Rutao Luo

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.

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.

Modeling Uncertainty in Single-Copy Assays for HIV

ABSTRACT We present a simple computational model of measurement accuracy for single-copy sensitivity assays (SCA) of HIV RNA that was developed from first principles. The model shows that the SCA is significantly right-skewed. Measured virus concentrations of 1 and 10 virions/ml had overlapping 95% confidence intervals and were statistically indistinguishable.

Spatial modeling of HIV cryptic viremia and 2-LTR formation during raltegravir intensification

Combination Antiretroviral Therapy (cART) can suppress plasma HIV below the limit of detection in normal assays. Recently reported results suggest that viral replication may continue in some patients, despite undetectable levels in the blood. It has been suggested that the appearance of the circularized episomal HIV DNA artifact 2-LTR following treatment intensification with the integrase inhibitor raltegravir is a marker of ongoing viral replication. Other work has suggested that lymphoid organs may be a site of reduced antiviral penetration and increased viral production. In this study we model the hypothesis that this ongoing replication occurs in lymphoid follicle sanctuary sites and investigate the patterns of 2-LTR formation expected after raltegravir application. Experimental data is used to estimate the reaction and diffusion parameters in the model, and Monte-Carlo simulations are used to explore model behavior subject to variation in these rates. The results suggest that conditions for the formation of an observed transient peak in 2-LTR formation following raltegravir intensification include a sanctuary site diameter larger than 0.2mm, a viral basic reproductive ratio within the site larger than 1, and a total volume of active sanctuary sites above 20mL. Significant levels of uncontrolled replication can occur in the sanctuary sites without measurable changes in the plasma viral load. By contrast, subcritical replication (where the basic reproductive ratio of the virus is less than 1 in all sites) always results in monotonic increases of measured 2-LTR following raltegravir intensification, occurring at levels below the limit of detection.

Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching

In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by “pre-conditioning” the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implementation of these approaches to modeling errors. To account for hidden parameter dependencies, we use parameter distributions obtained from the convergence of Bayesian parameter estimation techniques applied to sets of clinical data obtained during serial therapy interruptions as the distribution from which the Monte-Carlo method samples.

Resistance Risk Management in HIV Therapy Switching with Explicit Quiescent T-Cell Modeling

Abstract Highly Active Antiretroviral Therapy (HAART) has proven remarkably effective in controlling the development of HIV. However, drug resistance may compromise these benefits. During the use of HAART, drug-resistant strains can 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. Drug resistance is therefore a critical problem for HIV treatment. In this paper, we explicitly model one known reservoir compartment, the quiescent infected CD4+ T cells, and explore the effects of this reservoir on a drug-switching strategy designed to minimize the further development of drug-resistant virus.

Quantitative analysis of viral persistence and transient viral load rebound from HIV clinical data

Highly active antiretroviral therapy (HAART) suppresses HIV RNA viral load below the limit of detection for many patients. However, clinical data demonstrates that the HIV virus is not eradicated by HAART, even in patients who have had no detectable virus for 7 years [1]. One possible reason is that a stable resting latent reservoir with a long half-life exists in resting memory CD4+T cells [2]. In this paper, we propose a mathematical model with a constant contribution of a stable latent reservoir and identified this constant by using one patients data from AutoVac HAART interruption study [3]. Many patients also have transient rebounds of plasma viral RNA (viral blips) under otherwise successful control of the virus by HAART. Activation of latently infected cells can explain these transient rebounds of viral load. Little quantitative analysis about the activation of reservoir has been done based on any clinical experiment data. Here, we model the activation dynamics of the reservoir by a time-independent activation rate and estimate this rate by using the clinical data from the AutoVac HAART interruption study [3].