Non-compartmental analysis

The simplest non-compartmental parameter that can be obtained from the time course of the plasma concentration is its area under the curve AUC (see also Section 39.1.1)  

This parameter can be obtained by numerical integration, for example using the trapezium rule, between time 0 and the time T when the last plasma sample has been taken. The remaining tail of the curve (between T and infinity) must be estimated from an exponential model of the slowest descending part of the observed plasma curve ((3-phase) as shown in Fig. 39.15. The area under the curve AUC can thus be decomposed into a tmncated and extrapolated part  

The mean residence time MRT of the drug in plasma can be expressed in the form  

The quantity AUMC can be decomposed into an observed and an extrapolated part  

The extrapolated area can be expressed analytically by means of integration by parts between times T and infinity  

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Non-compartmental analysis uses techniques derived from statistical moment theory to... 

Based on the integrated PK database analysis, CL values were estimated and are displayed in Fig. 14.6 as a function of the cetuximab serum concentrations. A concentration-dependent decrease in CL was observed in the integrated PK database analysis, similar to that observed in the non-compartmental analysis. 

Where applicable, pharmacokinetic parameters (Cmax, Tmax, AUCo-24, hn) were calculated using a non-compartmental analysis employing a linear/log trapezoidal method. 

Muir KT, Gomeni RO. Non-compartmental analysis. In Pharmacokinetics in Drug Development. Volume 1 Clinical Study Design and Analysis. Bonate PL, Howard DR, eds. 2004. AAPS Press, Arlington, VA. pp. 235-265. 

In many cases pharmacokinetic data (i.e. plasma drug concentration versus time data) cannot be fitted to an explicit equation equivalent to a system containing a discrete number of compartments into which dmg distributes. This data analysis requires some form of non-compartmental analysis (also referred to as model-independent analysis.) This is achieved by the use of statistical moment theory. 

For a dmg administered orally, MRT is the sum of time spent in the gastrointestinal tract (mean absorption time) as well as time spent in the rest of the body. In the case of a one-compartment model drug, the mean absorption time is actually equal to the reciprocal of the absorption rate constant (Eq. A.55) and is, therefore, proportional to the absorption half life. For non-compartmental analysis, the mean absorption time is still a good indicator of the rate of drug absorption. In order to get an estimate of mean absorption time in the non-compartmental situation, the drug is administered both orally and intravenously to a subject. Then ... 

Deleu D, Sarre S, Michotte Y, Ebinger G. Simultaneous in vivo microdialysis in plasma and skeletal muscle a study of the pharmacokinetic properties of levodopa by non-compartmental analysis. J Pharm Sd 1994 83 25-8. 

Pharmacokinetics is closely related to pharmacodynamics, which is a recent development of great importance to the design of medicines. The former attempts to model and predict the amount of substance that can be expected at the target site at a certain time after administration. The latter studies the relationship between the amount delivered and the observable effect that follows. In some cases the observable effect can be related directly to the amount of drug delivered at the target site [2]. In many cases, however, this relationship is highly complex and requires extensive modeling and calculation. In this text we will mainly focus on the subject of pharmacokinetics which can be approached from two sides. The first approach is the classical one and is based on so-called compartmental models. It requires certain assumptions which will be explained later on. The second one is non-compartmental and avoids the assumptions of compartmental analysis. 

The primary analysis examined pharmacokinetic parameters calculated from plasma concentrations of CYS-conjugated XYZ1234 using non-compartmental techniques. The secondary analysis examined the pharmacokinetic parameters of unconjugated XYZ1234. 

The advantages of using non-compartmental methods for calculating pharmacokinetic parameters, such as systemic clearance (CZg), volume of distribution (Vd(area))/ systemic availability (F) and mean residence time (MRT), are that they can be applied to any route of administration and do not entail the selection of a compartmental pharmacokinetic model. The important assumption made, however, is that the absorption and disposition processes for the drug being studied obey first-order (linear) pharmacokinetic behaviour. The first-order elimination rate constant (and half-life) of the drug can be calculated by regression analysis of the terminal four to six measured plasma... 

In recent years, non-compartmental or model-independent approaches to pharmacokinetic data analysis have been increasingly utilized since this approach permits the analysis of data without the use of a specific compartment model. Consequently, sophisticated, and often complex, computational methods are not required. The statistical or non-compartmental concept was first reported by Yamaoka in a general manner and by Cutler with specific application to mean absorption time. Riegelman and Collier reviewed and clarified these concepts and applied statistical moment theory to the evaluation of in vivo absorption time. This concept has many additional significant applications in pharmacokinetic calculations. 

It often takes many years of research, analysis, and writing to produce credible scientific articles in peer-reviewed journals. By comparison, critical policy decisions can sometimes be made over the span of months, weeks, or even days. The scientific literature on the impact of atmospheric deposition on biological diversity is vast, dispersed, often compartmentalized into various scientific specialties, and usually difficult to understand for non-scientists. Policy work requires synthesis and a solution oriented approach that is widely understood. Non-govemmental organizations may be able to play an important role in improving the flow of information from science to policy. 

We make no attempt to characterize these chemicals beyond the generalizations that (i) wide ranges of both primary and secondary plant substances act as excitatory stimuli (ii) inhibition of investment behaviors is triggered mainly by secondary substances but sometimes by unfavorable balances of primary nutrients and (iii) because they are relatively non-volatile and effectively compartmentalized, many phytochemicals generating the inhibitory inputs influence insect behavior only during or after the examining phase when direct contact has been established. Readers are referred to Hedin et al. (1974) for analysis of behaviorally active phytochemicals by chemical class. 

Compartmental modeling involves the specification of a structural mathematical model (commonly using either explicit or ordinary differential equations) and system parameters are estimated from fitting the model to pharmacokinetic data via non linear regression analysis or population mixed effects modeling. One popular structural model is the open two-compartment model shown in Figure 6.10. 

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