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Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

Overview of attention for article published in PLoS Computational Biology, September 2015
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Citations

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158 Dimensions

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mendeley
185 Mendeley
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1 CiteULike
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Title
Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance
Published in
PLoS Computational Biology, September 2015
DOI 10.1371/journal.pcbi.1004493
Pubmed ID
Authors

Daniel Nichol, Peter Jeavons, Alexander G. Fletcher, Robert A. Bonomo, Philip K. Maini, Jerome L. Paul, Robert A. Gatenby, Alexander R.A. Anderson, Jacob G. Scott

Abstract

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2-4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically 'steer' the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic-resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 113 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 185 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
France 2 1%
United Kingdom 1 <1%
Spain 1 <1%
Mexico 1 <1%
Unknown 177 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 21%
Researcher 32 17%
Student > Master 23 12%
Student > Bachelor 19 10%
Student > Doctoral Student 11 6%
Other 31 17%
Unknown 30 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 30%
Biochemistry, Genetics and Molecular Biology 23 12%
Medicine and Dentistry 20 11%
Mathematics 11 6%
Engineering 8 4%
Other 29 16%
Unknown 38 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 172. 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 05 December 2023.
All research outputs
#235,274
of 25,416,581 outputs
Outputs from PLoS Computational Biology
#158
of 8,977 outputs
Outputs of similar age
#2,894
of 280,256 outputs
Outputs of similar age from PLoS Computational Biology
#4
of 166 outputs
Altmetric has tracked 25,416,581 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,977 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 done particularly well, scoring higher than 98% 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 280,256 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 166 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.