Chronic Hepatitis C Clinical Trial
Official title:
Analysis of Gene Expression of PBMC in the Hepatitis C Virus Genotype 1b-infected Patients During Peg-interferon-α Plus Ribavirin Combination Therapy
Our previous collaborative studies has developed a molecular diagnosis tool, which is
characterized with a prediction model consisting single nucleotide polymorphisms (SNPs), for
assessing the efficacy of interferon combined therapy for chronic hepatitis C (CHC) patients
prior to treatment.
Aims of this project:
1. To analyze and validate the gene expression profiling dependent of treatment response
to peg-interferon-α plus ribavirin combination therapy in CHC genotype-1 patients.
2. To select the candidate genes and establish a monitoring model assessing the efficacy
of interferon treatment.
A combination of pegylated-interferon (PEG-IFN) with ribavirin has been the choice for
treating chronic hepatitis C (CHC) patients. In addition to the high cost, the treatment
takes 6 to 12 months and often brings significant adverse reactions to some patients. It
would therefore be beneficial to include a pre-treatment evaluation and post-treatment
monitoring system to maximize the efficacy of CHC therapy. We have established a molecular
diagnosis tool for assessing the efficacy of interferon combined therapy for CHC patients
prior to treatment. This diagnostic tool is characterized with a prediction model consisting
single nucleotide polymorphisms (SNPs) strongly associated with interferon efficacy in
treating CHC patients. The prediction model has been validated by hepatitis C samples in
Taiwan, and it achieved 80 % accuracy, higher than the current efficacy rates. Supplemented
with the viral genotype information would further increase the prediction accuracy to 85%.
In this project, we aim to analyze patients' gene expression profiling during the treatment.
The current study will focus on 30 PEG-IFN and ribavirin treated patients with HCV genotype
1 infection. After the responding status of those patients has been confirmed, we will
compare gene expression profiling among the different responses before and during treatment.
By using this information, we can properly select the candidate genes, and establish a
monitoring model with these biomarkers. This monitoring model can thus be applied to assess
the efficacy of PEG-IFN plus ribavirin combination treatment.
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Allocation: Non-Randomized, Endpoint Classification: Safety/Efficacy Study, Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Diagnostic
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