Patterns of Early Hepatitis C Virus Decline Predict the Outcome of Interferon Therapy (sIFN-pred2)

Hepatitis C virus (HCV) infection rate in China is about 3%, which means about 30 million patients. Combination therapy of ribavirin and interferons (IFN) is the standard clinical treatment of HCV chronical infections. However, overall rate of sustained virological response (SVR) still do not exceed 60% even with ribavirin and peg-IFN. Due to several virus- and patient-related factors, treatment is even less successful in certain populations, especially in HCV genotype 1 infection. Thus the standard therapy duration is optimized according to the virus genotype in the clinical practice. Nowadays, two direct antiviral agents (DAAs) have been approved by Food and Drug Administration (FDA) of USA this year, which increases the SVR rate. However, high price, side effects and long duration render people to hesitate about the addition of the third drug in the traditional prescription. Predicting the outcome of traditional therapy is the cornerstone of the personalized therapy for HCV infected patients. In order to obtain an accurate prediction, different methods have been tried. Several indicators have been suggested to predict the final treatment outcomes. Rapid Virus Response (RVR), which indicates the non-detectable virus at the forth week since therapy starts, has been used to predict the final treatment outcome.Other indicators, including virus genotype, host genotype of IL-28B, human race and interferon stimulated genes (ISG) expression have also been shown to relate to and be able to predict the treatment outcomes to some extent. Here the investigators propose that the HCV virus dynamics analysis will give a more precise prediction for the therapy outcome. The general idea is that blood HCV titration data is obtained continuously in the early treatment period (first 2 weeks) of the patients who have strictly followed the therapy method. These titration data will be used to draw virus dynamics curve and calculate the corresponding parameters individually. The parameter(s) that can distinguish patients who reach the therapy evaluation standard from those who failed to reach the evaluation standard will be selected out, and such parameter(s) may be used to predict the therapy outcome of a new patient in the early stage of his/her treatment.