Modelling the emergence of influenza drug resistance: The roles of surface proteins, the immune response and antiviral mechanisms

How do viruses get around? A review of mathematical modeling of in-host viral transmission

Mathematical models of within host viral infections have provided important insights into the dynamics of viral infections. There has been much progress in adding more detailed biological processes to these models, such as incorporating the immune response, drug resistance, and viral coinfections. Unfortunately, the default assumption for the majority of these models is that virus is released from infected cells, travels through extracellular space, and deposits on another cell. This mode of transmission is known as cell-free infection. However, virus can also tunnel directly from one cell to another or cause neighboring cells to fuse, processes that also pass the infection to new cells. Additionally, most models do not explicitly include the transport of virus from one cell to another when describing cell-free transmission. In this review, we examine the current state of mathematical modeling that explicitly examines transmission beyond the cell-free assumption. While mathematical models have been developed to examine these processes, there are further improvements that can be made to better capture known viral dynamics.

Exploring the treatment of SARS-CoV-2 with modified vesicular stomatitis virus

SARS-CoV-2 caused a global pandemic and is now an endemic virus that will require continued antiviral and vaccine development. A possible new treatment modality was recently suggested that would use vesicular stomatitis virus (VSV) modified to express the ACE2 receptor. Since the modified VSV expresses the cell surface receptor that is used by the SARS-CoV-2 spike protein, the thought is that SARS-CoV-2 virions would bind to the modified VSV and thus be neutralized. Additionally, since SARS-CoV-2 infected cells also express the spike protein, the modified VSV could potentially infect these cells, allowing for its own replication, but also potentially interfering with replication of SARS-CoV-2. This idea has not yet been tested experimentally, but we can investigate the feasibility of this possible treatment theoretically. In this manuscript, we develop a mathematical model of this suggested treatment and explore conditions under which it might be effective. We find that treatment with modified VSV does little to change the SARS-CoV-2 time course except when the treatment is applied at the onset of the SARS-CoV-2 infection at very high doses. In this case, VSV reduces the peak SARS-CoV-2 viral load, but lengthens the duration of the SARS-CoV-2 infection. Thus, we find that modified VSV treatment is unlikely to be effective largely because it does not prevent infection of cells by SARS-CoV-2.

Viral Rebound After Antiviral Treatment: A Mathematical Modeling Study of the Role of Antiviral Mechanism of Action

The development of antiviral treatments for SARS-CoV-2 was an important turning point for the pandemic. Availability of safe and effective antivirals has allowed people to return back to normal life. While SARS-CoV-2 antivirals are highly effective at preventing severe disease, there have been concerning reports of viral rebound in some patients after cessation of antiviral treatment. In this study, we use a mathematical model of viral infection to study the potential of different antivirals to prevent viral rebound. We find that antivirals that block production are most likely to result in viral rebound if the treatment time course is not sufficiently long. Since these antivirals do not prevent infection of cells, cells continue to be infected during treatment. When treatment is stopped, the infected cells will begin producing virus at the usual rate. Antivirals that prevent infection of cells are less likely to result in viral rebound since cells are not being infected during treatment. This study highlights the role of antiviral mechanism of action in increasing or reducing the probability of viral rebound.

Mathematical Modeling of Virus-Mediated Syncytia Formation: Past Successes and Future Directions

Many viruses have the ability to cause cells to fuse into large multi-nucleated cells, known as syncytia. While the existence of syncytia has long been known and its importance in helping spread viral infection within a host has been understood, few mathematical models have incorporated syncytia formation or examined its role in viral dynamics. This review examines mathematical models that have incorporated virus-mediated cell fusion and the insights they have provided on how syncytia can change the time course of an infection. While the modeling efforts are limited, they show promise in helping us understand the consequences of syncytia formation if future modeling efforts can be coupled with appropriate experimental efforts to help validate the models.

The effect of random virus failure following cell entry on infection outcome and the success of antiviral therapy

A virus infection can be initiated with very few or even a single infectious virion, and as such can become extinct, i.e. stochastically fail to take hold or spread significantly. There are many ways that a fully competent infectious virion, having successfully entered a cell, can fail to cause a productive infection, i.e. one that yields infectious virus progeny. Though many stochastic models (SMs) have been developed and used to estimate a virus infection's establishment probability, these typically neglect infection failure post virus entry. The SM presented herein introduces parameter [Formula: see text] which corresponds to the probability that a virion's entry into a cell will result in a productive cell infection. We derive an expression for the likelihood of infection establishment in this new SM, and find that prophylactic therapy with an antiviral reducing [Formula: see text] is at least as good or better at decreasing the establishment probability, compared to antivirals reducing the rates of virus production or virus entry into cells, irrespective of the SM parameters. We investigate the difference in the fraction of cells consumed by so-called extinct versus established virus infections, and find that this distinction becomes biologically meaningless as the probability of establishment approaches zero. We explain why the release of virions continuously over an infectious cell's lifespan, rather than as a single burst at the end of the cell's lifespan, does not result in an increased risk of infection extinction. We show, instead, that the number of virus released, not the timing of the release, affects infection establishment and associated critical antiviral efficacy.

