Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza

Abstract

Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can reduce the severity of SBIs, but many have limited efficacy or cause adverse effects. Thus, new treatment strategies are needed. Kinetic models describing the infection process can help determine optimal therapeutic targets, the time scale on which a drug will be most effective, and how infection dynamics will change under therapy. To understand how different therapies perturb the dynamics of influenza infection and bacterial coinfection and to quantify the benefit of increasing a drug’s efficacy or targeting a different infection process, I analyzed data from mice treated with an antiviral, an antibiotic, or an immune modulatory agent with kinetic models. The results suggest that antivirals targeting the viral life cycle are most efficacious in the first 2 days of infection, potentially because of an improved immune response, and that increasing the clearance of infected cells is important for treatment later in the infection. For a coinfection, immunotherapy could control low bacterial loads with as little as 20 % efficacy, but more effective drugs would be necessary for high bacterial loads. Antibiotics targeting bacterial replication and administered 10 h after infection would require 100 % efficacy, which could be reduced to 40 % with prophylaxis. Combining immunotherapy with antibiotics could substantially increase treatment success. Taken together, the results suggest when and why some therapies fail, determine the efficacy needed for successful treatment, identify potential immune effects, and show how the regulation of underlying mechanisms can be used to design new therapeutic strategies.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-016-9494-9) contains supplementary material, which is available to authorized users.

A Critical, Nonlinear Threshold Dictates Bacterial Invasion and Initial Kinetics During Influenza

“…These results are consistent with those presented here, where a decrease in dose and thus an increase in the distance from the threshold results in faster clearance and resolution in some mice. Thus, similar outcomes will manifest through therapeutically decreasing AM depletion or decreasing bacterial loads, however the therapeutic requirement may change because of the nonlinearity of the relationship28. A more effective therapeutic approach may be to use antibiotics, which limit bacterial replication, either prophylactically or as an early treatment together with rGM-CSF28.…”

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

“…It also begins to reveal the relationship between these rates and the strength needed to induce a change in the dynamics (eg, with drug therapy or coinfection). Further investigating how changing the rates affects outcome, for example, through sensitivity analysis, has generated predictions about the response to therapy or coinfection with other pathogens . Collectively, these types of analyses reveal aspects of influenza biology that are not immediately available from the experimental or clinical data alone.…”

“…This information suggests that the behavior can be predicted for any bacteria‐aMΦ pairing, which is ideal. It also aids in the interpretation of bacterial load data and allows for exploration of therapies that manipulate bacterial loads (eg, antibiotics) and aMΦs (eg, immunotherapy or antivirals) …”

“…Drug effectiveness is equal to 1 when the drug is 100% effective and 0 when the drug is inactive or absent. Model simulations suggest that targeting virus infection (β) would yield similar results as targeting virus production and that increased efficacy would be needed for an antiviral that improves clearance of free virus ( c ) . Unsurprisingly, a therapy designed to improve the timing and/or rate of infected cell clearance (δ) could result in faster resolution …”

“…A secondary effect of NAI therapy was detected in one study that assessed viral load kinetics when therapy was initiated either early or late in the infection . An extra term () in the target cell equation together with the reduction in the rate of virus production () was needed in the model to simultaneously capture the data: , where is the efficacy of the antiviral in reducing the number of cells susceptible to infection. The requirement of the term in the model suggests that the antiviral limits the number of cells that can be infected.…”