Literature DB >> 12792870

Variable prediction of antiretroviral treatment outcome by different systems for interpreting genotypic human immunodeficiency virus type 1 drug resistance.

Andrea De Luca1, Antonella Cingolani, Simona Di Giambenedetto, Maria Paola Trotta, Francesco Baldini, Maria Gabriella Rizzo, Ada Bertoli, Giuseppina Liuzzi, Pasquale Narciso, Rita Murri, Adriana Ammassari, Carlo Federico Perno, Andrea Antinori.   

Abstract

To determine the variability of genotypic human immunodeficiency virus (HIV) type 1 drug-resistance interpretation by available expert systems and its clinical implications, 261 subjects for whom a potent antiretroviral regimen was failing who were starting salvage therapy were evaluated. The association of the genotypic susceptibility score (GSS) of the salvage regimen, according to 11 interpretation systems, with HIV RNA outcomes for 6 months was examined. GSS was highly variable, as determined by the different interpretation systems, and showed independent correlation with changes from baseline HIV RNA levels at 6 months with 5 systems--Stanford hivdb, GuideLines 3.0, Retrogram 1.4, HIVresistanceWeb, and São Paulo University. Most GSSs predicted virologic response in regimens containing stavudine, lamivudine, efavirenz, or indinavir. Selected systems predicted response in regimens containing didanosine, abacavir, or nelfinavir, and no system predicted outcome of boosted protease inhibitors. GSSs predicted changes in HIV RNA levels better in adherent patients than in nonadherent individuals. Interpretation may be improved, and knowledge should be used uniformly throughout different expert systems.

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Year:  2003        PMID: 12792870     DOI: 10.1086/375355

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


  22 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.  A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance.

Authors:  Patricia Buendia; Brice Cadwallader; Victor DeGruttola
Journal:  Bioinformatics       Date:  2009-08-03       Impact factor: 6.937

4.  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

5.  Use of the l1 norm for selection of sparse parameter sets that accurately predict drug response phenotype from viral genetic sequences.

Authors:  Rabinowitz Matthew; Milena Banjevic; A S Chan; Lance Myers; Roland Wolkowicz; Jessica Haberer; Joshua Singer
Journal:  AMIA Annu Symp Proc       Date:  2005

6.  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

7.  Only slight impact of predicted replicative capacity for therapy response prediction.

Authors:  Hendrik Weisser; André Altmann; Saleta Sierra; Francesca Incardona; Daniel Struck; Anders Sönnerborg; Rolf Kaiser; Maurizio Zazzi; Monika Tschochner; Hauke Walter; Thomas Lengauer
Journal:  PLoS One       Date:  2010-02-03       Impact factor: 3.240

8.  Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time.

Authors:  Dineke Frentz; Charles A B Boucher; Matthias Assel; Andrea De Luca; Massimiliano Fabbiani; Francesca Incardona; Pieter Libin; Nino Manca; Viktor Müller; Breanndán O Nualláin; Roger Paredes; Mattia Prosperi; Eugenia Quiros-Roldan; Lidia Ruiz; Peter M A Sloot; Carlo Torti; Anne-Mieke Vandamme; Kristel Van Laethem; Maurizio Zazzi; David A M C van de Vijver
Journal:  PLoS One       Date:  2010-07-09       Impact factor: 3.240

9.  Maraviroc observational study: the impact of expanded resistance testing and clinical considerations for antiretroviral regimen selection in treatment-experienced patients.

Authors:  James H Willig; Sara-Anne Wilkins; Ashutosh Tamhane; Christa R Nevin; Michael J Mugavero; James L Raper; Laura A Napolitano; Michael S Saag
Journal:  AIDS Res Hum Retroviruses       Date:  2012-09-05       Impact factor: 2.205

10.  Antiretroviral genotypic resistance mutations in HIV-1 infected Korean patients with virologic failure.

Authors:  Bum Sik Chin; Ju-Yeon Choi; Jin Young Choi; Gab Jung Kim; Mee-Kyung Kee; June Myung Kim; Sung Soon Kim
Journal:  J Korean Med Sci       Date:  2009-11-07       Impact factor: 2.153

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