December 1, 2013

Predictors of the therapeutic response in hepatitis C. A 2013 update

Clin Res Hepatol Gastroenterol. 2013 Nov 20. pii: S2210-7401(13)00177-0. doi: 10.1016/j.clinre.2013.08.003. [Epub ahead of print]

Arnaud C, Trépo C, Petit MA.

Inserm U1052/CNRS UMR5286, Centre de Recherche en Cancérologie de Lyon (CRCL), 151, Cours-Albert-Thomas, 69424 Lyon cedex 03, France; Université Claude-Bernard Lyon 1, 69000 Lyon, France.


Chronic hepatitis C is a major cause of cirrhosis and hepatocellular carcinoma. Current therapy based on pegylated-interferon-α (PEG-IFN) and ribavirin (RBV) combination has limited efficacy and is poorly tolerated. Disease progression is highly variable and pre-therapeutic prediction of response to treatment remains difficult. Although viral kinetics proved most useful to monitor duration of therapy, other predictors would be helpful to identify patients with the best chance of subsequent treatment response prior initiation of antiviral therapy (double or triple therapy). The predictive power of IL28B polymorphism is well-recognized and has become the reference biomarker for clinicians in patients treated with double therapy. The combination of serum IP-10 and IL28B SNPs increases predictive value of treatment response. Recently, anti-E1E2 antibodies appear to closely correlate with therapeutic outcome and predict the complete elimination of HCV. They may represent a new relevant prognostic biomarker of double therapy response. Since the introduction of triple therapy including protease inhibitors (telaprevir/boceprevir), the major priority is to help patients who failed on double therapy, and there is now an urgent need for robust pre-therapeutic predictors of response to better select the patients to treat. Indeed, the relevance of IL28B polymorphism and IP-10 serum concentration are limited in triple therapy. Many new drugs are currently under investigation and there is hope that effective and well-tolerated IFN-free regimens may become a part of future therapy. In this context, this will help to identify the most powerful predictive marker and/or to assess the benefit of anti-E1E2 in decision to treat.

Copyright © 2013 Elsevier Masson SAS. All rights reserved.

PMID: 24268305 [PubMed - as supplied by publisher]


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