Effect of cellular regeneration and viral transmission mode on viral spread

Illness negatively affects all aspects of life and one major cause of illness is viral infections. Some viral infections can last for weeks; others, like influenza (the flu), can resolve quickly. During infections, uninfected cells can replicate in order to replenish the cells that have died due to the virus. Many viral models, especially those for short-lived infections like influenza, tend to ignore cellular regeneration since many think that uncomplicated influenza resolves much faster than cells regenerate. This research accounts for cellular regeneration, using an agent-based framework, and varies the regeneration rate in order to understand how cell regeneration affects viral infection dynamics under assumptions of different modes of transmission. We find that although the general trends in peak viral load, time of viral peak, and chronic viral load as regeneration rate changes are the same for cell-free or cell-to-cell transmission, the changes are more extreme for cell-to-cell transmission due to limited access of infected cells to newly generated cells.

Antiviral Peptides as Anti-Influenza Agents

Influenza viruses represent a leading cause of high morbidity and mortality worldwide. Approaches for fighting flu are seasonal vaccines and some antiviral drugs. The development of the seasonal flu vaccine requires a great deal of effort, as careful studies are needed to select the strains to be included in each year's vaccine. Antiviral drugs available against Influenza virus infections have certain limitations due to the increased resistance rate and negative side effects. The highly mutative nature of these viruses leads to the emergence of new antigenic variants, against which the urgent development of new approaches for antiviral therapy is needed. Among these approaches, one of the emerging new fields of "peptide-based therapies" against Influenza viruses is being explored and looks promising. This review describes the recent findings on the antiviral activity, mechanism of action and therapeutic capability of antiviral peptides that bind HA, NA, PB1, and M2 as a means of countering Influenza virus infection.

Untapping host-targeting cross-protective efficacy of anticoagulants against SARS-CoV-2

Responding quickly to emerging respiratory viruses, such as SARS-CoV-2 the causative agent of coronavirus disease 2019 (COVID-19) pandemic, is essential to stop uncontrolled spread of these pathogens and mitigate their socio-economic impact globally. This can be achieved through drug repurposing, which tackles inherent time- and resource-consuming processes associated with conventional drug discovery and development. In this review, we examine key preclinical and clinical therapeutic and prophylactic approaches that have been applied for treatment of SARS-CoV-2 infection. We break these strategies down into virus- versus host-targeting and discuss their reported efficacy, advantages, and disadvantages. Importantly, we highlight emerging evidence on application of host serine protease-inhibiting anticoagulants, such as nafamostat mesylate, as a potentially powerful therapy to inhibit virus activation and offer cross-protection against multiple strains of coronavirus, lower inflammatory response independent of its antiviral effect, and modulate clotting problems seen in COVID-19 pneumonia.

Anti-Influenza Strategies Based on Nanoparticle Applications

Influenza virus has the potential for being one of the deadliest viruses, as we know from the pandemic's history. The influenza virus, with a constantly mutating genome, is becoming resistant to existing antiviral drugs and vaccines. For that reason, there is an urgent need for developing new therapeutics and therapies. Despite the fact that a new generation of universal vaccines or anti-influenza drugs are being developed, the perfect remedy has still not been found. In this review, various strategies for using nanoparticles (NPs) to defeat influenza virus infections are presented. Several categories of NP applications are highlighted: NPs as immuno-inducing vaccines, NPs used in gene silencing approaches, bare NPs influencing influenza virus life cycle and the use of NPs for drug delivery. This rapidly growing field of anti-influenza methods based on nanotechnology is very promising. Although profound research must be conducted to fully understand and control the potential side effects of the new generation of antivirals, the presented and discussed studies show that nanotechnology methods can effectively induce the immune responses or inhibit influenza virus activity both in vitro and in vivo. Moreover, with its variety of modification possibilities, nanotechnology has great potential for applications and may be helpful not only in anti-influenza but also in the general antiviral approaches.

Targeting SARS-CoV-2 receptors as a means for reducing infectivity and improving antiviral and immune response: an algorithm-based method for overcoming resistance to antiviral agents

The ongoing severe acute respiratory syndrome pandemic caused by the novel coronavirus 2 (SARS-CoV-2) is associated with high morbidity and mortality rates, and it has created a pressing global need for effective antiviral therapies against it. COVID-19 disease pathogenesis is characterized by an initial virus-mediated phase, followed by inappropriate hyperactivation of the immune system leading to organ damage. Targeting of the SARS-CoV-2 viral receptors is being explored as a therapeutic option for these patients. In this paper, we summarize several potential receptors associated with the infectivity of SARS-CoV-2 and discuss their association with the immune-mediated inflammatory response. The potential for the development of resistance towards antiviral drugs is also presented. An algorithm-based platform to improve the efficacy of and overcome resistance to viral receptor blockers through the introduction of personalized variability is described. This method is designed to ensure sustained antiviral effectiveness when using SARS-CoV-2 receptor blockers.

