Los Alamos National Laboratory, Los Alamos, NM, USA
"Stochastic viral dynamics modeling of time series from HIV-1 cure experiments in macaque and mouse models"
Conventional wisdom has it that the only way to control HIV-1 infection is lifelong antiretroviral drug therapy (ART). However recent observations indicate that functional cure, i.e., control of HIV infection in absence of ART, is possible. Treatment strategies to achieve functional cure have therefore become a very active area of research. The efforts to develop a functional cure for HIV-1 must overcome the persistence of the latent reservoir of infected CD4+ T cells. These cells contain integrated HIV-1 DNA and are both long-lived and can re-active producing new viral particles that re-establish infection after ART interruption. All progress towards a HIV-1 cure must contend with this problem. Proposed strategies for curing HIV-1 include methods to reduce the size of the reservoir, or induce immune responses that can prevent viral rebound after ART interruption. Such novel treatments are typically tested in the macaque model or in humanized mice. We analyze data from two such studies: a macaque model to assess the effect of early ART initiation, which may limit latent reservoir size, and a CD4+ T-cell xenograft mouse model to assess the effect of immuno-therapies targeted at HIV-specific CD8+ T cells (HSTs). Stochasticity plays a major role in both these experiments. The formation of the latent reservoir and reactivation from this reservoir leading to viral rebound are both thought to be highly stochastic processes. In the mouse model, the virus escapes from the CD8+ T-cell immune response in a largely unpredictable manner, despite the fact that biological variation between repeated experiments is reduced to a minimum. We are therefore motivated to develop stochastic viral dynamics models to describe the data, rather than the standard deterministic viral dynamics model, and further, we estimate parameters using Sequential Monte Carlo methods for panel data. This allows us to integrate data from repeated experiments in our inference. In our investigation of early SIV infection with macaque data, we compare different models of reservoir formation. We show that a model in which reservoir establishment saturates at high viral loads, can reconcile early establishment with the observed distribution of rebound times. In our investigation of immuno-therapies with mouse data, we succeed in disentangling viral rebound due to escape from the HST response, from rebound due to typical expansion and contraction dynamics of the HSTs.
Virginia Tech, Department of Mathematics, Blacksburg, VA, USA
"Understanding the antiviral effects of RNAi-based therapy on chronic hepatitis B infection"
Reaching functional cure following chronic hepatitis B virus infections is hindered difficult by the presence of large numbers of HBsAg in the blood of infected patients. Therapies with the RNA interfer- ence drug ARC-520, which silence viral translation, together with daily administration of the nucleoside analogue drug entecavir have showed reduction in the overall levels of serum HBsAg in HBeAg-positive, treatment naive patients. Understanding the relative effects of ARC-520 alone, and in combination with entecavir, is particularly important in informing the development of new generation antiHBsAg drugs. A mathematical model describing the mechanistic interactions between HBV DNA, HBsAg, and HBeAg in the presence of ARC-520 and entecavir has been developed. We fitted the model to patient data and investigated the long term dynamics of the virus and viral protein titers under entecavir alone and under combination therapy. We run in silico boosting experiments and used them to determine the tradeoff between viral protein decay and drug induced toxicity. Such results can inform policy.
Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
"How do CD8+ T cells control HIV infection?"
Human immunodeficiency virus (HIV) infection is still one of the most important causes of morbidity and mortality in the world, with a disproportionate human and economic burden especially in poorer countries. Despite many years of intense research, an aspect that still is not well understood is what immune mechanisms control the viral load during the prolonged asymptomatic stage of infection. Because CD8+ T cells have been implicated in this control by multiple lines of evidence, there has been a focus on understanding the potential mechanisms of action of this immune effector population. One type of experiment used to this end has been depleting these cells with monoclonal antibodies in the SIV-macaque model and then studying the effect of that depletion on the viral dynamics. These experiments generated controversial results, with dynamical models developed to help interpret these data leading to conflicting conclusions. We propose a new explanation for these results and provide both new experimental data and modeling evidence that helps to reconcile previous observations. In this hypothesis the main effect of CD8+ T cells occurs before viral integration.
University of New South Wales, Sydney
"Heterogeneity in the risk of latent malaria parasite reactivation explains the timing and pattern of infection recurrences in (Plasmodium vivax) malaria endemic settings"
The parasite Plasmodium vivax causes both blood-stage malaria infection and the formation of latent liver-stage parasites called hypnozoites. These recurrence of infection through hypnozoite activation is a major contributor to the total new infections in Plasmodium vivax endemic regions. After being treated for a single infection it is well known that some individuals will experience a second recurrence very rapidly while others will not experience a recurrence for some time. The mechanisms governing the ‘schedule’ of reactivation are not completely understood. A variety of conceptual models have been proposed, including a ‘biological clock’ mechanism, induction by external factors such as fever, or simply random reactivation of hypnozoites. In addition to these models, we propose an alternative explanation that there is heterogeneity in the risk of malaria relapse within the population. To explore the mechanisms governing P. vivax recurrence, we constructed differential equation models of each of the above conceptual models of hypnozoite reactivation. The models were compared, through fitting and simulation, to previously published time-to-infection data from a malaria endemic region following 1299 people for about one year. The data used in our study provided a powerful opportunity to study the mechanisms underlying P. vivax relapse because rather than including only a single infection event for each individual, multiple occurrences were recorded in many individuals over a one-year follow-up. However, the multiple measurements within each individual added complexity to fitting our custom time-to-infection model and required us to build these models within a mixed-effects type model framework. Our results show that the models with population heterogeneity in the reactivation rate provided the simplest and best explanation of the data and unlike the other conceptual models, heterogeneity could explain the observed patterns of P. vivax recurrences between individuals.