Modeling Viral and T Lymphocyte Dynamics

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SUMMARY Multiple recent studies have provided proof-of-concept that a “functional cure” of HIV-1 infection, i.e. long-term control of HIV without continued treatment, is achievable. The VISCONTI study identified 14 HIV+ patients, who received antiretroviral treatment (ART) during primary HIV-1 infection, and maintained post-treatment control (PTC) of their virus below the limit of detection for a median of 89 months after stopping therapy. The CHAMP (Control of HIV after Antiretroviral Medication Pause) study has identified 67 post-treatment controllers from 14 treatment interruption studies involving more than 700 participants. To determine why some individuals control HIV to undetectable or low levels after treatment discontinuation, we need a better understanding of the factors that lead to establishment of viral reservoirs, that determine its size, the dynamics of its maintenance and its reactivation possibly leading to viral rebound after treatment cessation. Here we propose to develop a set of new models to help understand the factors that led to functional cure in the studies mentioned above and to understand more generally how functional cure can be achieved. We will collaborate with leading experimental scientists, who will provide novel datasets allowing us to fulfil the following specific aims. Aim 1. To understand the mechanism of HIV latent reservoir establishment and the factors determining the rate of reservoir seeding during acute infection. We will develop mechanistic models of early reservoir establishment. We will test these models against rich datasets collected by collaborators and estimate key parameter values to accurately describe the dynamics of reservoir establishment. With the insights gained, we will extend the model to interpret recent data39 on the seeding, turnover and the genetic composition of the reservoir. Aim 2. To understand in quantitative detail the factors that determine the duration of post-treatment control after ART interruption. We will study new models that account for patient specific factors such as the ART regime and the level of cell-associated RNA at the time of ATI in predicting time to viral rebound after ATI. When the time to rebound is long, we aim to elucidate new factors such as a time-dependent rate of reservoir reactivation or immune control that lead to prolonged PTC. Aim 3. Using insights gained from mathematical modeling to propel the cure agenda for HBV. We will leverage previous modeling successes of HIV and HCV infection to develop a new generation of models of HBV infection and study the effects of different therapies singly and in combination.