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

Overview of attention for article published in Journal of Pharmacokinetics and Pharmacodynamics, September 2016
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Title
Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza
Published in
Journal of Pharmacokinetics and Pharmacodynamics, September 2016
DOI 10.1007/s10928-016-9494-9
Pubmed ID
Authors

Amber M. Smith

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 13%
Student > Doctoral Student 2 13%
Lecturer > Senior Lecturer 1 7%
Other 1 7%
Lecturer 1 7%
Other 5 33%
Unknown 3 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 20%
Medicine and Dentistry 2 13%
Immunology and Microbiology 2 13%
Mathematics 2 13%
Pharmacology, Toxicology and Pharmaceutical Science 1 7%
Other 2 13%
Unknown 3 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 September 2016.
All research outputs
#17,285,668
of 25,374,647 outputs
Outputs from Journal of Pharmacokinetics and Pharmacodynamics
#328
of 477 outputs
Outputs of similar age
#215,444
of 330,838 outputs
Outputs of similar age from Journal of Pharmacokinetics and Pharmacodynamics
#4
of 5 outputs
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So far Altmetric has tracked 477 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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