Date Published: July 10, 2017
Publisher: Public Library of Science
Author(s): Hana M. Dobrovolny, Catherine A. A. Beauchemin, Luis Menéndez-Arias.
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.
The annual cost of influenza illness and the ongoing threat of emergence of a pandemic strain make it all the more necessary to revisit the treatment options currently available. Two classes of drugs, adamantanes and neuraminidase inhibitors (NAIs), are currently available for treatment of influenza, although resistance to both classes of drugs threatens our ability to effectively treat influenza . Better understanding the processes underlying the emergence of drug resistance over the course of an influenza infection will enable health authorities to make more effective use of antivirals on a seasonal basis, or in the context of a pandemic.
While this is a modelling study, some of the predictions of our models have important implications that warrant further investigation. Our results indicate that it is particularly crucial to prevent breakthrough infections as they contain a large fraction of drug-resistant mutants which can then be spread to other people. Our models indicate that there is a minimum relative mutant fitness relative to the wild-type strain below which there will be no breakthrough infections. For the particular parameters used in our model, this threshold is quite low (mutants need only be 10% as fit as the wild-type virus in the absence of an immune response and about 15% in the presence of an immune response) and is determined by the basic reproductive number. Beyond this threshold, the fraction of breakthrough infections rises sharply as the fitness increases, and reaches an asymptotic value that is determined by the drug efficacy. The lower the drug efficacy, the higher the number of breakthrough infections. This is particularly concerning because there is some evidence that the efficacy of oseltamivir, the most commonly stockpiled antiviral , can be fairly low (30–80%) [42, 71]. Amantadine also appears to have a somewhat low efficacy in vitro, between 50–95% [10, 72], although this low value could be partly due to the emergence of drug-resistant mutants over the course of the infection [10, 12]. A further exacerbating factor in humans is the person-to-person variability of drug efficacy caused by individuals’ variability in pharmacokinetic parameters . This variability means that some people receiving the standard drug regimen will be receiving treatment at low efficacies, increasing the possibility of a breakthrough infection. The immune response helps to mitigate some of these problems, lowering the drug efficacy at which breakthrough infections rise to high levels. Unfortunately, even with the immune response, the typical efficacy of antivirals is below the efficacy needed to suppress breakthrough infections. These results suggest that early or preventative antiviral therapy is a risky proposition and that it might contribute to the emergence and spread of drug resistant mutants.
Our models present a simplified version of viral dynamics which help to elucidate some of the mechanisms driving the emergence of drug-resistant mutants during the course of a single infection. Our models show that the mechanism of drug action as well as the surface proteins present on the surface of the mutant virus both play a role in how quickly drug-resistant mutants will emerge and how many mutants will be produced over the course of the infection.