Bridging the gap between HIV epidemiology and antiretroviral resistance evolution: Modelling the spread of resistance in South Africa

Abstract

The scale-up of antiretroviral therapy (ART) in South Africa substantially reduced AIDS-related deaths and new HIV infections. However, its success is threatened by the emergence of resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI). The MARISA (Modelling Antiretroviral drug Resistance In South Africa) model presented here aims at investigating the time trends and factors driving NNRTI resistance in South Africa. MARISA is a compartmental model that includes the key aspects of the local HIV epidemic: continuum of care, disease progression, and gender. The dynamics of NNRTI resistance emergence and transmission are then added to this framework. Model parameters are informed using data from HIV cohorts participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) and literature estimates, or fitted to UNAIDS estimates. Using this novel approach of triangulating clinical and resistance data from various sources, MARISA reproduces the time trends of HIV in South Africa in 2005-2016, with a decrease in new infections, undiagnosed individuals, and AIDS-related deaths. MARISA captures the dynamics of the spread of NNRTI resistance: high levels of acquired drug resistance (ADR, in 83% of first-line treatment failures in 2016), and increasing transmitted drug resistance (TDR, in 8.1% of ART initiators in 2016). Simulation of counter-factual scenarios reflecting alternative public health policies shows that increasing treatment coverage would have resulted in fewer new infections and deaths, at the cost of higher TDR (11.6% in 2016 for doubling the treatment rate). Conversely, improving switching to second-line treatment would have led to lower TDR (6.5% in 2016 for doubling the switching rate) and fewer new infections and deaths. Implementing drug resistance testing would have had little impact. The rapid ART scale-up and inadequate switching to second-line treatment were the key drivers of the spread of NNRTI resistance in South Africa. However, even though some interventions could have substantially reduced the level of NNRTI resistance, no policy including NNRTI-based first line regimens could have prevented this spread. Thus, by combining epidemiological data on HIV in South Africa with biological data on resistance evolution, our modelling approach identified key factors driving NNRTI resistance, highlighting the need of alternative first-line regimens.

Fig 1. Compartmental model.

Three of the four dimensions are represented: 1) care stages (vertically), 2) disease progression (horizontally, stratified in 4 CD4 counts strata), 3) NNRTI resistance (represented by the two overlapped layers). For sake of clarity, arrows representing treatment interruption are not displayed. Red arrows represent acquisition of NNRTI resistance, while blue arrows represent reversion to wild-type HIV-strain.

Fig 2. Best fit of the model.

The plots a, b, c and d correspond to the four outcomes used during the fitting procedure: A) the number of newly infected per year, B) the total number of undiagnosed individuals at each year, C) the number of AIDS-related deaths per year and D) the percentage of infected individuals that are on ART. NNRTI ADR and TDR levels are displayed in E and, F and G respectively, and are not used to fit the model. Lines correspond to model output and circles to Thembisa estimates (in red) or to results from cross-sectional studies (in blue, see Table 6 in S1 File). Grey shades correspond to 100% sensitivity ranges. See Table 2 for more details.

Fig 3. Counterfactual scenario that investigates the impact of increased treatment rate.

Simulations of the MARISA model from 2005 to 2016 under the scenarios where the treatment rate is increased by 2, 3 and 5, represented respectively by the blue, red and yellow curves. Simulations of the baseline model are represented in black. The following HIV outcomes are displayed: A) the number of newly infected per year, B) the total number of undiagnosed individuals at each year, C) the number of AIDS-related deaths per year and D) the percentage of infected individuals that are on ART, E) level NNRTI ADR and F) level of NNRTI TDR. Different colours correspond to different rates of starting treatment, where the rates are expressed as multiple of the rate in the standard model. The coloured circles and vertical lines at the right of each sub-figure correspond to the point estimates and 100% sensitivity ranges in 2016, respectively.

Fig 4. Counterfactual scenario that investigates the impact of increased switching rate to second line regimen.

Simulations of the MARISA model from 2005 to 2016 under the scenarios where the switching rate to second-line regimen ${\gamma }_{F_1\to T_2}$ is increased by factor 2, 5 and 10, represented respectively by the blue, red and yellow curves. In the baseline simulations represented by the black curves, a switching rate ${\gamma }_{F_1\to T_2}$ of 1/2.9 years⁻¹ is assumed for individuals with CD4<200 copies/μl. The following HIV outcomes are displayed: A) the number of newly infected per year, B) the total number of undiagnosed individuals at each year, C) the number of AIDS-related deaths per year and D) the percentage of infected individuals that are on ART, E) level NNRTI ADR and F) level of NNRTI TDR. Different colours correspond to different rates of starting treatment, where the rates are expressed as multiple of the rate in the standard model. The coloured circles and vertical lines at the right of each sub-figure correspond to the point estimates and 100% sensitivity ranges in 2016, respectively.