Timing of treatment affects early post-rebound viral levels.

A. Example of viral load trajectories for animals treated on days 4 (blue), 6 (green), 27 (red) from Cohort 1. Vertical dashed lines indicate the timing of antiretroviral treatment initiation. Shaded area indicates time of early post-treatment setpoint viral load (days 30–60 post-rebound). B, C. The relationship between day of anti-retroviral treatment initiation and setpoint viral load (n = 122) (B) or peak viral load (n = 124) (C) after treatment interruption. Black lines show the best fit of the nonlinear regression (see methods and Formula (2)) that estimates the early decrease, inflection point, and later increase in viral levels. When fitting model (2), we incorporate the data for set point and peak viral load at primary infection (Primary) by defining the timing of treatment to be 0 days for these data points. The best-fit parameters are in the Table B in S1 Text. The SIV inoculum, duration of treatment, and other interventions are shown in Table A in S1 Text. # Time-weighted setpoint viral loads were averaged over shorter time intervals for some animals (see Table A in S1 Text).</p

Longer treatment is associated with increased post-rebound setpoint viral level.

Setpoint viral load from different cohorts treated for different amounts of time significantly correlates with time on treatment (linear mixed effect model slope = 0.011 log10 copies/ml per day, p<0.0001).</p

Supplementary text.

Summary of the experimental studies and additional analysis. (PDF)</p

Duration of post-rebound viral control to <10,000 copies/ml.

In order to compare the duration of post-rebound control, we compared the proportion of each cohort that maintained post-rebound viral loads below 10,000 copies per ml at different times after ATI. The first 30 days after detection of viral rebound are ignored to avoid the initial post-rebound peak of viral load in the analysis of the duration of viral control (shaded grey). A. The proportion of animals maintaining viral control over time post-rebound is higher in groups treated around the optimal time, however the difference is not significant when comparing three groups together (p-values for the log-rank test are shown in the figures). Coloured stars indicate groups where all animals had viral loads greater than 10,000 copies per ml at day 30 post detection. B. Animals that have a low peak of the virus during early rebound are more likely to maintain low viral control over time. C. Having a low viral growth rate during post-treatment rebound is also associated with longer-term control of post-rebound viral loads.</p

Association between CD8+ T cell responses on ART and post-rebound setpoint viral load in a subset of animals in which CD8 T cell responses were measured.

A. In Cohort 1, higher CD8+ T cell responses before ATI were associated with lower post-rebound setpoint viral loads (r = -0.82, p = 0.0031). B, C. However, in Cohort 2 the frequency of SIV-specific CD8+ T cells (measured between days 511 and 609) was not significantly associated with post-rebound setpoint viral levels.</p

Different immune interventions may be required depending on timing of ART initiation and the duration of treatment.

One approach to long-term, ART-free control of HIV is to boost immune responsiveness to infection during ART. However, different therapeutic interventions may be effective depending on the timing of ART initiation. For example, for animals treated at an early stage of infection (red shaded area), increased priming of the response or measures to boost immunity may be effective. However, for animals treated later in infection (green shaded area), it may be necessary to reverse immune exhaustion to improve post-rebound setpoint viral levels. The mechanisms that lead to increase of post-rebound setpoint after a prolonged treatment (blue shaded area) are unclear and can be explained by either declining levels of immune memory or prolonged exposure to low levels of viral antigen that drives immune exhaustion. Thus, the interventions for boosting immune control may differ, depending on the underlying mechanisms. The surface depicted here is the best-fit of Eq (3) to the data used in this study. See Fig G in S1 Text for overlay of data points on this best-fit curve.</p

Dynamics of viral load by cohorts.

Thick coloured lines are the group median viral loads, thin lines of the matched colour are the individual viral loads of monkeys from the same group. Vertical dashed lines indicate the timing of antiretroviral treatment initiation of monkeys in the group with respectively coloured lines.</p

A short summary of the experimental studies used in the current analysis.

The first column is the identifier as it appears in the text. The more detailed parameters of the studies are presented in the Table A in S1 Text.</p

Impact of protective MHC Mamu-A*01, B*08, and B*17 on the setpoint and peak viral load.

A. Fitting the model defined by Formula (2) to data from macaques with any protective MHC-1 alleles A*01, B*08, B*17 (purple points and curves) and macaques without these known protective alleles (blues points and curves). The curves are allowed to differ by only one parameter (the viral load at day 0 post infection–b0), with the rest of the parameters fitted simultaneously to both datasets. For the data on post-rebound setpoint viral levels, the model allowing different viral levels for animals with protective alleles fits better than the model with all parameters the same. The setpoint viral load is lower in macaques with protective alleles by 0.65 log10 copies/ml (F-test’s p-value = 0.0002, best-fit parameters are in Table F in S1 Text. The best-fit parameters for the model with single b0 are in Table B in S1 Text). B. For data on post-rebound peak viral levels, the best fit model is one in which there is no difference between groups with and without protective alleles (indicated as grey curve) (F-test p-value = 0.61. Best-fit parameters of the model with different b0 are presented in Table F in S1 Text. Best-fit parameters for the model with a single b0 are in Table B in S1 Text). This suggests that the post-rebound peak viral load is not affected by the presence of protective MHC-1 Mamu-A*01, B*08, and B*17 alleles.</p

Determinants of post-treatment rebound setpoint viral load.

We analysed animals from the three cohorts to understand factors associated with the post-rebound setpoint viral level. Viral load at treatment (A) and peak viral load before treatment (B) where poorly predictive of post-rebound setpoint viral load when data from all treatment times was combined. However, when we divided the animals into those treated before day 20 and after day 20, clear patterns emerged. (C,D) For animals treated before day 20, the viral load at treatment (C) does not predict setpoint viral load at rebound. Instead, the day of treatment (D) is a significant predictor of the setpoint viral load. (E,F). In animals treated after 20 days post-infection, viral load at treatment is a good predictor of the rebound setpoint viral load (E), while the rebound peak viral load is only weakly associated with rebound setpoint (F).</p