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A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
A Hepatitis C Virus Infection Model with Time-Varying Drug Effectiveness: Solution and Analysis
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003769
Pubmed ID
Authors

Jessica M. Conway, Alan S. Perelson

Abstract

Simple models of therapy for viral diseases such as hepatitis C virus (HCV) or human immunodeficiency virus assume that, once therapy is started, the drug has a constant effectiveness. More realistic models have assumed either that the drug effectiveness depends on the drug concentration or that the effectiveness varies over time. Here a previously introduced varying-effectiveness (VE) model is studied mathematically in the context of HCV infection. We show that while the model is linear, it has no closed-form solution due to the time-varying nature of the effectiveness. We then show that the model can be transformed into a Bessel equation and derive an analytic solution in terms of modified Bessel functions, which are defined as infinite series, with time-varying arguments. Fitting the solution to data from HCV infected patients under therapy has yielded values for the parameters in the model. We show that for biologically realistic parameters, the predicted viral decay on therapy is generally biphasic and resembles that predicted by constant-effectiveness (CE) models. We introduce a general method for determining the time at which the transition between decay phases occurs based on calculating the point of maximum curvature of the viral decay curve. For the parameter regimes of interest, we also find approximate solutions for the VE model and establish the asymptotic behavior of the system. We show that the rate of second phase decay is determined by the death rate of infected cells multiplied by the maximum effectiveness of therapy, whereas the rate of first phase decline depends on multiple parameters including the rate of increase of drug effectiveness with time.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Italy 1 4%
Germany 1 4%
Unknown 25 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 32%
Student > Ph. D. Student 4 14%
Student > Bachelor 4 14%
Other 2 7%
Student > Doctoral Student 1 4%
Other 5 18%
Unknown 3 11%
Readers by discipline Count As %
Medicine and Dentistry 4 14%
Agricultural and Biological Sciences 4 14%
Mathematics 3 11%
Physics and Astronomy 2 7%
Computer Science 2 7%
Other 7 25%
Unknown 6 21%
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 23 August 2014.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#8,570
of 8,964 outputs
Outputs of similar age
#207,439
of 241,673 outputs
Outputs of similar age from PLoS Computational Biology
#151
of 163 outputs
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