A multi-variant, viral dynamic model of genotype 1 HCV to assess the in vivo evolution of protease-inhibitor resistant variants

Variants resistant to compounds specifically targeting HCV are observed in clinical trials. A multi-variant viral dynamic model was developed to quantify the evolution and in vivo fitness of variants in subjects dosed wi
Variants resistant to compounds specifically targeting HCV are observed in clinical trials. A multi-variant viral dynamic model was developed to quantify the evolution and in vivo fitness of variants in subjects dosed with monotherapy of an HCV protease inhibitor, telaprevir. Variant fitness was estimated using a model in which variants were selected by competition for shared limited replication space. Fitness was represented in the absence of telaprevir by different variant production rate constants and in the presence of telaprevir by additional antiviral blockage by telaprevir. Model parameters, including rate constants for viral production, clearance, and effective telaprevir concentration, were estimated from 1) plasma HCV RNA levels of subjects before, during, and after dosing, 2) post-dosing prevalence of plasma variants from subjects, and 3) sensitivity of variants to telaprevir in the HCV replicon. The model provided a good fit to plasma HCV RNA levels observed both during and after telaprevir dosing, as well as to variant prevalence observed after telaprevir dosing. After an initial sharp decline in HCV RNA levels during dosing with telaprevir, HCV RNA levels increased in some subjects. The model predicted this increase to be caused by pre-existing variants with sufficient fitness to expand once available replication space increased due to rapid clearance of wild-type (WT) virus. The average replicative fitness estimates in the absence of telaprevir ranged from 1% to 68% of WT fitness. Compared to the relative fitness method, the in vivo estimates from the viral dynamic model corresponded more closely to in vitro replicon data, as well as to qualitative behaviors observed in both on-dosing and long-term post-dosing clinical data. The modeling fitness estimates were robust in sensitivity analyses in which the restoration dynamics of replication space and assumptions of HCV mutation rates were varied.
…show moreshow less

Download full text files

Export metadata

  • Export Bibtex
  • Export RIS

Additional Services

    Share in Twitter Search Google Scholar
Metadaten
Author:Bambang S. Adiwijaya, Eva Herrmann, Brian Hare, Tara Kieffer, Chao Lin, Ann D. Kwong, Varun Garg, John C. R. Randle, Christoph Sarrazin, Stefan Zeuzem, Paul R. Caron
URN:urn:nbn:de:hebis:30-114266
DOI:http://dx.doi.org/10.1371/journal.pcbi.1000745
ISSN:1553-7358
Parent Title (English):PLoS Computational Biology
Document Type:Article
Language:English
Date of Publication (online):2011/09/02
Year of first Publication:2010
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2011/09/02
Note:
Copyright: © 2010 Adiwijaya et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Source:PLoS Computational Biology 6(4): e1000745. doi: 10.1371/journal.pcbi.1000745
HeBIS PPN:275131041
Institutes:Medizin
Dewey Decimal Classification:570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Sondersammelgebiets-Volltexte
Licence (German):License LogoCreative Commons - Namensnennung 3.0

$Rev: 11761 $