Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Pharmacometrics to characterize pharmacokinetics and optimize treatment in understudied populations
Clinical trials frequently exhibit a lack of participant diversity, with underrepresentation of certain demographics, including pregnant women, individuals at the extremes of age (such as children or the elderly), and specific racial or ethnic groups (such as people of African ancestry). Consequentl...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English English |
| Published: |
Division of Clinical Pharmacology
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | Clinical trials frequently exhibit a lack of participant diversity, with underrepresentation of certain demographics, including pregnant women, individuals at the extremes of age (such as children or the elderly), and specific racial or ethnic groups (such as people of African ancestry). Consequently, suitable dose labeling is lacking at the time of drug approval for these groups, leaving healthcare practitioners with a dilemma: either exclude these groups from treatment or make dosing recommendations based on data from a potentially non-representative population, posing a risk of suboptimal or toxic treatment outcomes. Post-marketing pharmacokinetic studies in these populations are challenging due to low consent rates, difficulties with frequent blood sampling, unbalanced study designs resulting from the opportunistic nature of such studies, or resource constraints in developing countries, all contributing to sparse data. Pharmaceutical companies may also lack the incentive to invest in these studies when the medication's market performance is already stable and there is no immediate return on investment. In this thesis, I apply pharmacometrics for understudied populations with limited data across various therapeutic areas, including: optimizing dose of cefazolin, an antibiotic used forsurgical site infection prophylaxis, in children undergoing cardiac surgery with cardiopulmonary bypass; optimizing dose of rifabutin, an antibiotic used for TB, during lopinavir/ritonavir (LPV/r)-co treatment in children with TB-HIV co-infection; showcasing a model-based adherence monitoring tool using enalapril, an angiotensin-converting enzyme inhibitor for hypertension and heart failure, in a small cohort of African heart failure patients, and characterizing pharmacokinetics of esomeprazole, a proton pump inhibitor for hyperacidity and being investigated for preeclampsia, in patients with preterm preeclampsia. Using data contributed by 22 children on cefazolin, Isuggested a continuous infusion regimen for bypass surgeries, which improves the probability of target attainment compared with the intermittent weight-based regimen used in clinical settings. Using data from 28 children on rifabutin, I developed a parent-metabolite model to assess the weight-based dosing used in the studies, proposing a weight-band based regimen for rifabutin without and with LPV/r for controlled exposures across age groups. For enalapril, I devised a model-based adherence assessment method based on data from 30 adults, including 6 African heart failure patients, which improves adherence monitoring since it considers variations in pharmacokinetics and dosing schedules. Finally, I used modelling to identify pharmacokinetic changes in esomeprazole during pregnancy using data from 59 adults(including 10 pregnant) on esomeprazole. In summary, using pharmacometrics, I could interpret limited data from understudied populations to characterize pharmacokinetics, evaluate existing practices and guide the consideration of alternative dosing scenarios |
|---|