Current options for predicting therapeutic response in chronically infected patients with hepatitis C virus genotype 1

Verónica Saludes Montoro, Vicente Ausina Ruiz, Elisa Martró Català

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Only about 50% of patients chronically infected with hepatitis C virus genotype 1 achieve a successful response to standard treatment with pegylated interferon-alfa and ribavirin. Moreover, the recently approved protease inhibitors will have to be administered together with these drugs. Consequently, predicting response to standard treatment, ideally before starting it, remains an important challenge. Although several baseline predictors of treatment failure have been described, including clinical and virological factors, none of them is able to provide reliable predictions at the individual level. In addition, the development of multivariate models combining several predictive factors has not yet yielded predictions with the requisite reliability for use in clinical practice. Therefore, further research is needed to improve predictive models and to describe new factors that would enable us to predict treatment outcome with greater reliability and reproducibility. The development of candidate selection algorithms that help clinicians to identify which patients could benefit from the new therapies on the basis of their chances of responding to standard therapy is of major interest for both patient well-being and healthcare expense. This review attempts to provide a view of the current options for predicting the response to pegylated interferon-alfa plus ribavirin therapy in patients chronically infected with hepatitis C virus genotype 1. © 2011 Elsevier España, S.L. All rights reserved.
Original languageEnglish
Pages (from-to)51-58
JournalEnfermedades Infecciosas y Microbiologia Clinica
Volume29
Issue numberSUPPL. 5
DOIs
Publication statusPublished - 1 Jan 2011

Keywords

  • Genotype 1
  • Hepatitis C virus
  • Multivariate models
  • Pegylated interferon alfa
  • Treatment response prediction

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