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Browsing by Author "Singh, Avashna."

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    Characterizing protease inhibitor failure in HIV-1 subtype C, using ultra deep pyro-sequencing and homology modelling.
    (2015) Singh, Avashna.; Gordon, Michelle Lucille.
    The extensive roll-out of combination antiretroviral therapy (cART) has significantly improved the life expectancy for HIV-1 infected individuals in South Africa. Despite the inclusion of potent Protease Inhibitors (PIs) in second-line cART, many patients still fail treatment. The extent to which PI resistance contributes to treatment failure is not completely clear. In this study we report the prevalence of PI mutations amongst individuals failing a second-line Lopinavir (LPV/r) inclusive regimen. We also investigated if low frequency minority variants at LPV/r failure influence Darunavir (DRV/r) failure in a subset of patients using Ultra Deep Pyro-sequencing. Structural changes at DRV/r failure were investigated using Homology modeling. Models were constructed using the SWISS-MODEL webserver and visualized in Chimera v1.8.1. Darunavir was docked into each of the structures using the CLC Drug Discovery workbench ™ and Molecular Dynamics simulations was performed using the AMBER12 package. Our study reports a 24% prevalence of PI resistance mutations, slightly higher than other studies. A distinct pattern of PI resistance mutations was found: M46I+I54V+L76V+V82A, present in 13/37 (35%) of those with PI mutations. Darunavir resistance mutations detected following DRV/r failure included V11I, V32I, L33F and I54L. There were no minority variants detected at LPV/r failure that could have influenced DRV/r failure. Distinct conformational changes were evident in both the LPV/r-resistant and DRV/r-resistant model. Molecular docking showed that the inhibitory potency of DRV was lowered in the mutated DRV/r-resistant model and to a lesser extent in the LPV/r-resistant model. These results show that resistance mutations greatly contribute to DRV drug susceptibility. This work will contribute to the clinical management of patients failing treatment and will also assist in the design of new and improved ARVs.