Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems

2017 ◽  
Vol 41 (10) ◽  
Author(s):  
Letícia M. Raposo ◽  
Flavio F. Nobre
Keyword(s):  
Ensemble Classifiers ◽  
Genotypic Resistance ◽  
Rule Based ◽  
Hiv 1 ◽  
Interpretation Systems

scholarly journals sierra-local: A lightweight standalone application for secure HIV-1 drug resistance prediction

2018 ◽  
Author(s):  
Jasper C Ho ◽  
Garway T Ng ◽  
Mathias Renaud ◽  
Art FY Poon
Keyword(s):  
Clinical Management ◽  
Data Provenance ◽  
Genotypic Resistance ◽  
Web Based ◽  
Network Connection ◽  
Legal Obligations ◽  
Drug Resistance Prediction ◽  
Development And Validation ◽  
Hiv 1 ◽  
Interpretation Systems

AbstractGenotypic resistance interpretation systems for the prediction and interpretation of HIV-1 antiretroviral resistance are an important part of the clinical management of HIV-1 infection. Current interpretation systems are generally hosted on remote webservers that enable clinical laboratories to generate resistance predictions easily and quickly from patient HIV-1 sequences encoding the primary targets of modern antiretroviral therapy. However they also potentially compromise a health provider’s ethical, professional, and legal obligations to data security, patient information confidentiality, and data provenance. Furthermore, reliance on web-based algorithms makes the clinical management of HIV-1 dependent on a network connection. Here, we describe the development and validation of sierra-local, an open-source implementation of the Stanford HIVdb genotypic resistance interpretation system for local execution, which aims to resolve the ethical, legal, and infrastructure issues associated with remote computing. This package reproduces the HIV-1 resistance scoring by the web-based Stanford HIVdb algorithm with a high degree of concordance (99.997%) and a higher level of performance than current methods of accessing HIVdb programmatically.

scholarly journals Discordance between Etravirine Phenotype and Genotype-Based Predicted Phenotype for Subtype C HIV-1 from First-Line Antiretroviral Therapy Failures in South Africa

2020 ◽  
Vol 64 (5) ◽  
Author(s):  
Kevin D. McCormick ◽  
Kerri J. Penrose ◽  
Chanson J. Brumme ◽  
P. Richard Harrigan ◽  
Raquel V. Viana ◽  
...  
Keyword(s):  
Virologic Failure ◽  
Transcriptase Inhibitor ◽  
Subtype C ◽  
Genotypic Resistance ◽  
Low Level ◽  
Nnrti Resistance ◽  
Subtype B ◽  
Treatment Naïve ◽  
Hiv 1 ◽  
Interpretation Systems

ABSTRACT Etravirine (ETR) is a nonnucleoside reverse transcriptase inhibitor (NNRTI) used in treatment-experienced individuals. Genotypic resistance test-interpretation systems can predict ETR resistance; however, genotype-based algorithms are derived primarily from HIV-1 subtype B and may not accurately predict resistance in non-B subtypes. The frequency of ETR resistance among recombinant subtype C HIV-1 and the accuracy of genotypic interpretation systems were investigated. HIV-1LAI containing full-length RT from HIV-1 subtype C-positive individuals experiencing virologic failure (>10,000 copies/ml and >1 NNRTI resistance-associated mutation) were phenotyped for ETR susceptibility. Fold change (FC) was calculated against a composite 50% effective concentration (EC50) from treatment-naive individuals and three classifications were assigned: (i) <2.9-FC, susceptible; (ii) ≥2.9- to 10-FC, partially resistant; and (iii) >10-FC, fully resistant. The Stanford HIVdb-v8.4 was used for genotype predictions merging the susceptible/potential low-level and low-level/intermediate groups for 3 × 3 comparison. Fifty-four of a hundred samples had reduced ETR susceptibility (≥2.9-FC). The FC correlated with HIVdb-v8.4 (Spearman’s rho = 0.62; P < 0.0001); however, 44% of samples were partially (1 resistance classification difference) and 4% completely discordant (2 resistance classification differences). Of the 34 samples with an FC of >10, 26 were HIVdb-v8.4 classified as low-intermediate resistant. Mutations L100I, Y181C, or M230L were present in 27/34 (79%) of samples with an FC of >10 but only in 2/46 (4%) of samples with an FC of <2.9. No other mutations were associated with ETR resistance. Viruses containing the mutation K65R were associated with reduced ETR susceptibility, but 65R reversions did not increase ETR susceptibility. Therefore, genotypic interpretation systems were found to misclassify ETR susceptibility in HIV-1 subtype C samples. Modifications to genotypic algorithms are needed to improve the prediction of ETR resistance for the HIV-1 subtype C.