SARS-CoV-2 coinfections: Could influenza and the common cold be beneficial?

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world, causing serious illness and death and creating a heavy burden on the healthcare systems of many countries. Since the virus first emerged in late November 2019, its spread has coincided with peak circulation of several seasonal respiratory viruses, yet some studies have noted limited coinfections between SARS-CoV-2 and other viruses. We use a mathematical model of viral coinfection to study SARS-CoV-2 coinfections, finding that SARS-CoV-2 replication is easily suppressed by many common respiratory viruses. According to our model, this suppression is because SARS-CoV-2 has a lower growth rate (1.8/d) than the other viruses examined in this study. The suppression of SARS-CoV-2 by other pathogens could have implications for the timing and severity of a second wave.

Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections

SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) causes a variety of responses in those who contract the virus, ranging from asymptomatic infections to acute respiratory failure and death. While there are likely multiple mechanisms triggering severe disease, one potential cause of severe disease is the size of the initial inoculum. For other respiratory diseases, larger initial doses lead to more severe outcomes. We investigate whether there is a similar link for SARS-CoV-2 infections using the combination of an agent-based model (ABM) and a partial differential equation model (PDM). We use the model to examine the viral time course for different sizes of initial inocula, generating dose-response curves for peak viral load, time of viral peak, viral growth rate, infection duration, and area under the viral titer curve. We find that large initial inocula lead to short infections, but with higher viral titer peaks; and that smaller initial inocula lower the viral titer peak, but make the infection last longer.

The evolution of Tamiflu synthesis, 20 years on: Advent of enabling technologies the last piece of the puzzle?

Influenza is a serious respiratory disease responsible for significant morbidity and mortality due to both annual epidemics and pandemics; its treatment involves the use of neuraminidase inhibitors. (-)-Oseltamivir phosphate (Tamiflu) approved in 1999, is one of the most potent oral anti-influenza neuraminidase inhibitors. Consequently, more than 70 Tamiflu synthetic procedures have been developed to date. Herein, we highlight the evolution of Tamiflu synthesis since its discovery over 20 years ago in the quest for a truly efficient, safe, cost-effective and environmentally benign synthetic procedure. We have selected a few representative routes to give a clear account of the past, present and the future with the advent of enabling technologies.

A two-thresholds policy for a Filippov model in combating influenza

This work designs a two-thresholds policy for a Filippov model in combating influenza, so as to estimate when and whether to take control strategies, including the media coverage, antiviral treatment of infected individuals and vaccination of susceptible population. By introducing two tolerance thresholds [Formula: see text] and [Formula: see text] of susceptible and infected individuals, the two-thresholds policy is designed as: a vaccination program is implemented when the number of susceptible individuals is above [Formula: see text]; an antiviral treatment strategy is taken and the mass media begins to report information about influenza when the infection number is larger than [Formula: see text]; no control strategies are required in other cases. Furthermore, the global dynamics of the model are analyzed by varying these two thresholds, including the existence and dynamics of sliding mode, and the existence and global stability of equilibrium. It is shown that the model solutions ultimately converge to a pseudoequilibrium or a pseudoattractor on the switching surface, or a real equilibrium. The obtained results indicate that, by choosing susceptible and infected thresholds properly, the infection number can be remained below or at an acceptable level.

A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract

Within the human respiratory tract (HRT), virus diffuses through the periciliary fluid (PCF) bathing the epithelium. But virus also undergoes advection: as the mucus layer sitting atop the PCF is pushed along by the ciliated cell's beating cilia, the PCF and its virus content are also pushed along, upwards towards the nose and mouth. While many mathematical models (MMs) have described the course of influenza A virus (IAV) infections in vivo, none have considered the impact of both diffusion and advection on the kinetics and localization of the infection. The MM herein represents the HRT as a one-dimensional track extending from the nose down towards the lower HRT, wherein stationary cells interact with IAV which moves within (diffusion) and along with (advection) the PCF. Diffusion was found to be negligible in the presence of advection which effectively sweeps away IAV, preventing infection from disseminating below the depth at which virus first deposits. Higher virus production rates (10-fold) are required at higher advection speeds (40 μm/s) to maintain equivalent infection severity and timing. Because virus is entrained upwards, upper parts of the HRT see more virus than lower parts. As such, infection peaks and resolves faster in the upper than in the lower HRT, making it appear as though infection progresses from the upper towards the lower HRT, as reported in mice. When the spatial MM is expanded to include cellular regeneration and an immune response, it reproduces tissue damage levels reported in patients. It also captures the kinetics of seasonal and avian IAV infections, via parameter changes consistent with reported differences between these strains, enabling comparison of their treatment with antivirals. This new MM offers a convenient and unique platform from which to study the localization and spread of respiratory viral infections within the HRT.

