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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 (PBPK) model with equations and underlying assumptions.

  • List the advantages and disadvantages related to the use of PBPK, population PK, and the noncompartmental PK approaches for data analyses and predictions.

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

  • Describe 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 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.

  • Estimate other PK parameters such as mean absorption time and total volume of distribution using MRT


The introductory chapter to this section (Chapter 11) presented the three main methods that can be used to calculate, predict, and simulate PK and PK/PD. The subsequent chapters provided the main equations used to describe and characterize PK profiles for drugs and biologics that display linear and nonlinear characteristics. All these equations can be used to describe individual and population PK and PK/PD data by incorporating them in a complete PK and PK/PD model. This chapter will first show how to conduct model-independent analyses (noncompartmental PK analyses), which can be used to scientifically describe and quantify the PK of drugs when a large number of observations are made after a drug dose. The latter method is crucial for drug and biologic development because of its simplicity and is also recognized for its robustness. While noncompartmental approaches are frequently used by undergraduate students, clinicians, and researchers, more elaborate model-based methods are often employed in drug development to characterize PK and/or PK/PD. For students or researchers interested in data fitting, the remainder of the chapter will focus on how to specify the PK and/or PK/PD models that can be derived from equations described in previous chapters. Simple models, such as those used to conduct allometric scaling from animals to humans, will be examined as well as more complicated ones, from population compartmental models to physiologically-based models.


Noncompartmental analyses provide an alternative method for describing drug pharmacokinetics without having to assign a particular compartmental model to the drug. Although this method is often considered to be model independent, there are still a few assumptions and key considerations that must not be overlooked. This approach is therefore better referred to as “noncompartmental” as it does assume a “model,” in that, among other things reviewed below, the PK needs to be linear and the terminal phase must be log-linear.

The first assumption is that the drug in question displays linear pharmacokinetics (DiStefano and Landaw, 1984; Gibaldi and Perrier, 2007). In other words, ...

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