Editors' ChoiceChronic Viral Hepatitis

The Holy Grail of Hepatitis C Treatment

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Science Translational Medicine  11 Jan 2012:
Vol. 4, Issue 116, pp. 116ec6
DOI: 10.1126/scitranslmed.3003669

Hepatitis C is an infectious disease of the liver caused by the hepatitis C virus (HCV). Most patients infected with HCV are asymptomatic, but chronic infection can lead to scarring of the liver (cirrhosis), liver failure, or liver cancer. Over 100 million people worldwide are estimated to suffer from chronic hepatitis C infection, an enormous health and economic burden. The current standard of treatment for chronic hepatitis C infection is ribavirin plus pegylated interferon (PEG-IFN). Recently, telaprevir and boceprevir, two direct-acting antiretroviral protease inhibitors, have been approved for clinical use in combination with the above regimen. However, it has remained difficult to predict sustained virological response (SVR) in individual patients, and these treatments have substantial side effects and costs. Now, Ochi et al. identify a set of risk factors for SVR that form a predictive model for hepatitis C treatment response.

Previous studies have identified several potential predictive factors for HCV treatment response, including clinical factors such as age, laboratory values such as viral load and liver function test, and genetic factors such as IL28B (interleukin 28B gene) single-nucleotide polymorphism genotype. This study, however, is a major step forward in combining multiple predictive factors and establishing a powerful prediction model with high accuracy. Using a series of 640 Japanese hepatitis C patients treated with PEG-IFN and ribavirin, the authors identified univariate factors that could differentiate SVRs, transient responders, and nonresponders. Then they used powerful regression analysis to formulate a multivariate predictive model that included the following independent predictors of SVR: age, genotype, initial viral load, aspartate aminotransferase/alanine aminotransferase ratio, rs8099917 TT genotype (IL28B), and alpha-fetoprotein. Most importantly, the final model had excellent predictive power, with an area under the receiver–operating characteristic curve (AUC) of 0.85.

Although this study did not identify any novel predictors of SVR, it put together an elegant and simple, yet powerful, prediction model from readily available clinical, laboratory, and genetic information. If validated in other patient populations and in treatment settings that involve new protease inhibitors, this model may become the standard to select the patients who will actually respond to the rather toxic treatment. We may one day adopt the concept of personalized medicine for this highly prevalent condition.

H. Ochi et al., Toward the establishment of a prediction system for the personalized treatment of chronic hepatitis C. J. Infect. Dis. 205, 204–210 (2012). [PubMed]

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