Population pharmacokinetic (PK) and pharmacodynamic (PD) modeling using a mixed effect modeling (MEM) approach has been widely used for various drug classes during development. The MEM approach provides a significant advantage when analyzing large scale clinical trials and special population where only a few samples are available per subject.
The aims of this thesis are to explore the applications and advantages of MEM approach in the analysis of target populations (e.g., late-life depression, intensive care unit patients) from various aspects.
1): To characterize the sources of variability and evaluate the impact of patients¡¯ specific characteristics on SSRIs disposition using hyper-sparse concentration data. This study demonstrated that age and weight are significant covariates on citalopram clearance and volume of distribution. The age effect persists across the entire age range (22 to 93 years). Thus elderly subjects may need to receive different dose of citalopram based on their age. The other late-life depression study shows that weight and CYP2D6 polymorphisms significantly impact on maximal velocity (Vm) of paroxetine elimination. Thus, female and male subjects with different CYP2D6 genotypes may receive different dose based on their metabolizer genotype.
2): To optimize a dosing strategy for general medical and intensive care unit (ICU) patients receiving enoxaparin by continuous intravenous infusion. The study suggests that dose should be individualized based on patients¡¯ renal function and weight. It is also found that patients in the ICU tend to have higher exposure, thus should receive lower dose than those in the general medical unit.
3): To evaluate the consistency of exposure using the deviation between model-predicted and observed concentrations (Cpred/Cobs ratio) and assess the stability and robustness of using the ratio in reflecting erratic adherence patterns. The simulations demonstrate that ratio could be used as the indicator of the extreme adherence conditions for both long and short-half life drug.
The knowledge gained in the thesis will contribute to the understanding the sources of variability in target population, including subjects specific characteristics, enzyme genetics and adherence, under conditions of highly sparse concentration sampling. This provides a basis whereby the magnitude and consistency of exposure can be examined in conjunction with the maintenance response of subjects in a future study as response data become available.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-03302006-142255 |
| Date | 26 May 2006 |
| Creators | Feng, Yan |
| Contributors | Robert R. Bies, Robert S. Parker, Bruce G. Pollock, Mark Sale, Randall B. Smith, Robert E Ferrell |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-03302006-142255/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
Page generated in 0.0228 seconds