mean residence time (MRT) and how it can be calculated.

  • Define the mean transit time (MTT) and how it can be used to calculate the mean dissolution time (MDT), or in vivo mean dissolution time, for a solid drug product given orally.

  • Using MRT, derive equations to estimate other pharmacokinetic parameters such as mean absorption time and total volume of distribution.

The study of pharmacokinetics describes the absorption, distribution, and elimination of a drug and its metabolites in quantitative terms (see Chapter 1). Ideally, a pharmacokinetic model uses the observed time course for drug concentrations in the body and, from these data, obtains various pharmacokinetic parameters to predict drug dosing outcomes, pharmacodynamics, and toxicity.

In developing a model, certain underlying assumptions are made by the pharmacokineticist as to the type of pharmacokinetic model, the order of the rate processes, tissue blood flow, the method for the estimation of the plasma or tissue volume, and other factors. Even with a more general approach such as the noncompartmental method, first-order drug elimination is often assumed in the calculation of image. In selecting a model for data analysis, the pharmacokineticist may choose more than one method of modeling, depending on many factors, including experimental conditions, study design, and completeness of data. The goodness-of-fit to the model and the desired pharmacokinetic parameters are other considerations. Each estimated pharmacokinetic parameter has an inherent variability because of the variability of the biological system and of the observed data.

In spite of challenges in the construction of these pharmacokinetic models, such models have been extremely useful in describing the time course of drug action, improving drug therapy by enhancing drug efficacy, and minimizing adverse reactions through more accurate dosing regimens. Pharmacokinetic models are used routinely within the development process of new molecules or drug delivery systems.

Models can be broadly categorized as empirical or mechanistic. Empirical models are focused on describing the data with the specification of very few assumptions about the data being analyzed. An example of an empirical model is one that is used for allometric scaling, a type of prediction of PK parameters across diverse species. On the other hand, mechanistic models specify assumptions and attempt to incorporate known factors about the systems surrounding the data into the model, while describing the available data (Bonate, 2011). Both physiological modeling and compartmental modeling fall into the latter category. Pharmacokinetic parameters can also be calculated ...

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