Ryan Zurakowski

HIV-1 persistence in CD4 + T cells with stem cell-like properties

Cellular HIV-1 reservoirs that persist despite antiretroviral treatment are incompletely defined. We show that during suppressive antiretroviral therapy, CD4+ T memory stem cells (TSCM cells) harbor high per-cell levels of HIV-1 DNA and make increasing contributions to the total viral CD4+ T cell reservoir over time. Moreover, we conducted phylogenetic studies that suggested long-term persistence of viral quasispecies in CD4+ TSCM cells. Thus, HIV-1 may exploit the stem cell characteristics of cellular immune memory to promote long-term viral persistence.

Long-Term Antiretroviral Treatment Initiated at Primary HIV-1 Infection Affects the Size, Composition, and Decay Kinetics of the Reservoir of HIV-1-Infected CD4 T Cells

ABSTRACT Initiation of antiretroviral therapy during the earliest stages of HIV-1 infection may limit the seeding of a long-lasting viral reservoir, but long-term effects of early antiretroviral treatment initiation remain unknown. Here, we analyzed immunological and virological characteristics of nine patients who started antiretroviral therapy at primary HIV-1 infection and remained on suppressive treatment for >10 years; patients with similar treatment duration but initiation of suppressive therapy during chronic HIV-1 infection served as controls. We observed that independently of the timing of treatment initiation, HIV-1 DNA in CD4 T cells decayed primarily during the initial 3 to 4 years of treatment. However, in patients who started antiretroviral therapy in early infection, this decay occurred faster and was more pronounced, leading to substantially lower levels of cell-associated HIV-1 DNA after long-term treatment. Despite this smaller size, the viral CD4 T cell reservoir in persons with early treatment initiation consisted more dominantly of the long-lasting central-memory and T memory stem cells. HIV-1-specific T cell responses remained continuously detectable during antiretroviral therapy, independently of the timing of treatment initiation. Together, these data suggest that early HIV-1 treatment initiation, even when continued for >10 years, is unlikely to lead to viral eradication, but the presence of low viral reservoirs and durable HIV-1 T cell responses may make such patients good candidates for future interventional studies aiming at HIV-1 eradication and cure. IMPORTANCE Antiretroviral therapy can effectively suppress HIV-1 replication to undetectable levels; however, HIV-1 can persist despite treatment, and viral replication rapidly rebounds when treatment is discontinued. This is mainly due to the presence of latently infected CD4 T cells, which are not susceptible to antiretroviral drugs. Starting treatment in the earliest stages of HIV-1 infection can limit the number of these latently infected cells, raising the possibility that these viral reservoirs are naturally eliminated if suppressive antiretroviral treatment is continued for extremely long periods of time. Here, we analyzed nine patients who started on antiretroviral therapy within the earliest weeks of the disease and continued treatment for more than 10 years. Our data show that early treatment accelerated the decay of infected CD4 T cells and led to very low residual levels of detectable HIV-1 after long-term therapy, levels that were otherwise detectable in patients who are able to maintain a spontaneous, drug-free control of HIV-1 replication. Thus, long-term antiretroviral treatment started during early infection cannot eliminate HIV-1, but the reduced reservoirs of HIV-1 infected cells in such patients may increase their chances to respond to clinical interventions aiming at inducing a drug-free remission of HIV-1 infection.

Enhancing immune response to HIV infection using MPC-based treatment scheduling

icant drop in deaths due to opportunistic infections, it Using recently developed models of the interaction of the human immune system and the Human Immunodeficiency Virus (HIV), we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules. These schedules use interruptions of Highly Active AntiRetroviral Therapy (HAART) to induce immune control of the virus without the need for continued treatment, as suggested by the models. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method and show simulation results, and discuss future work necessary to implement the method.

HIV Model Parameter Estimates from Interruption Trial Data including Drug Efficacy and Reservoir Dynamics

Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.

Modelling HIV-1 2-LTR dynamics following raltegravir intensification

A model of reservoir activation and viral replication is introduced accounting for the production of 2-LTR HIV-1 DNA circles following antiviral intensification with the HIV integrase inhibitor raltegravir, considering contributions of de novo infection events and exogenous sources of infected cells, including quiescent infected cell activation. The model shows that a monotonic increase in measured 2-LTR concentration post intensification is consistent with limited de novo infection primarily maintained by sources of infected cells unaffected by raltegravir, such as quiescent cell activation, while a transient increase in measured 2-LTR concentration is consistent with significant levels of efficient (R0 > 1) de novo infection. The model is validated against patient data from the INTEGRAL study and is shown to have a statistically significant fit relative to the null hypothesis of random measurement variation about a mean. We obtain estimates and confidence intervals for the model parameters, including 2-LTR half-life. Seven of the 13 patients with detectable 2-LTR concentrations from the INTEGRAL study have measured 2-LTR dynamics consistent with significant levels of efficient replication of the virus prior to treatment intensification.

