Libin Rong

Rapid Emergence of Protease Inhibitor Resistance in Hepatitis C Virus

Computational modeling suggests that the rapid emergence of resistance to drugs against hepatitis C virus is a result of preexisting resistant virus and potent suppression of wild-type virus. Fighting the Resistance in Hepatitis C Following a few decades behind the infection-fighting miracles wrought by antibiotics was the cloud to the silver lining—drug resistance. Microbes evolved under the selection pressure exerted by antibiotics, and drug-resistant strains began to emerge. The microbes that cause respiratory infections, HIV/AIDS, diarrhea, tuberculosis, and malaria have all developed some resistance to their primary treatment, forcing physicians to turn to secondary, often inferior agents. Hepatitis C virus (HCV), which can cause serious liver damage, is genetically diverse and so may be particularly prone to develop resistance, sometimes within days of treatment onset. One way to combat this would be to administer multiple drugs, each with a different way of inhibiting the pathogen. Rong and colleagues have used a modeling approach to predict how resistance emerges in this disease and suggest that a combination of drugs that can fight three or more mutated viral strains may be needed to cure this disease. By using experimentally measured mutation rates and knowing the rate of viral production and other parameters, the authors were able to calculate that all possible HCV variants with single and double mutations already exist in infected patients before treatment and that one additional mutation is expected to arise during therapy. Thus, they concluded that a combination of direct antiviral drugs would need to be effective against variants with three or more mutations. The authors also constructed a model to study the development of drug-resistant virus during treatment. They showed that the model predictions match well with the actual data from a clinical trial in which the drug telaprevir was given to patients with HCV. Because hepatitis C is potentially curable, this modeling tool for designing more effective treatments is especially welcome. It may, however, also prove useful for application to other situations in which the emergence of viral or bacterial resistance renders the primary therapeutic treatment ineffective. About 170 million people worldwide are infected with hepatitis C virus (HCV). The current standard therapy leads to sustained viral elimination in only ~50% of the treated patients. Telaprevir, an HCV protease inhibitor, has substantial antiviral activity in patients with chronic HCV infection. However, in clinical trials, drug-resistant variants emerge at frequencies of 5 to 20% of the total virus population as early as the second day after the beginning of treatment. Here, using probabilistic and viral dynamic models, we show that such rapid emergence of drug resistance is expected. We calculate that all possible single- and double-mutant viruses preexist before treatment and that one additional mutation is expected to arise during therapy. Examining data from a clinical trial of telaprevir therapy for HCV infection in detail, we show that our model fits the observed dynamics of both drug-sensitive and drug-resistant viruses and argue that therapy with only direct antivirals will require drug combinations that have a genetic barrier of four or more mutations.

Modeling HIV persistence, the latent reservoir, and viral blips

HIV-1 eradication from infected individuals has not been achieved with the prolonged use of highly active antiretroviral therapy (HAART). The cellular reservoir for HIV-1 in resting memory CD4(+) T cells remains a major obstacle to viral elimination. The reservoir does not decay significantly over long periods of time but is able to release replication-competent HIV-1 upon cell activation. Residual ongoing viral replication may likely occur in many patients because low levels of virus can be detected in plasma by sensitive assays and transient episodes of viremia, or HIV-1 blips, are often observed in patients even with successful viral suppression for many years. Here we review our current knowledge of the factors contributing to viral persistence, the latent reservoir, and blips, and mathematical models developed to explore them and their relationships. We show how mathematical modeling has helped improve our understanding of HIV-1 dynamics in patients on HAART and of the quantitative events underlying HIV-1 latency, reservoir stability, low-level viremic persistence, and emergence of intermittent viral blips. We also discuss treatment implications related to these studies.

Modeling Latently Infected Cell Activation: Viral and Latent Reservoir Persistence, and Viral Blips in HIV-infected Patients on Potent Therapy

Although potent combination therapy is usually able to suppress plasma viral loads in HIV-1 patients to below the detection limit of conventional clinical assays, a low level of viremia frequently can be detected in plasma by more sensitive assays. Additionally, many patients experience transient episodes of viremia above the detection limit, termed viral blips, even after being on highly suppressive therapy for many years. An obstacle to viral eradication is the persistence of a latent reservoir for HIV-1 in resting memory CD4+ T cells. The mechanisms underlying low viral load persistence, slow decay of the latent reservoir, and intermittent viral blips are not fully characterized. The quantitative contributions of residual viral replication to viral and the latent reservoir persistence remain unclear. In this paper, we probe these issues by developing a mathematical model that considers latently infected cell activation in response to stochastic antigenic stimulation. We demonstrate that programmed expansion and contraction of latently infected cells upon immune activation can generate both low-level persistent viremia and intermittent viral blips. Also, a small fraction of activated T cells revert to latency, providing a potential to replenish the latent reservoir. By this means, occasional activation of latently infected cells can explain the variable decay characteristics of the latent reservoir observed in different clinical studies. Finally, we propose a phenomenological model that includes a logistic term representing homeostatic proliferation of latently infected cells. The model is simple but can robustly generate the multiphasic viral decline seen after initiation of therapy, as well as low-level persistent viremia and intermittent HIV-1 blips. Using these models, we provide a quantitative and integrated prospective into the long-term dynamics of HIV-1 and the latent reservoir in the setting of potent antiretroviral therapy.

