Advances in the mathematical modeling of posttreatment control of HIV-1.

PURPOSE OF REVIEW

Several new intervention strategies have shown significant improvements over antiretroviral therapy (ART) in eliciting lasting posttreatment control (PTC) of HIV-1. Advances in mathematical modelling have offered mechanistic insights into PTC and the workings of these interventions. We review these advances.

RECENT FINDINGS

Broadly neutralizing antibody (bNAb)-based therapies have shown large increases over ART in the frequency and the duration of PTC elicited. Early viral dynamics models of PTC with ART have been advanced to elucidate the underlying mechanisms, including the role of CD8+ T cells. These models characterize PTC as an alternative set-point, with low viral load, and predict routes to achieving it. Large-scale omic datasets have offered new insights into viral and host factors associated with PTC. Correspondingly, new classes of models, including those using learning techniques, have helped exploit these datasets and deduce causal links underlying the associations. Models have also offered insights into therapies that either target the proviral reservoir, modulate immune responses, or both, assessing their translatability.

SUMMARY

Advances in mathematical modeling have helped better characterize PTC, elucidated and quantified mechanisms with which interventions elicit it, and informed translational efforts.