Literature DB >> 12854073

Comparison between rules-based human immunodeficiency virus type 1 genotype interpretations and real or virtual phenotype: concordance analysis and correlation with clinical outcome in heavily treated patients.

Carlo Torti1, Eugenia Quiros-Roldan, Wilco Keulen, Luigia Scudeller, Sergio Lo Caputo, Charles Boucher, Francesco Castelli, Francesco Mazzotta, Piera Pierotti, Anne Mieke Been-Tiktak, Giovanni Buccoliero, Michele De Gennaro, Giampiero Carosi, Carmine Tinelli.   

Abstract

We compared 2 rules-based genotype interpretation systems and real or virtual phenotype through a retrospective analysis of a prospective trial. Genotypes were determined with VircoGEN II (VIRCO) and were interpreted with either RetroGram 1.4 or TRUGENE HIV-1 (guidelines 3.0) or original virtual phenotype (Virtual Phenotype; VIRCO), as available in the year 2000. Among 188 human immunodeficiency virus (HIV) type 1 isolates, overall concordance (kappa agreement) was observed for the 2 rules-based systems, whereas striking discordances were noted between them and real and virtual phenotype interpretations for stavudine, didanosine, zalcitabine, abacavir, and amprenavir (kappa<0.4). Clinical evaluation of a subset of 173 patients showed that both rules-based sensitivity scores were independently associated with HIV RNA loads <400 copies/mL at week 16 of during-treatment analysis (TRUGENE: odds ratio [OR], 2.90; 95% confidence interval [CI], 1.52-5.52; P=.001; RetroGram: OR, 2.34; 95% CI, 1.21-4.55; P=.012), whereas, in contrast to real or virtual phenotype, interpretations according to biological cut-offs were not (OR, 1.91; 95% CI, 0.77-4.76; P=.162).

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Year:  2003        PMID: 12854073     DOI: 10.1086/376512

Source DB:  PubMed          Journal:  J Infect Dis        ISSN: 0022-1899            Impact factor:   5.226


  11 in total

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Journal:  Antimicrob Agents Chemother       Date:  2006-02       Impact factor: 5.191

4.  Successful virological outcome in an HIV-infected individual with a three-class resistant variant and an insertion in the protease genome with a Tipranavir based regimen.

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7.  Web resources for HIV type 1 genotypic-resistance test interpretation.

Authors:  Tommy F Liu; Robert W Shafer
Journal:  Clin Infect Dis       Date:  2006-04-28       Impact factor: 9.079

8.  The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients.

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Journal:  PLoS Comput Biol       Date:  2013-08-29       Impact factor: 4.475

Review 9.  HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data.

Authors:  Olga Tarasova; Vladimir Poroikov
Journal:  Molecules       Date:  2018-04-19       Impact factor: 4.411

10.  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

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