Current treatments for chronic hepatitis C virus (HCV) infection have severe side effects and fail in about half of treated patients, including people living with HIV. However, according to a ScienceDaily report, researchers at Saint Louis University have developed an approach that may predict the outcome of therapy, raising the possibility of a test to predict whether treatment will be effective before it is started.
The SLU research team, led by John Tavis, PhD, used a mathematical formula to analyze variations in the genome-wide amino-acid sequences of viruses isolated from HCV-infected patients before they underwent treatment. Using this approach, networks of covariation were found to associate with specific responses of the patients to treatment.
The authors suggest that these results have implications for developing a test to predict how an individual infected with HCV will respond to treatment and may even help identify targets for new antiviral drugs.
"If the test shows the treatment won't work, physicians could counsel against interferon-based therapy, avoiding tens of thousands of dollars in expenses and painful side effects for the patient," Tavis said. "It's wasteful to spend millions of dollars on medicine that won't work."
