Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using...

Area of Science:

• Virology
• Computational Biology
• Genetics

Background:

• Direct-acting antivirals (DAAs) have revolutionized hepatitis C virus (HCV) treatment, but resistance-associated variants (RAVs) can impact treatment efficacy.
• Interpreting genotypic drug resistance in HCV is complex, particularly for patients requiring retreatment after DAA failure.

Purpose of the Study:

• To apply Bayesian network (BN) learning to HCV sequence data for elucidating resistance pathways against NS3/4A protease inhibitors (PIs) in HCV subtypes 1a and 1b.
• To identify novel RAVs and baseline variants associated with PI treatment response and failure.

Main Methods:

• Utilized the 'Rega-BN' tool chain for associative analyses on HCV sequence data from PI-naïve and PI-experienced patients.
• Compared NS3 sequences from patients who cleared the virus versus those who failed PI therapy.

Main Results:

• Identified NS3 substitutions R155K and V36M as major and minor RAVs associated with PI exposure in HCV1a.
• Newly identified NS3 variant 174H as potentially linked to PI resistance.
• Found NS3 baseline variant 67S predisposes to treatment failure, while variant 72I is associated with treatment success.

Conclusions:

• Bayesian network analysis provides valuable insights into HCV resistance pathways against PIs.
• This approach can identify baseline polymorphisms that predict treatment failure, aiding in the characterization of RAVs.
• The method holds potential for improving retreatment strategies by better understanding drug resistance in HCV.