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Noncompartmental Model

The noncompartmental model provides a framework to introduce and use statistical moment analysis [Pg.92]

FIGURE 8.2 The single (A) and two (B) accessible pool models. See text for explanation. [Pg.93]

The two accessible pool model accommodates a more complex experimental format than does the single pool model. For example/ one could have inputs into both poolS/ and samples from both as well. However/ in most pharmacokinetic studies with the two accessible pool model/ pool 2 is plasma and input is only into pool 1. In this situation/ the pharmacokinetic parameters depend on bioavailability and can only be estimated up to a proportionality constant/ as is the case with so-called oral clearance (CLjF), referred to as relative clearance in this chapter. [Pg.93]

The kinetic parameters of the noncompartmental model are those defined previously for the accessible pool and system. However/ the formulas depend upon the experimental protocol/ especially on the mode of drug administration. In this chapter/ only the canonical inputs will be considered/ such as an intravenous bolus (or multiple boluses) or constant infusion (or multiple constant infusions). References will be given for those interested in more complex protocols. [Pg.93]

The relationships among the accessible pool parameters in the noncompartmental model are given in the following equations  [Pg.93]


E.M. Landau and J.J. DiStefano III, Multiexponential, multicompartmental and noncompartmental modeling II, Data analysis and statistical considerations, Am. J. Physiol. 246 (1984) R665-R677. [Pg.219]

Conversely, some substances are transported relatively slowly to their site of degradation, transformation, or excretion, so that the rate of diffusion limits their rate of removal from the system. Substances of this nature are best described by noncompartmental models and power functions. [Pg.169]

Noncompartmental models were introduced as models that allow for transport of material through regions of the body that are not necessarily well mixed or of uniform concentration [248]. For substances that are transported relatively slowly to their site of degradation, transformation, or excretion, so that the rate of diffusion limits their rate of removal from the system, the noncompartmental model may involve diffusion or other random walk processes, leading to the solution in terms of the partial differential equation of diffusion or in terms of probability distributions. A number of noncompartmental models deal with plasma time-concentration curves that are best described by power functions of time. [Pg.202]

How does one resolve the difficulty associated with partial differential equations The most common way is to reduce the system into a finite number of components. This can be accomplished by lumping together processes based upon time or location, or a combination of the two. One thus moves from partial derivatives to ordinary derivatives, where space is not taken directly into account. This reduction in complexity results in the compartmental models discussed later in this chapter. The same lumping process also forms the basis for the noncompartmental models discussed in the next section, although the reduction is much simpler than for compartmental models. [Pg.91]

The relationships among the system parameters for the noncompartmental model are... [Pg.94]

There are several reasons for going first to this level of generality for the n-compartment model. First/ it points out clearly that the theories of noncompartmental and compartmental models are very different. While the theory underlying noncompartmental models relies more on statistical theory/ especially in developing residence time concepts [see/ e.g./ Weiss (11)]/ the theory underlying compartmental models is really the theory of ordinary/ first-order differential equations in which/ because of the nature of the compartmental model applied to biological applications/ there are special features in the theory. These are reviewed in detail in Jacquez and Simon (5)/ who also refer to the many texts and research articles on the subject. [Pg.98]

If one has pharmacokinetic data and knows that the situation calls for nonlinear kinetics, then compartmental models, no matter how difficult to postulate, are really required. Noncompartmental models cannot deal with the time-varying situation. [Pg.100]

However, more can be said about the i5 y that is important in comparing compartmental and noncompartmental models. Suppose there is a generic input into compartment 1 only, f iq (remember, in this... [Pg.102]

This discussion will rely heavily on the following sources. First, the publications of DiStefano and Landaw (22, 23) deal with issues related to compartmental versus single accessible pool noncompartmental models. Second, Cobelli and Toffolo (3) discuss the two accessible pool noncompartmental model. Finally, Coveil et al. (4) provide the theory to demonstrate the link between noncompartmental and compartmental models in estimating the pharmacokinetic parameters. [Pg.102]

Suppose one has a set of pharmacokinetic data. The question is how to obtain information from the data related to the disposition of the drug in question. DiStefano and Landaw (22) deal with this question by making the distinction between models of data and models of system. Understanding this distinction is useful in understanding the differences between compartmental and noncompartmental models. [Pg.103]

As discussed, the noncompartmental model divides the system into two components an accessible pool and nonaccessible pools. The kinetics of the nonacces-sible pools are lumped into the recirculation-exchange arrows. From this, as has been discussed, we can estimate pharmacokinetic parameters describing the accessible pool and system. [Pg.103]

When are the parameter estimates from the noncompartmental model equal to those from a linear, constant-coefficient compartmental model As DiStefano and Landaw (22) explain, they are equal when the equivalent sink and source constraints are valid. The equivalent source constraint means that all... [Pg.103]

The equivalent sink constraint is illustrated in Figure 8.8. In Figure 8.8A, the constraint holds and hence the parameters estimated from either the noncompartmental model (left) or the multicompart-mental model (right) will be equal. If the multi-compartmental model is a model of the system, then, of course, the information about the drug s disposition will be much richer, since many more specific parameters can be estimated to describe each compartment. [Pg.104]

In Figure 8.8B, the constraint is not satisfied, and the noncompartmental model is not appropriate. [Pg.104]

Assume a linear, constant-coefficient compartmental model in which compartment 1 is the accessible compartment into which the drug is administered and from which samples are taken. Following a bolus injection of the drug, the volume Vi will be estimated as a parameter of the model. Vi thus will correspond to Va for the noncompartmental model. The clearance rate from compartment 1, CLi, is equal to the product of Vi and A oi ... [Pg.104]

In conclusion, noncompartmental models and linear, constant-coefficient models have different domains of validity. When the domains are identical, then the pharmacokinetic parameters estimated by either method should, in theory, be equal. If they are not, then differences are due to the methods used to estimate them. [Pg.105]

Cobelli C, Toffolo G. Compartmental versus noncompartmental modeling for two accessible pools. Am J Physiol 1984 247 R488-96. [Pg.105]

DiStefano JJ III, Landaw EM. Multiexponential, mul-ticompartmental and noncompartmental modeling I Methodological limitations and physiological interpretations. Am J Physiol 1984 246 R651-64. [Pg.105]

DiStefano, J.J. and Landaw, E.M. Multiexponential, multi-compartmental, and noncompartmental modeling. I. Methodological limitations and physiological interpretations. American Journal of Physiology 1984 246 R651-R664. [Pg.368]

This technique worked quite well in measuring the T4 volume of distribution, but it led to an artificially high volume of distribution when T3 was studied (Figure 19.3), since it ignored the early data points in the disappearance curve. This was corrected by the introduction of the noncompartmental model that integrated the total area under the disappearance curve to calculate volume of distribution (Oppenheimer et al., 1975). [Pg.194]

There are three basic approaches to modeling pharmacokinetic data traditional compartmental models - (or classical modeling described in Chapters 1 and 12), noncompartmental models - (examined in this chapter), and physiologically based models. To understand when to use noncompartmental modeling, it is necessary to know the assumptions of each approach and the desired objective of the pharmacokinetic study. [Pg.284]

This approach has been incorrectly described as structureless pharmacokinetics, or model-independent pharmacokinetics. These references to noncompartmental modeling really are misnomers. This approach is not model-less instead, it uses a simpler, more general model. Another term used to describe this process is nonpara-metric pharmacokinetics because a structure with compartments and corresponding... [Pg.284]


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