Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Pharmaceutical Sciences


Applied Pharmaceutical Sciences


Applied Pharmaceutical Sciences

First Advisor

Sara Rosenbaum


The population approach to pharmacokinetic involves the estimation of mean pharmacokinetic parameters and their variability within a study population. Advantages of this approach include the ability to i) utilize sparse data, ii) analyze data from large, heterogeneous populations to obtain realistic and relevant estimates of variability and iii) evaluate the influence of patient characteristics on pharmacokinetic estimation.

Sparse data from a phase ill clinical trial of the protease inhibitor, nelfinavir, were used to obtain estimates of the pharmacokinetic parameters and their variability. The effects of patient covariates on the pharmacokinetic parameters of nelfinavir were evaluated. Clearance, estimated from data in the latter part of a dosing interval, was estimated well due to random spread of the data in this part of the concentration-time profile. Only the influence of concomitant administration of the azole antifungal agent, fluconazole was statistically significant resulting in a reduction in clearance of 30%. Problems arose in the estimation of volume of distribution and the absorption rate constant and their variability. The lack of early samples and the lack of variability in the timing of these samples contributed to the difficulty in estimating these parameters.

A simulation study was designed to investigate design issues including the ability to detect a sub-population with a 30% reduction in clearance. A one-compartment model with intravenous input was employed. Different designs consisting of 2 samples per individual in 100 individuals were evaluated at two levels of interindividual variability (30% and 60%). When interindividual variability was 30%, a sub-population of 20 individuals could consistently be identified and the pharmacokinetic parameters of the model could be accurately estimated. Estimates of the variability parameters were less accurate but acceptable using some designs. When interindividual variability was 60%, no design could consistently identify the sub-population even when the sub-population was 30, and no design provided accurate estimates of all of the parameters. Overall, the performance of one design, which consisted of three sampling windows covering the whole dosing interval, proved superior to the other designs investigated. Increasing the total sample size and using the FOCE method in NONMEM improved the ability of this design.



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