Tuberculosis continues to claim millions of lives each year despite enormous efforts to control the epidemic over the past century. It remains the leading cause of death worldwide from a curable infectious disease. Tuberculosis is a significant cause of maternal mortality and morbidity, but little is known about the effect of pregnancy on anti-TB drugs concentrations. A critical challenge to the global efforts to control the tuberculosis epidemic is the spreading of drug resistance to first-line tuberculosis drugs. The treatment of drug-resistant tuberculosis relies on both new anti-tuberculosis agents such as bedaquiline, delamanid, and pretomanid and repurposed drugs, such as linezolid and clofazimine. In this thesis, we employed nonlinear mixed-effects modelling to evaluate the pharmacokinetics of first-line tuberculosis drugs isoniazid, pyrazinamide, and ethambutol in pregnancy. We assessed the pharmacokinetics and pharmacodynamics of pretomanid, clofazimine, and linezolid in African tuberculosis patients. Reassuringly, we found no significant pregnancy effect on the exposure of these antituberculosis agents, thus confirming the suitability of current doses in pregnancy. For pretomanid, we found that in spite of exposure being reduced by 44% with rifampicin coadministration, the drug levels were within the efficacy range observed in previous trials, provided that pretomanid doses are administered with food. Clofazimine exposure was found to accumulate more slowly in women, an effect driven by sex-related differences in the proportion of body fat. We characterised the effect of clofazimine concentration on QT interval prolongation. We investigated alternative dosing regimen to optimise clofazimine treatment and suggested that a 2-week loading dose may support treatment shortening by safely enabling more rapid attainment of efficacy targets. For linezolid model, we characterised population pharmacokinetic parameters in African tuberculosis patients, assessed probability of target attainment and related toxicity following different doses administration. We showed that population modelling could maximize information from collected data, and have a significant impact on advancing patients care especially in places with limited resources.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/35668 |
| Date | 08 February 2022 |
| Creators | Abdelwahab, Mahmoud Tareq |
| Contributors | Denti, Paolo |
| Publisher | Faculty of Health Sciences, Division of Clinical Pharmacology |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, Doctoral, PhD |
| Format | application/pdf |
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