Modelling HIV-1 control and remission

Abstract

Remarkable advances are being made in developing interventions for eliciting long-term remission of HIV-1 infection. The success of these interventions will obviate the need for lifelong antiretroviral therapy, the current standard-of-care, and benefit the millions living today with HIV-1. Mathematical modelling has made significant contributions to these efforts. It has helped elucidate the possible mechanistic origins of natural and post-treatment control, deduced potential pathways of the loss of such control, quantified the effects of interventions, and developed frameworks for their rational optimization. Yet, several important questions remain, posing challenges to the translation of these promising interventions. Here, we survey the recent advances in the mathematical modelling of HIV-1 control and remission, highlight their contributions, and discuss potential avenues for future developments.

Fig. 1. Outcomes of HIV-1 infection and ART.

a The population prevalence of the three different outcomes, viz., chronic progressors (CP, magenta), natural controllers (NC, yellow) and post-treatment controllers (PTC, cyan)^,. b, c Cartoons showing viral load trajectories in the different scenarios.

Fig. 2. HIV-1 infection dynamics.

a A schematic of the within-host model encoded in Eqs. (1)–(5) (see text). b Numerical simulation of the model in (a). We let $f_1=\lambda -d_{T}T$ and $f_2=-d_{L}L$. Parameter values used^,: $\lambda$ = 10⁴ cells/mL-d, $\beta$ = 10^−5.5 mL/copies-d, $\epsilon$ = 0.99, $d_T$ = 0.01 /d, ${\varphi }_D$ = 0.93, ${\varphi }_L$ = 10⁻⁶, $a$ = 10⁻³/d, $d_I$ = 1.0/d, $d_D$ = 0.07/d, $d_L$ = 0.004/d, $p$ = 2000 copies/cells-d and $d_V$ = 23/d. Initiation conditions: $T\left(0\right)=\lambda /d_T$, $V\left(0\right)$ = 10⁻² copies/mL and no infected cells. The dashed line is the limit of detection of viral RNA (1 copy/mL) using ultrasensitive assays.

Fig. 3. Adaptive immune responses to HIV-1 infection.

Responses mediated by (a) CD8 T cells (red) and (b) antibodies (green), considered in within-host mathematical models. Exhaustion (black blunted line in (a)) is also considered in models.

Fig. 4. Viral evolution and immune escape.

Schematic showing mutation and recombination creating diverse viral genomes (middle), facilitating the selection of variants that escape antibody (left) and CD8 T cell (right) mediated immune pressures. While antibody escape mutations typically enable virus particles to resist clearance and other antibody-mediated immune functions, CD8 T cell escape mutations prevent the infected cell from being identified by CD8 T cells and thus limit their effector responses.