A new strategy to decrease risk of resistance emerging during therapy switching in HIV treatment

Although highly active antiretroviral therapy (HAART) provides a powerful strategy for HIV treatment, it has been shown that HAART cannot eradicate all viruses in patients because of the existence of long-term reservoir. With the use of HAART, resistant strains develop and become the dominant species. Because the number of independent treatment regimens is limited, once resistance to all available drug classes arises, the patient will die. In this paper, we propose a drug switching strategy to minimize resistance risk of resistance and preserve long-term control of the HIV infection based on a simple model of HIV infection with persistent viral reservoirs.

Nonlinear observer output-feedback MPC treatment scheduling for HIV

BackgroundMathematical models of the immune response to the Human Immunodeficiency Virus demonstrate the potential for dynamic schedules of Highly Active Anti-Retroviral Therapy to enhance Cytotoxic Lymphocyte-mediated control of HIV infection.MethodsIn previous work we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated output-feedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We use Monte-Carlo approaches to test robustness of the algorithm.ResultsThe nonlinear observer shows robust state tracking while preserving state positivity both for continuous and discrete measurements. The integrated output-feedback MPC algorithm stabilizes the desired steady-state. Monte-Carlo testing shows significant robustness to modeling error, with 90% success rates in stabilizing the desired steady-state with 15% variance from nominal on all model parameters.ConclusionsThe possibility of enhancing immune responsiveness to HIV through dynamic scheduling of treatment is exciting. Output-feedback Model Predictive Control is uniquely well-suited to solutions of these types of problems. The unique constraints of state positivity and very slow sampling are addressable by using a special-purpose nonlinear state estimator, as described in this paper. This shows the possibility of using output-feedback MPC-based algorithms for this purpose.

A generalized multi-strain model of HIV evolution with implications for drug-resistance management

Since 1996, the National Institutes of Health and other organizations have recommended offering Highly Active Antiretroviral Therapy (HAART) to all patients infected with HIV. Although HAART provides a powerful strategy for HIV treatment, it does not prevent completely the development of multi-drug resistant strains, and drug resistance is the primary reason for treatment failure. A better control of drug-resistance risk is critical for the success of long-term antiviral therapy in HIV patients. Recent research focuses on how to develop new drugs, but little has been done to control resistance risk by using an appropriate treatment regime. In this paper, we propose a generalized multi-strain model of HIV evolution with viral mutations. Based on this model, we suggest a drug switching strategy to minimize resistance risk and preserve long-term control of the HIV infection for the case in which the patient only has one kind of drug-resistance virus. Though simulations, this model can also be used for detecting and minimizing the resistance risk for the patients who develops multiple drug-regimen resistance.

Treatment interruptions to decrease risk of resistance emerging during therapy switching in HIV treatment

The development of multi-drug resistant strains of HIV remains the primary reason for treatment failure and progression to AIDS in the United States. The failure of a particular multi-drug regimen necessitates a switch to a new multi-drug regimen. We use a simple model of the interaction of resistant strains to show that the transition to the new regimen involves a significant risk of strains resistant to the new regimen emerging, and that treatment interruptions using the failing regimen can be used to decrease this risk.

Nonlinear estimators for censored data: A comparison of the EKF, the UKF and the Tobit Kalman filter

Measurement censoring, or Tobit model censoring, is common in many engineering applications. It arises from limits in sensor dynamic range, and may be exacerbated by poor calibration of sensors. Censoring is often referred to as a clipped measurement or limit-of-detection discontinuity, and is represented as a piecewise-linear transform of the output variable. The slope of the piecewise-linear transform is zero in the censored region. This form of nonlinearity presents significant challenges when a nonlinear approximation to the Kalman filter is to be used as an estimator. The Tobit Kalman filter is a new method that is a computationally efficient, unbiased estimator for linear dynamical systems with censored output. In this paper, we use Monte Carlo methods to compare the performance of the Tobit Kalman Filter to the performance of the Extended Kalman Filter and the Unscented Kalman Filter. We show that the Tobit Kalman Filter reliably provides accurate estimates of the state and state error covariance with censored measurement data, while both the EKF and the UKF provide unreliable estimates in censored data conditions.