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Bayesian Analysis

Combining Individual and Population Data

Student Objectives for this Chapter

Once a drug is on the market and even before release pharmacokinetic models and parameter values should be well developed. Linear model or nonlinear. Numerous covariates would have been studied. This information can be very useful for developing suitable dosing regimens. In some cases customized for individual patients. One example is the development of dosing regimens for warfarin on Another example for aminoglycosides. Search for 'drug dosing nomogram'. Other examples are included in resources such as FDA: Find Information about a Drug and NIH-NIAID: Search for Package Inserts.

A drug nomogram may be used to determine an initial dosing regimen. The calculation of this initial dosing regimen may be based on the patient weight or height. Other covariates such as renal function, cardiac function, liver status or smoking status might be considered. Mullen (1978) proposed a direct linear plot method (Figure 21.5.1) using data collected after two phenytoin steady state dosing regimens, .

Another approach is the use of therapeutic drug monitoring (TDM). Briefly, this involves collecting one or more blood samples from the patient after their initial dose or dosing regimen. These samples are quickly assayed and analyzed for pharmacokinetic parameters. These parameter values may be supported by Bayesian estimation which includes population parameter values and their uncertainty (standard deviation or variance) as well as the individual subject data in the analysis.


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