Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Suspension of therapy is followed by rebound of viral loads to high, pre-therapy levels. However, there is significant heterogeneity in speed of rebound, with some rebounds occurring within days, weeks, or sometimes years. We present a stochastic mathematical model to gain insight into these post-treatment dynamics, specifically characterizing the dynamics of short term viral rebounds (≤ 60 days). Li et al. (2016) report that the size of the expressed HIV reservoir, i.e., cell-associated HIV RNA levels, and drug regimen correlate with the time between ART suspension and viral rebound to detectable levels. We incorporate this information and viral rebound times to parametrize our model. We then investigate insights offered by our model into the underlying dynamics of the latent reservoir. In particular, we refine previous estimates of viral recrudescence after ART interruption by accounting for heterogeneity in infection rebound dynamics, and determine a recrudescence rate of once every 2-4 days. Our parametrized model can be used to aid in design of clinical trials to study viral dynamics following analytic treatment interruption. We show how to derive informative personalized testing frequencies from our model and offer a proof-of-concept example. Our results represent first steps towards a model that can make predictions on a person living with HIV (PLWH)’s rebound time distribution based on biomarkers, and help identify PLWH with long viral rebound delays.Author summary: Antiretroviral therapy (ART) effectively controls HIV infection, holding HIV viral loads to levels undetectable by commercial assays. Therapy interruption is followed by rebound of viral loads to high, pre-therapy levels, but there is significant heterogeneity in the timing of the rebound to those high levels. Some rebounds occur within days, weeks, or even, rarely, years. Here we develop a mathematical model to characterize rebounds occurring within two months of treatment interruption. Li et al. (2016) report biological markers that correlate with the time between ART interruption and viral rebound. We incorporate this information to parametrize our model so that our model can make predictions on time to rebound tailored to the individual undergoing ATI. Our parametrized model can aid in design of clinical trials to study infection dynamics following treatment interruption. We also use our model to gain insight into the underlying within-host viral dynamics. For example, we refine previous estimates of viral recrudescence after ART interruption and determine a recrudescence rate of once every 2-4 days. Our results represent first steps towards a model that can make predictions on an person living with HIV’s rebound time based on personal biomarkers, and help identify patients with long viral rebound delays.