Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations

dc.contributor.advisorDenti, Paolo
dc.contributor.authorGausi, Kamunkhwala
dc.date.accessioned2022-08-30T10:06:37Z
dc.date.available2022-08-30T10:06:37Z
dc.date.issued2022
dc.date.updated2022-08-26T07:16:53Z
dc.description.abstractThe global scale-up of tuberculosis treatment administered with antiretroviral therapy (ART) is the primary contributor to the 11 million averted deaths among individuals living with HIV observed between 2000 and 2019 in adults and children. Unfortunately, not all patients in need could fully benefit from these recent improvements in treatment because neglected populations are often excluded from clinical trials, including pregnant and breastfeeding women, children, adolescents, those with co-morbidities requiring additional drug treatments, and those with drug-resistant strains. This leaves many unanswered questions surrounding the management of TB, HIV, and TB/HIV in these vulnerable subpopulations. In this thesis, we utilise population pharmacokinetics and pharmacodynamic modelling to improve TB and HIV treatment in neglected populations using data from patients with TB or/and HIV. We analyse the pharmacogenomics, pharmacokinetics, and drug-drug interaction of efavirenz, isoniazid, and bedaquiline in pregnant women and characterise the pharmacokinetics and pharmacodynamics of high dose isoniazid in adults with multidrugresistant tuberculosis. We found that isoniazid and efavirenz exposures were reduced during pregnancy, but the main determinants of drug concentration were N-acetyltransferase 2 and CYP2B6 genotypes, which resulted in a 5-fold difference for both drugs between rapid and slow metabolisers. Bedaquiline exposures were lower during both postpartum and antepartum compared to historical data in non-pregnant patients. For high dose isoniazid, we observed markedly lower isoniazid exposures in participants on combination MDR-TB treatment compared to monotherapy and identified saturable kinetics at doses >10 mg/kg. We suggest that dosing isoniazid based on N-acetyltransferase 2 acetylator status might help patients attain effective exposures against inhA-mutated isolates.
dc.identifier.apacitationGausi, K. (2022). <i>Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations</i>. (). ,Faculty of Health Sciences ,Department of Medicine. Retrieved from http://hdl.handle.net/11427/36776en_ZA
dc.identifier.chicagocitationGausi, Kamunkhwala. <i>"Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations."</i> ., ,Faculty of Health Sciences ,Department of Medicine, 2022. http://hdl.handle.net/11427/36776en_ZA
dc.identifier.citationGausi, K. 2022. Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations. . ,Faculty of Health Sciences ,Department of Medicine. http://hdl.handle.net/11427/36776en_ZA
dc.identifier.ris TY - Doctoral Thesis AU - Gausi, Kamunkhwala AB - The global scale-up of tuberculosis treatment administered with antiretroviral therapy (ART) is the primary contributor to the 11 million averted deaths among individuals living with HIV observed between 2000 and 2019 in adults and children. Unfortunately, not all patients in need could fully benefit from these recent improvements in treatment because neglected populations are often excluded from clinical trials, including pregnant and breastfeeding women, children, adolescents, those with co-morbidities requiring additional drug treatments, and those with drug-resistant strains. This leaves many unanswered questions surrounding the management of TB, HIV, and TB/HIV in these vulnerable subpopulations. In this thesis, we utilise population pharmacokinetics and pharmacodynamic modelling to improve TB and HIV treatment in neglected populations using data from patients with TB or/and HIV. We analyse the pharmacogenomics, pharmacokinetics, and drug-drug interaction of efavirenz, isoniazid, and bedaquiline in pregnant women and characterise the pharmacokinetics and pharmacodynamics of high dose isoniazid in adults with multidrugresistant tuberculosis. We found that isoniazid and efavirenz exposures were reduced during pregnancy, but the main determinants of drug concentration were N-acetyltransferase 2 and CYP2B6 genotypes, which resulted in a 5-fold difference for both drugs between rapid and slow metabolisers. Bedaquiline exposures were lower during both postpartum and antepartum compared to historical data in non-pregnant patients. For high dose isoniazid, we observed markedly lower isoniazid exposures in participants on combination MDR-TB treatment compared to monotherapy and identified saturable kinetics at doses >10 mg/kg. We suggest that dosing isoniazid based on N-acetyltransferase 2 acetylator status might help patients attain effective exposures against inhA-mutated isolates. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Clinical Pharmacology LK - https://open.uct.ac.za PY - 2022 T1 - Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations TI - Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations UR - http://hdl.handle.net/11427/36776 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/36776
dc.identifier.vancouvercitationGausi K. Pharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations. []. ,Faculty of Health Sciences ,Department of Medicine, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/36776en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Medicine
dc.publisher.facultyFaculty of Health Sciences
dc.subjectClinical Pharmacology
dc.titlePharmacometric modelling to inform and improve TB and HIV treatment: Focus on drug-drug interactions and neglected populations
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationlevelPhD
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