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Foretelling Hepatitis C Drug Success

December 30, 2008

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By utilizing a mathematical formula on the specific genetics of an individual's Hepatitis C infection, doctors may be able to predict whether or not treatment will be successful.

Study may predict if hepatitis C drugs will work

December 23, 2008

By Julie Steenhuysen

CHICAGO (Reuters) - Doctors hope to be able to better predict which patients will respond to traditional treatment for the hepatitis C virus using a new method for identifying slight variances in the virus' genetic makeup.

U.S. researchers said on Monday that the technique may prove useful for other viruses such as HIV as well. The finding could be used to develop a test that would analyze a patient's specific virus strain before treatment was started.

A team at Saint Louis University in Missouri analyzed genetic patterns of the virus in patients infected with Hepatitis C to see if they could tell why many patients fail to respond to standard treatment with pegylated-interferon and ribavirin.

The year-long therapy activates the body's natural defenses against viruses, but patients often feel as though they have a bad case of influenza. Only about half of the people who suffer through the treatment actually respond.

"This is a very difficult therapy to take. It's really hard on the patient," said John Tavis, a professor of molecular and microbiology at Saint Louis University, whose study appears in the Journal of Clinical Investigation.

"If you can identify those patients who aren't going to respond anyways because they've got a strain that is highly resistant to the drug, then you just don't treat those patients and you save them $20,000 to $30,000 in medical bills just from drugs alone -- not to mention the side effects," Tavis said in a telephone interview.

He and colleagues studied the ribonucleic acid or RNA chains of the hepatitis C virus, looking for patterns that would explain why some people responded to the treatment while others did not.

Using a math formula, they zeroed in on a specific pattern of changes called "covariance networks" that differed depending on whether the drug worked. And these patterns proved to be a strong indicator of whether the virus was especially resistant to therapy.

"What we found will allow a doctor to predict whether or not a medication will work in a patient," Tavis said in a statement.

The finding also may have implications for other types of RNA viruses, such as human immunodeficiency virus or HIV or the influenza virus.

"It's a pretty easy process. The algorithm can be applied fairly quickly," he said. Whether or not it turns up a pattern that will be useful is less clear, he said.

Hepatitis C is a blood-borne liver disease that can lead to chronic liver disease, liver cancer, cirrhosis and death. The virus affects an estimated 3.2 million people in the United States alone and some 170 million worldwide.

Pegylated interferon brands include Roche Holding AG's Pegasys and Schering-Plough Corp's Pegintron.

(Editing by Cynthia Osterman)

© Thomson Reuters 2008 All rights reserved.

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URL for Article Source:
http://uk.reuters.com/article/healthNewsMolt/idUKTRE4BM00020081223

Posted by Editors at December 30, 2008 3:16 PM