Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naive and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naive patients who cleared the virus during PI therapy and from PI-naive patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach. (c) 2017 Elsevier B.V. All rights reserved.

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

Cento, Valeria;
2017

Abstract

Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naive and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naive patients who cleared the virus during PI therapy and from PI-naive patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach. (c) 2017 Elsevier B.V. All rights reserved.
Bayesian network learning
Drug resistance
HCV
NS3/4A protease inhibitors
Amino Acid Substitution
Antiviral Agents
Bayes Theorem
Carrier Proteins
Cohort Studies
Databases, Genetic
Drug Resistance, Viral
Europe
Female
Genotype
Hepacivirus
Hepatitis C, Chronic
Humans
Intracellular Signaling Peptides and Proteins
Male
Middle Aged
Mutation, Missense
Oligopeptides
Proline
Protease Inhibitors
RNA, Viral
Viral Nonstructural Proteins
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/65785
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