Model-based simulation and prediction of an antiviral strategy against influenza A infection

Treatment of Respiratory Viral Coinfections

With the advent of rapid multiplex PCR, physicians have been able to test for multiple viral pathogens when a patient presents with influenza-like illness. This has led to the discovery that many respiratory infections are caused by more than one virus. Antiviral treatment of viral coinfections can be complex because treatment of one virus will affect the time course of the other virus. Since effective antivirals are only available for some respiratory viruses, careful consideration needs to be given on the effect treating one virus will have on the dynamics of the other virus, which might not have available antiviral treatment. In this study, we use mathematical models of viral coinfections to assess the effect of antiviral treatment on coinfections. We examine the effect of the mechanism of action, relative growth rates of the viruses, and the assumptions underlying the interaction of the viruses. We find that high antiviral efficacy is needed to suppress both infections. If high doses of both antivirals are not achieved, then we run the risk of lengthening the duration of coinfection or even of allowing a suppressed virus to replicate to higher viral titers.

Integrating mathematical models with experimental data to investigate the within-host dynamics of bacterial infections

Bacterial infections still constitute a major cause of mortality and morbidity worldwide. The unavailability of therapeutics, antimicrobial resistance and the chronicity of infections due to incomplete clearance contribute to this phenomenon. Despite the progress in antimicrobial and vaccine development, knowledge about the effect that therapeutics have on the host–bacteria interactions remains incomplete. Insights into the characteristics of bacterial colonization and migration between tissues and the relationship between replication and host- or therapeutically induced killing can enable efficient design of treatment approaches. Recently, innovative experimental techniques have generated data enabling the qualitative characterization of aspects of bacterial dynamics. Here, we argue that mathematical modeling as an adjunct to experimental data can enrich the biological insight that these data provide. However, due to limited interdisciplinary training, efforts to combine the two remain limited. To promote this dialogue, we provide a categorization of modeling approaches highlighting their relationship to data generated by a range of experimental techniques in the area of in vivo bacterial dynamics. We outline common biological themes explored using mathematical models with case studies across all pathogen classes. Finally, this review advocates multidisciplinary integration to improve our mechanistic understanding of bacterial infections and guide the use of existing or new therapies.

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.

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

The emergence of influenza drug resistance has become of particular interest as current planning for an influenza pandemic involves using massive amounts of antiviral drugs. We use semi-stochastic simulations to examine the emergence of drug resistant mutants during the course of a single infection within a patient in the presence and absence of antiviral therapy. We specifically examine three factors and their effect on the emergence of drug-resistant mutants: antiviral mechanism, the immune response, and surface proteins. We find that adamantanes, because they act at the start of the replication cycle to prevent infection, are less likely to produce drug-resistant mutants than NAIs, which act at the end of the replication cycle. A mismatch between surface proteins and internal RNA results in drug-resistant mutants being less likely to emerge, and emerging later in the infection because the mismatch gives antivirals a second chance to prevent propagation of the mutation. The immune response subdues slow growing infections, further reducing the probability that a drug resistant mutant will emerge and yield a drug-resistant infection. These findings improve our understanding of the factors that contribute to the emergence of drug resistance during the course of a single influenza infection.

Avian influenza viruses that cause highly virulent infections in humans exhibit distinct replicative properties in contrast to human H1N1 viruses

Avian influenza viruses present an emerging epidemiological concern as some strains of H5N1 avian influenza can cause severe infections in humans with lethality rates of up to 60%. These have been in circulation since 1997 and recently a novel H7N9-subtyped virus has been causing epizootics in China with lethality rates around 20%. To better understand the replication kinetics of these viruses, we combined several extensive viral kinetics experiments with mathematical modelling of in vitro infections in human A549 cells. We extracted fundamental replication parameters revealing that, while both the H5N1 and H7N9 viruses replicate faster and to higher titers than two low-pathogenicity H1N1 strains, they accomplish this via different mechanisms. While the H7N9 virions exhibit a faster rate of infection, the H5N1 virions are produced at a higher rate. Of the two H1N1 strains studied, the 2009 pandemic H1N1 strain exhibits the longest eclipse phase, possibly indicative of a less effective neuraminidase activity, but causes infection more rapidly than the seasonal strain. This explains, in part, the pandemic strain’s generally slower growth kinetics and permissiveness to accept mutations causing neuraminidase inhibitor resistance without significant loss in fitness. Our results highlight differential growth properties of H1N1, H5N1 and H7N9 influenza viruses.

Prevention and Control of Influenza Viruses

The 2003–2004 outbreaks of highly pathogenic avian influenza (HPAI) have proven to be disastrous to the regional poultry industry in Asia, and have raised serious worldwide public health apprehension regarding the steps that should be taken to urgently control HPAI. Control measures must be taken based on the principles of biosecurity and disease management and at the same time making public aware of the precautionary measures at the verge of outbreak. Creation of protection and surveillance zones, various vaccination strategies viz. routine, preventive, emergency, mass and targeted vaccination programmes using live, inactivated and recombinant vaccines are the common strategies adopted in different parts of the globe. The new generation vaccines include recombinant vaccines and recombinant fusion vaccine. The pro-poor disease control programmes, giving compensation and subsidies to the farmers along with effective and efficient Veterinary Services forms integral part of control of HPAI. Following biosecurity principles and vaccination forms integral part of control programme against swine and equine influenza as well. Use of neuraminidase (NA) inhibitors (Zanamivir and Oseltamivir) for the treatment of human influenza has been widely accepted worldwide. The threat of increasing resistance of the flu viruses to these antivirals has evoked interest in the development of novel antiviral drugs for influenza virus such as inhibitors of cellular factors and host signalling cascades, cellular miRNAs, siRNA and innate immune peptides (defensins and cathelicidins). Commercial licensed inactivated vaccines for humans against influenza A and B viruses are available consisting of three influenza viruses: influenza type A subtype H3N2, influenza type A subtype H1N1 (seasonal) virus strain and influenza type B virus strain. As per WHO, use of tetravaccine consisting of antigens of influenza virus serotypes H3N2, H1N1, B and H5 is the most promising method to control influenza pandemic. All healthy children in many countries are required to be vaccinated between 6 and 59 months of age. The seasonal vaccines currently used in humans induce strain-specific humoral immunity as the antibodies. Universal influenza virus vaccines containing the relatively conserved ectodomain of M2 (M2e), M1, HA fusion peptide and stalk domains, NA, NP alone or in combination have been developed which have been shown to induce cross-protection. The T cell-based vaccines are another recent experimental approach that has been shown to elicit broad-spectrum heterosubtypic immunity in the host. As far as HPAI is concerned, various pandemic preparedness strategies have been documented.