Prospects for personalizing antiviral therapy for hepatitis C virus with pharmacogenetics

John E Tavis1, Maureen J Donlin, Rajeev Aurora

  • 1Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, Saint Louis, MO 63104, USA. tavisje@slu.edu.

Genome Medicine
|February 25, 2011
PubMed

Insights

Predicting hepatitis C treatment success is possible using patient and virus genetic markers. Understanding these genetic factors can personalize therapy, reducing ineffective treatments and improving outcomes for chronic hepatitis C virus infection.

Area of Science:

  • Hepatology and Virology
  • Immunogenetics
  • Pharmacogenomics

Background:

  • Chronic hepatitis C virus (HCV) infection is a significant global health issue causing liver disease.
  • Current interferon-alpha (IFNα) and ribavirin therapy has a high failure rate (up to 50%), necessitating predictive biomarkers.
  • Host and viral genetic factors significantly influence treatment response to IFNα-based therapies.

Purpose of the Study:

  • To review advances in understanding host and viral genetic variations impacting HCV treatment outcomes.
  • To highlight the potential of pharmacogenetics in personalizing HCV therapy and avoiding futile treatments.
  • To discuss the integration of genetic markers for predicting response to current and emerging HCV therapies.

Main Methods:

  • Review of scientific literature on host and viral genetic factors in HCV treatment.
  • Analysis of associations between human gene polymorphisms (e.g., IL28B, HLA, cytokines) and treatment outcomes.
  • Examination of HCV genetic variations (genotype and intragenotypic differences) influencing therapy response.

Main Results:

  • Polymorphisms in the IL28B gene are strongly associated with treatment outcomes.
  • Variations in human leukocyte antigen and cytokine genes also correlate with treatment success.
  • HCV genotype 1 shows lower sensitivity to therapy compared to genotypes 2 and 3, with intragenotypic differences also playing a role.

Conclusions:

  • Pharmacogenetic biomarkers, including host and viral genetic profiles, are crucial for predicting IFN-based HCV therapy outcomes.
  • Integrating viral resistance markers with patient genetic data will enhance predictive accuracy.
  • Future HCV treatment prediction will incorporate genetic markers for direct-acting antiviral agents.

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