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Analysis of Hepatitis C Virus Decline during Treatment with the Protease Inhibitor Danoprevir Using a Multiscale Model

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posted on 29.03.2016 by Libin Rong, Jeremie Guedj, Harel Dahari, Daniel J. Jr Coffield, Micha Levi, Patrick Smith
The current paradigm for studying hepatitis C virus (HCV) dynamics in patients utilizes a standard viral dynamic model that keeps track of uninfected (target) cells, infected cells, and virus. The model does not account for the dynamics of intracellular viral replication, which is the major target of direct-acting antiviral agents (DAAs). Here we describe and study a recently developed multiscale age-structured model that explicitly considers the potential effects of DAAs on intracellular viral RNA production, degradation, and secretion as virus into the circulation. We show that when therapy significantly blocks both intracellular viral RNA production and virus secretion, the serum viral load decline has three phases, with slopes reflecting the rate of serum viral clearance, the rate of loss of intracellular viral RNA, and the rate of loss of intracellular replication templates and infected cells, respectively. We also derive analytical approximations of the multiscale model and use one of them to analyze data from patients treated for 14 days with the HCV protease inhibitor danoprevir. Analysis suggests that danoprevir significantly blocks intracellular viral production (with mean effectiveness 99.2%), enhances intracellular viral RNA degradation about 5-fold, and moderately inhibits viral secretion (with mean effectiveness 56%). The multiscale model can be used to study viral dynamics in patients treated with other DAAs and explore their mechanisms of action in treatment of hepatitis C.

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Funding

Portions of this work were performed under the auspices of the U.S. Department of Energy under contract DE-AC52-06NA25396 and supported by NSF grants DMS-1122290 and PHY-1125915, NIH grants R56/R01-AI078881, P20-GM103452, AI028433, R34-HL109334 and OD011095, the University of Illinois Walter Payton Liver Center GUILD, and Roche, Inc.

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Publisher Statement

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. © 2013 by Name of Public Library of Science , PLoS Computational Biology

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Public Library of Science

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en_US

issn

1553-7358

Issue date

01/03/2013

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