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Optimal Drug Synergy in Antimicrobial Treatments

Overview of attention for article published in PLoS Computational Biology, June 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

blogs
1 blog
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1 research highlight platform

Citations

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140 Dimensions

Readers on

mendeley
262 Mendeley
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5 CiteULike
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Title
Optimal Drug Synergy in Antimicrobial Treatments
Published in
PLoS Computational Biology, June 2010
DOI 10.1371/journal.pcbi.1000796
Pubmed ID
Authors

Joseph Peter Torella, Remy Chait, Roy Kishony

Abstract

The rapid proliferation of antibiotic-resistant pathogens has spurred the use of drug combinations to maintain clinical efficacy and combat the evolution of resistance. Drug pairs can interact synergistically or antagonistically, yielding inhibitory effects larger or smaller than expected from the drugs' individual potencies. Clinical strategies often favor synergistic interactions because they maximize the rate at which the infection is cleared from an individual, but it is unclear how such interactions affect the evolution of multi-drug resistance. We used a mathematical model of in vivo infection dynamics to determine the optimal treatment strategy for preventing the evolution of multi-drug resistance. We found that synergy has two conflicting effects: it clears the infection faster and thereby decreases the time during which resistant mutants can arise, but increases the selective advantage of these mutants over wild-type cells. When competition for resources is weak, the former effect is dominant and greater synergy more effectively prevents multi-drug resistance. However, under conditions of strong resource competition, a tradeoff emerges in which greater synergy increases the rate of infection clearance, but also increases the risk of multi-drug resistance. This tradeoff breaks down at a critical level of drug interaction, above which greater synergy has no effect on infection clearance, but still increases the risk of multi-drug resistance. These results suggest that the optimal strategy for suppressing multi-drug resistance is not always to maximize synergy, and that in some cases drug antagonism, despite its weaker efficacy, may better suppress the evolution of multi-drug resistance.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 262 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Switzerland 2 <1%
Sweden 2 <1%
Germany 2 <1%
United Kingdom 2 <1%
Belgium 2 <1%
Denmark 2 <1%
Canada 1 <1%
Mexico 1 <1%
Other 3 1%
Unknown 240 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 24%
Researcher 54 21%
Student > Master 36 14%
Student > Bachelor 33 13%
Student > Doctoral Student 12 5%
Other 34 13%
Unknown 31 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 93 35%
Biochemistry, Genetics and Molecular Biology 33 13%
Medicine and Dentistry 31 12%
Immunology and Microbiology 12 5%
Engineering 10 4%
Other 42 16%
Unknown 41 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 01 February 2012.
All research outputs
#4,836,169
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#3,864
of 8,960 outputs
Outputs of similar age
#19,750
of 105,114 outputs
Outputs of similar age from PLoS Computational Biology
#24
of 60 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 105,114 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.