[Current options for predicting therapeutic response in chronically infected patients with hepatitis C virus genotype 1].
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.
Servicio de Microbiología, Fundación Instituto de Investigación en Ciencias de la Salud Germans Trias y Pujol, Hospital Universitario Germans Trias y Pujol, Badalona, Universidad Autónoma de Barcelona, Barcelona, España.
Verónica
V
Saludes Montoro
Vicente
V
Ausina Ruiz
Elisa
E
Martró Català
D000465:Algorithms; D000998:Antiviral Agents / Q000627:therapeutic use*; D024882:Drug Resistance, Viral / Q000235:genetics*; D005544:Forecasting; D014644:Genetic Variation; D005838:Genotype; D016174:Hepacivirus / Q000145:classification / Q000235:genetics / Q000472:pathogenicity*; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000453:epidemiology / Q000821:virology; D054884:Host-Pathogen Interactions / Q000235:genetics; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000008:administration & dosage / Q000627:therapeutic use; D007372:Interferons; D007378:Interleukins / Q000235:genetics; D008954:Models, Biological; D015999:Multivariate Analysis; D011092:Polyethylene Glycols / Q000008:administration & dosage / Q000627:therapeutic use; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D011994:Recombinant Proteins / Q000008:administration & dosage / Q000627:therapeutic use; D012254:Ribavirin / Q000008:administration & dosage / Q000627:therapeutic use; D016896:Treatment Outcome; D014764:Viral Proteins / Q000235:genetics; D000095022:Interferon Lambda
D000998:Antiviral Agents; D000077190:Interferon alpha-2; D016898:Interferon-alpha; D007372:Interferons; D007378:Interleukins; D011092:Polyethylene Glycols; D011994:Recombinant Proteins; D012254:Ribavirin; D014764:Viral Proteins; C000624353:interferon-lambda, human; D000095022:Interferon Lambda; C417083:peginterferon alfa-2b; C100416:peginterferon alfa-2a