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J Theor Biol. 2015 Oct 7;382:91-8. doi: 10.1016/j.jtbi.2015.06.041. Epub 2015 Jul 8.

Prediction of long-term treatment outcome in HCV following 24 day PEG-IFN alpha-2b therapy using population pharmacokinetic-pharmacodynamic mixture modeling and classification analysis.

Author information

1
Institute for Systems Biology SPb, Moscow, Russia.
2
Xenologiq Ltd. Unit 43, Canterbury Innovation Centre, University Road. Canterbury, Kent.CT2 7FG, UK.
3
Leiden Academic Centre for Drug Research (LACDR), Systems Pharmacology, 2300 RA Leiden, The Netherlands; Xenologiq Ltd. Unit 43, Canterbury Innovation Centre, University Road. Canterbury, Kent.CT2 7FG, UK.
4
Institute for Systems Biology SPb, Moscow, Russia. Electronic address: karelina@insysbio.ru.

Abstract

Mathematical models have been widely used for understanding the dynamics of the hepatitis C virus (HCV). We propose a method to predict final clinical outcome for 24 HIV-HCV - coinfected patients with the help of a mathematical model based on the first two weeks of PEG-IFN therapy. Applying a pharmacokinetic-pharmacodynamic (PKPD) approach, together with mixture models, to the adapted model of viral dynamics developed by Neumann et al., we have analyzed the influence of PEG-IFN on the kinetics and interaction of target cells, infected cells and virus mRNA. It was found that PEG-IFN pharmacokinetic parameters were similar in sustained virological responders and nonresponders, while the plasma PEG-IFN concentration that decreases HCV production by 50% (EC50) and the rate of infected cell death were different. The treatment outcome depended mainly on the initial viral mRNA concentration and the rate of infected cell death. The population PKPD approach with a mixture model enabled the determination of individual PKPD parameters and showed high sensitivity (93.5%) and specificity (97.4%) for the prediction of the treatment outcome.

KEYWORDS:

HCV; Mixture modeling; PKPD modeling

PMID:
26163367
DOI:
10.1016/j.jtbi.2015.06.041
[Indexed for MEDLINE]

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