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Table 6.3.1 Pharmacokinetic parameters and patient data
With these data we could explore some of these correlations. For example plotting the kel measured in these patient versus the clinical parameter creatinine clearance may result in a plot such as Figure 6.3.1.
Figure 6.3.1 Linear plot of observed kel versus creatinine clearance
Figure 6.3.1 suggests that there is a significant correlation between elimination of this drug and renal function as expressed by the creatinine clearance. A large fraction of the drug dose must be excreted into urine. If renal function is poor elimination would be impaired and the drug dosage regimen should be adjusted appropriately. We could also explore the relationship between apparent volume of distribution and creatinine clearance.
Figure 6.3.2 Linear plot of apparent volume of distribution and creatinine clearance
In Figure 6.3.2 we see that there is little correlation between the apparent volume of distribution and creatinine clearance.
In another study we might look at the effect of drug dose and pharmacokinetic parameters. Some data are shown in Table 6.3.2.
Table 6.3.2 Plasma concentrations after three different doses
Plotting these data on semi-log graph paper provides three lines with different slope and shape.
Figure 6.3.3 Semi-log plot of concentration versus time after three different doses
It would appear that these data represent nonlinear or saturable pharmacokinetics (which will be discussed in more detail in Chapter 21). A model which could explain these data are shown in Figure 6.3.4 along with a plot of AUC versus dose. This is another representation of these and more data collected after additional dose values which illustrates the nonlinear model.
Figure 6.3.3 Linear plot of AUC achieved after various doses
These and other mechanisms can be explored by modeling pharmacokinetic data.
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