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Predictors of Hepatitis B Cure Using Gene Therapy to Deliver DNA Cleavage Enzymes: A Mathematical Modeling Approach

Overview of attention for article published in PLoS Computational Biology, July 2013
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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 (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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3 X users
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31 patents

Citations

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38 Dimensions

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Title
Predictors of Hepatitis B Cure Using Gene Therapy to Deliver DNA Cleavage Enzymes: A Mathematical Modeling Approach
Published in
PLoS Computational Biology, July 2013
DOI 10.1371/journal.pcbi.1003131
Pubmed ID
Authors

Joshua T. Schiffer, Dave A. Swan, Daniel Stone, Keith R. Jerome

Abstract

Most chronic viral infections are managed with small molecule therapies that inhibit replication but are not curative because non-replicating viral forms can persist despite decades of suppressive treatment. There are therefore numerous strategies in development to eradicate all non-replicating viruses from the body. We are currently engineering DNA cleavage enzymes that specifically target hepatitis B virus covalently closed circular DNA (HBV cccDNA), the episomal form of the virus that persists despite potent antiviral therapies. DNA cleavage enzymes, including homing endonucleases or meganucleases, zinc-finger nucleases (ZFNs), TAL effector nucleases (TALENs), and CRISPR-associated system 9 (Cas9) proteins, can disrupt specific regions of viral DNA. Because DNA repair is error prone, the virus can be neutralized after repeated cleavage events when a target sequence becomes mutated. DNA cleavage enzymes will be delivered as genes within viral vectors that enter hepatocytes. Here we develop mathematical models that describe the delivery and intracellular activity of DNA cleavage enzymes. Model simulations predict that high vector to target cell ratio, limited removal of delivery vectors by humoral immunity, and avid binding between enzyme and its DNA target will promote the highest level of cccDNA disruption. Development of de novo resistance to cleavage enzymes may occur if DNA cleavage and error prone repair does not render the viral episome replication incompetent: our model predicts that concurrent delivery of multiple enzymes which target different vital cccDNA regions, or sequential delivery of different enzymes, are both potentially useful strategies for avoiding multi-enzyme resistance. The underlying dynamics of cccDNA persistence are unlikely to impact the probability of cure provided that antiviral therapy is given concurrently during eradication trials. We conclude by describing experiments that can be used to validate the model, which will in turn provide vital information for dose selection for potential curative trials in animals and ultimately humans.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 3%
United States 1 1%
Colombia 1 1%
Switzerland 1 1%
Unknown 66 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 34%
Student > Ph. D. Student 14 20%
Professor 6 8%
Student > Bachelor 6 8%
Other 4 6%
Other 10 14%
Unknown 7 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 46%
Biochemistry, Genetics and Molecular Biology 17 24%
Medicine and Dentistry 8 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Immunology and Microbiology 1 1%
Other 3 4%
Unknown 8 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 07 March 2023.
All research outputs
#3,358,150
of 25,461,852 outputs
Outputs from PLoS Computational Biology
#2,958
of 8,981 outputs
Outputs of similar age
#27,706
of 206,685 outputs
Outputs of similar age from PLoS Computational Biology
#27
of 99 outputs
Altmetric has tracked 25,461,852 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,981 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 66% 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 206,685 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 86% of its contemporaries.
We're also able to compare this research output to 99 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 72% of its contemporaries.