MATHEMATICAL ANALYSIS OF AGE-STRUCTURED HIV-1 DYNAMICS WITH COMBINATION ANTIRETROVIRAL THERAPY*

Various classes of antiretroviral drugs are used to treat HIV infection, and they target different stages of the viral life cycle. Age-structured models can be employed to study the impact of these drugs on viral dynamics. We consider two models with age-of-infection and combination therapies involving reverse transcriptase, protease, and entry/fusion inhibitors. The reproductive number R is obtained, and a detailed stability analysis is provided for each model. Interestingly, we find in the age-structured model a different functional dependence of R onRT , the efficacy of a reverse transcriptase inhibitor, than that found previously in nonage-structured models, which has significant implications in predicting the effects of drug therapy. The influence of drug therapy on the within-host viral fitness and the possible development of drug-resistant strains are also discussed. Numerical simulations are performed to study the dynamical behavior of solutions of the models, and the effects of different combinations of antiretroviral drugs on viral dynamics are compared.

Mathematical modeling of viral kinetics under immune control during primary HIV-1 infection

Primary human immunodeficiency virus (HIV) infection is characterized by an initial exponential increase of viral load in peripheral blood reaching a peak, followed by a rapid decline to the viral setpoint. Although the target-cell-limited model can account for part of the viral kinetics observed early in infection [Phillips, 1996. Reduction of HIV concentration during acute infection: independence from a specific immune response. Science 271 (5248), 497-499], it frequently predicts highly oscillatory kinetics after peak viremia, which is not typically observed in clinical data. Furthermore, the target-cell-limited model is unable to predict long-term viral kinetics, unless a delayed immune effect is assumed [Stafford et al., 2000. Modeling plasma virus concentration during primary HIV infection. J. Theor. Biol. 203 (3), 285-301]. We show here that extending the target-cell-limited model, by implementing a saturation term for HIV-infected cell loss dependent upon infected cell levels, is able to reproduce the diverse observed viral kinetic patterns without the assumption of a delayed immune response. Our results suggest that the immune response may have significant effect on the control of the virus during primary infection and may support experimental observations that an anti-HIV immune response is already functional during peak viremia.

Combination Antiviral Therapy for Influenza: Predictions From Modeling of Human Infections

Emergence of resistance is a major concern in influenza antiviral treatment and prophylaxis. Combination antiviral therapy might overcome this problem. Here, we estimate that all possible single mutants and a sizeable fraction of double mutants are generated during an uncomplicated influenza infection. While most of them may sustain a fitness cost, some variants may confer drug resistance and be selected during therapy. We argue that a triple combination regimen would markedly reduce the risk of antiviral resistance emergence in seasonal and pandemic influenza viruses, especially in seriously ill or immunocompromised hosts.

Modeling Quasispecies and Drug Resistance in Hepatitis C Patients Treated with a Protease Inhibitor

Telaprevir, a novel hepatitis C virus (HCV) NS3-4A serine protease inhibitor, has demonstrated substantial antiviral activity in patients infected with HCV. However, drug-resistant HCV variants were detected in vivo at relatively high frequency a few days after drug administration. Here we use a two-strain mathematical model to explain the rapid emergence of drug resistance in HCV patients treated with telaprevir monotherapy. We examine the effects of backward mutation and liver cell proliferation on the preexistence of the mutant virus and the competition between wild-type and drug-resistant virus during therapy. We also extend the two-strain model to a general model with multiple viral strains. Mutations during therapy only have a minor effect on the dynamics of various viral strains, although they are capable of generating low levels of HCV variants that would otherwise be completely suppressed because of fitness disadvantages. Liver cell proliferation may not affect the pretreatment frequency of mutant variants, but is able to influence the quasispecies dynamics during therapy. It is the relative fitness of each mutant strain compared with wild-type that determines which strain(s) will dominate the virus population. This study provides a theoretical framework for exploring the prevalence of preexisting mutant variants and the evolution of drug resistance during treatment with other HCV protease inhibitors or polymerase inhibitors.

A Perspective on Modeling Hepatitis C Virus Infection

Summary.  By mathematically describing early hepatitis C virus (HCV) RNA decay after initiation of interferon (IFN)‐based antiviral therapy, crucial parameters of the in vivo viral kinetics have been estimated, such as the rate of production and clearance of free virus, and the rate of loss of infected cells. Furthermore, by suggesting mechanisms of action for IFN and ribavirin mathematical modelling has provided a means for evaluating and optimizing treatment strategies. Here, we review recent modelling developments for understanding complex viral kinetics patterns, such as triphasic HCV RNA declines and viral rebounds observed in patients treated with pegylated interferon and ribavirin. Moreover, we discuss new modelling approaches developed to interpret the viral kinetics observed in clinical trials with direct‐acting antiviral agents, which induce a rapid decline of wild‐type virus but also engender a higher risk for emergence of drug‐resistant variants. Lastly, as in vitro systems have allowed a better characterization of the virus lifecycle, we discuss new modelling approaches that combine the intracellular and the extracellular viral dynamics.