Nanobody-based sandwich reporter system for living cell sensing influenza A virus infection

The influenza epidemic is a huge burden to public health. Current influenza vaccines provide limited protection against new variants due to frequent mutation of the virus. The continual emergence of novel variants necessitates the method rapidly monitoring influenza virus infection in experimental systems. Although several replication-competent reporter viruses carrying fluorescent proteins or small luciferase have been generated in previous studies, visualizing influenza virus infection via such strategy requires reverse genetic modification for each viral strain which is usually time-consuming and inconvenient. Here, we created a novel influenza A nucleoprotein (NP) dependent reporter gene transcription activation module using NP-specific nanobodies. Our results demonstrated the modular design allowed reporter genes (mNeonGreen fluorescent protein and Gaussia luciferase) specifically expressing to detect intracellular NP protein, and therefore acts as a universal biosensor to monitor infection of various influenza A subtypes in living cells. The new system may provide a powerful tool to analyze influenza A infections at the cellular level to facilitate new antiviral drug discovery. Moreover, this approach may easily extend to develop live-cell biosensors for other viruses.

Effect of stochasticity on coinfection dynamics of respiratory viruses

Background

Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality.

Results

In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection.

Conclusions

We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2793-6) contains supplementary material, which is available to authorized users.

Transportan-derived cell-penetrating peptide delivers siRNA to inhibit replication of influenza virus in vivo

Introduction

In this study, we report on the development of an effective delivery system for siRNAs; a novel cell-penetrating peptide (CPP), T9(dR), obtained from transportan (TP), was used for in vivo and in vitro testing.

Methods

In this study, toxicity of T9(dR) and TP and efficient delivery of siRNA were tested in 293T, MDCK, RAW, and A549 cells. Furthermore, T9(dR)- and TP-delivered siRNAs against nucleoprotein (NP) gene segment of influenza virus (siNP) were studied in both cell lines and mice.

Results

Gel retardation showed that T9(dR) effectively condensed siRNA into nanoparticles sized between 350 and 550 nm when the mole ratio of T9(dR) to siRNA was ≥4:1. In vitro studies demonstrated that T9(dR) successfully delivered siRNA with low cellular toxicity into several cell lines. It was also observed that T9(dR)-delivered siRNAs inhibited replication of influenza virus more efficiently as compared to that delivered by TP into the MDCK and A549 cells. It was also noticed that when given a combined tail vein injection of siNP and T9(dR) or TP, all mice in the 50 nmol siNP group infected with PR8 influenza virus survived and showed weight recovery at 2 weeks post-infection.

Conclusion

This study indicates that T9(dR) is a promising siRNA delivery tool with potential application for nucleotide drug delivery.

Investigating Different Mechanisms of Action in Combination Therapy for Influenza

Combination therapy for influenza can have several benefits, from reducing the emergence of drug resistant virus strains to decreasing the cost of antivirals. However, there are currently only two classes of antivirals approved for use against influenza, limiting the possible combinations that can be considered for treatment. However, new antivirals are being developed that target different parts of the viral replication cycle, and their potential for use in combination therapy should be considered. The role of antiviral mechanism of action in the effectiveness of combination therapy has not yet been systematically investigated to determine whether certain antiviral mechanisms of action pair well in combination. Here, we use a mathematical model of influenza to model combination treatment with antivirals having different mechanisms of action to measure peak viral load, infection duration, and synergy of different drug combinations. We find that antivirals that lower the infection rate and antivirals that increase the duration of the eclipse phase perform poorly in combination with other antivirals.

Intermittent treatment of severe influenza

Severe, long-lasting influenza infections are often caused by new strains of the virus. The long duration of these infections leads to an increased opportunity for the emergence of drug resistant mutants. This is particularly problematic since for new strains there is often no vaccine, so drug treatment is the first line of defense. One strategy for trying to minimize drug resistance is to apply drugs periodically. During treatment phases the wild-type virus decreases, but resistant virus might increase; when there is no treatment, wild-type virus will hopefully out-compete the resistant virus, driving down the number of resistant virus. A stochastic model of severe influenza is combined with a model of drug resistance to simulate long-lasting infections and intermittent treatment with two types of antivirals: neuraminidase inhibitors, which block release of virions; and adamantanes, which block replication of virions. Each drug's ability to reduce emergence of drug resistant mutants is investigated. We find that cell regeneration is required for successful implementation of intermittent treatment and that the optimal cycling parameters change with regeneration rate.