Literature DB >> 12924541

Comparison of nine resistance interpretation systems for HIV-1 genotyping.

Martin Stürmer1, Hans Wilhelm Doerr, Schlomo Staszewski, Wolfgang Preiser.   

Abstract

HIV-1 genotyping has become a widely accepted tool for monitoring antiretroviral therapy. However, the benefit of genotyping is limited by the need to interpret the mutation pattern in order to obtain a prediction regarding susceptibility to each antiretroviral drug. Several different interpretation systems are currently available, commercially or free of charge; some are in combination with the genotyping test system used. In this study, we compared the results obtained on patient samples, using nine different resistance interpretation systems for HIV-1 genotype, and identified mutation patterns responsible for discordances between these systems. HIV-1 genotypes from 26 patients were obtained using the ViroSeq HIV-1 Genotyping System (Applied Biosystems). Nine resistance interpretation systems were used: the 'virtual phenotype' systems VirtualPhenotype (Virco) and Geno2Pheno (http://cartan.gmd.de/geno2pheno.html), the rule-based resistance algorithms Antiretroviral Drug Resistance Report (Applied Biosystems), Stanford HIV-SEQ program (http://hivdb.stanford.edu/) and the ViroScorer system (ABL; including ANRS AC11, Detroit Medical Center, Grupo de Aconselhamento Virológico, CHL, and Rega). Discordance was defined as the same sequence being interpreted as resistant and sensitive to a substance by different systems, with intermediate scores being regarded as neutral. Results for lopinavir were not available from some interpretation systems. None of the 26 patient samples had concordant results for all drugs. The highest degree of concordance was found for the resistance scoring of lamivudine (25/26), followed by nelfinavir (23/26), indinavir, ritonavir, saquinavir (all 22/26), zidovudine, nevirapine (all 21/26), lopinavir, efavirenz (all 18/26), amprenavir, delavirdine (all 17/26), stavudine, abacavir (all 13/26), zalcitabine (7/26) and didanosine (5/26). In most cases, it was only one interpretation system that gave an interpretation different from the others. If this interpretation system was omitted, the overall discordance rate decreased by a statistically significant degree. There exists, therefore, an urgent need for standardization of interpretation algorithms for HIV-1 genotyping.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 12924541

Source DB:  PubMed          Journal:  Antivir Ther        ISSN: 1359-6535


  7 in total

1.  Clinical evaluation of the potential utility of computational modeling as an HIV treatment selection tool by physicians with considerable HIV experience.

Authors:  Brendan A Larder; Andrew Revell; Joann M Mican; Brian K Agan; Marianne Harris; Carlo Torti; Ilaria Izzo; Julia A Metcalf; Migdalia Rivera-Goba; Vincent C Marconi; Dechao Wang; Daniel Coe; Brian Gazzard; Julio Montaner; H Clifford Lane
Journal:  AIDS Patient Care STDS       Date:  2011-01       Impact factor: 5.078

2.  Discordances between interpretation algorithms for genotypic resistance to protease and reverse transcriptase inhibitors of human immunodeficiency virus are subtype dependent.

Authors:  Joke Snoeck; Rami Kantor; Robert W Shafer; Kristel Van Laethem; Koen Deforche; Ana Patricia Carvalho; Brian Wynhoven; Marcelo A Soares; Patricia Cane; John Clarke; Candice Pillay; Sunee Sirivichayakul; Koya Ariyoshi; Africa Holguin; Hagit Rudich; Rosangela Rodrigues; Maria Belen Bouzas; Françoise Brun-Vézinet; Caroline Reid; Pedro Cahn; Luis Fernando Brigido; Zehava Grossman; Vincent Soriano; Wataru Sugiura; Praphan Phanuphak; Lynn Morris; Jonathan Weber; Deenan Pillay; Amilcar Tanuri; Richard P Harrigan; Ricardo Camacho; Jonathan M Schapiro; David Katzenstein; Anne-Mieke Vandamme
Journal:  Antimicrob Agents Chemother       Date:  2006-02       Impact factor: 5.191

3.  The use of computational models to predict response to HIV therapy for clinical cases in Romania.

Authors:  Andrew D Revell; Luminiţa Ene; Dan Duiculescu; Dechao Wang; Mike Youle; Anton Pozniak; Julio Montaner; Brendan A Larder
Journal:  Germs       Date:  2012-03-01

Review 4.  Peptide bioinformatics: peptide classification using peptide machines.

Authors:  Zheng Rong Yang
Journal:  Methods Mol Biol       Date:  2008

5.  "Dynamic range" of inferred phenotypic HIV drug resistance values in clinical practice.

Authors:  Luke C Swenson; Graham Pollock; Brian Wynhoven; Theresa Mo; Winnie Dong; Robert S Hogg; Julio S G Montaner; P Richard Harrigan
Journal:  PLoS One       Date:  2011-02-24       Impact factor: 3.240

6.  Comparison of predicted susceptibility between genotype and virtual phenotype HIV drug resistance interpretation systems among treatment-naive HIV-infected patients in Asia: TASER-M cohort analysis.

Authors:  Awachana Jiamsakul; Rami Kantor; Patrick C K Li; Sunee Sirivichayakul; Thira Sirisanthana; Pacharee Kantipong; Christopher K C Lee; Adeeba Kamarulzaman; Winai Ratanasuwan; Rossana Ditangco; Thida Singtoroj; Somnuek Sungkanuparph
Journal:  BMC Res Notes       Date:  2012-10-24

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

Authors:  Roger Paredes; Philip L Tzou; Gert van Zyl; Geoff Barrow; Ricardo Camacho; Sergio Carmona; Philip M Grant; Ravindra K Gupta; Raph L Hamers; P Richard Harrigan; Michael R Jordan; Rami Kantor; David A Katzenstein; Daniel R Kuritzkes; Frank Maldarelli; Dan Otelea; Carole L Wallis; Jonathan M Schapiro; Robert W Shafer
Journal:  PLoS One       Date:  2017-07-28       Impact factor: 3.752

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.