scholarly journals Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time

PLoS ONE ◽  
2010 ◽  
Vol 5 (7) ◽  
pp. e11505 ◽  
Author(s):  
Dineke Frentz ◽  
Charles A. B. Boucher ◽  
Matthias Assel ◽  
Andrea De Luca ◽  
Massimiliano Fabbiani ◽  
...  
Keyword(s):  
Resistance Test ◽  
Test Interpretation ◽  
Genotypic Resistance ◽  
Genotypic Resistance Test ◽  
Virological Outcomes ◽  
Hiv 1 ◽  
Over Time ◽  
Interpretation Systems

scholarly journals Collaborative update of a rule-based expert system for HIV-1 genotypic resistance test interpretation

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181357 ◽  
Author(s):  
Roger Paredes ◽  
Philip L. Tzou ◽  
Gert van Zyl ◽  
Geoff Barrow ◽  
Ricardo Camacho ◽  
...  
Keyword(s):  
Expert System ◽  
Resistance Test ◽  
Test Interpretation ◽  
Genotypic Resistance ◽  
Rule Based ◽  
Genotypic Resistance Test ◽  
Hiv 1

A study of seven rule-based algorithms for the interpretation of HIV-1 genotypic resistance data in Thailand

2008 ◽  
Vol 151 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Vongsakorn Poonpiriya ◽  
Somnuek Sungkanuparph ◽  
Pranee Leechanachai ◽  
Ekawat Pasomsub ◽  
Chotip Watitpun ◽  
...  
Keyword(s):  
Genotypic Resistance ◽  
Rule Based ◽  
Resistance Data ◽  
Hiv 1

scholarly journals Rules-based HIV-1 genotypic resistance interpretation systems predict 8 week and 24 week virological antiretroviral treatment outcome and benefit from drug potency weighting

2009 ◽  
Vol 64 (3) ◽  
pp. 616-624 ◽  
Author(s):  
Maurizio Zazzi ◽  
Mattia Prosperi ◽  
Ilaria Vicenti ◽  
Simona Di Giambenedetto ◽  
Annapaola Callegaro ◽  
...  
Keyword(s):  
Treatment Outcome ◽  
Antiretroviral Treatment ◽  
Genotypic Resistance ◽  
Drug Potency ◽  
Hiv 1 ◽  
Interpretation Systems

Concordance of genotypic resistance interpretation algorithms in HIV-1 infected patients: An exploratory analysis in Greece

2021 ◽  
Vol 137 ◽  
pp. 104779
Author(s):  
Maria Kantzanou ◽  
Maria A. Karalexi ◽  
Anduela Zivinaki ◽  
Elena Riza ◽  
Helen Papachristou ◽  
...  
Keyword(s):  
Exploratory Analysis ◽  
Genotypic Resistance ◽  
Hiv 1

scholarly journals Drug Resistance-Associated Mutations in Antiretroviral Treatment-Experienced Patients in Kuwait

2018 ◽  
Vol 27 (2) ◽  
pp. 152-157
Author(s):  
Wassim Chehadeh ◽  
Osama Albaksami ◽  
Sonia Elezebeth John ◽  
Widad Al-Nakib
Keyword(s):  
Reverse Transcriptase ◽  
Transcriptase Inhibitor ◽  
Antiretroviral Drugs ◽  
First Line ◽  
Genotypic Resistance ◽  
Nonnucleoside Reverse Transcriptase Inhibitor ◽  
High Level ◽  
Interpretation Algorithm ◽  
Hiv 1 ◽  
Level Resistance

Objectives: To investigate the prevalence of nonpolymorphic resistance-associated mutations (RAM) in HIV-1 patients on first-line antiretroviral therapy in Kuwait. Subjects and Methods: Total RNA was isolated from plasma samples of 42 patients who received a first-line nonnucleoside reverse transcriptase inhibitor (NNRTI)-based regimen. HIV-1 protease and reverse transcriptase genetic regions were then amplified by nested reverse transcription-polymerase chain reaction and directly sequenced. The HIV-1 subtype was identified using the Bayesian phylogenetic method, and RAM were identified using the Stanford University genotypic resistance interpretation algorithm. Results: The HIV-1 viral load at sampling ranged from < 20 to 8.25 × 104 copies/ml. CRF01_AE, C, and B were the most predominant HIV-1 subtypes. Nonpolymorphic mutations associated with resistance to antiretroviral drugs were detected in 11 (26.2%) of the 42 patients; 5 (11.9%) patients had mutations associated with a high-level resistance to nucleoside reverse transcriptase inhibitors (NRTI), 4 (9.5%) patients had mutations associated with resistance to NNRTI, 1 (2.4%) patient had mutations associated with resistance to both NRTI and NNRTI, and 1 (2.4%) patient had mutations potentially associated with low-level resistance to both protease inhibitors and NNRTI. All patients with RAM had a detectable plasma HIV-1 RNA level. Conclusion: Our results indicate the development of RAM during an NNRTI-based regimen and highlight the importance of considering other regimens to avoid treatment failure.

Sign in / Sign up

Export Citation Format

Share Document