Browsing by Author "Van der Berg, Anden"

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    Investigating the effects of treatment in HIV disease models
    (Stellenbosch : Stellenbosch University, 2021-12) Van der Berg, Anden; Van Niekerk, David Douglas; Snoep, Jacob Leendert; Stellenbosch University. Faculty of Science. Dept. of Biochemistry.
    ENGLISH ABSTRACT: HIV/AIDS and disease response to antiretroviral (ARV ) drugs are of major importance to the developing world, and the disease remains a burden on society, as viral replication still needs to be controlled continually in persons infected with HIV. The ever-increasing prevalence of drug-resistant viral strains and the latent reservoir which harbours dor- mant virus also remain barriers to a cure. To overcome these barriers, novel ways of treating the disease and new tools for effective and efficient drug development are re- quired. The use of mathematical models of disease and drug treatment continues to grow and remains an essential tool in drug development and the search for a cure. In this study, combined HIV disease-PKPD models are created and tested for their abil- ity to simulate real world patient data. First, independent mathematical models of HIV disease dynamics and ARV pharmacology are reproduced from literature. The effect of patient variability on simulation results is tested using Monte Carlo simulations, in which parameters are varied within biologically relevant ranges. The HIV disease models are then linked to PK and PD models of the currently prescribed ARVs. Monte Carlo sim- ulations are used to examine the effect that heterogeneity, model structure, and model assumptions have in the newly linked models. The viral load and CD4+ T-cell count predictions made by the combined models are compared to clinical patient data from the Western Cape, South Africa. Analysis of the combined models show that model struc- ture has to include latently infected cells and drug-resistant viral strains to be able to accurately predict the disease progression of HIV. Models need to incorporate the mechanisms that affect disease outcome. In the context of HIV, this may include drug-resistant strains and the effect of long-lived latently infected cells. Model predictions can be improved by including these mechanisms which have an impact on disease outcome and by considering longitudinal patient datasets. Such continual improvements will aid in making models powerful diagnostic tools.