Literature DB >> 20368406

Predicting tipranavir and darunavir resistance using genotypic, phenotypic, and virtual phenotypic resistance patterns: an independent cohort analysis of clinical isolates highly resistant to all other protease inhibitors.

Annie Talbot1, Philip Grant, Jonathan Taylor, Jean-Guy Baril, Tommy Fulisma Liu, Hugues Charest, Bluma Brenner, Michel Roger, Robert Shafer, Régis Cantin, Andrew Zolopa.   

Abstract

Genotypic interpretation systems (GISs) for darunavir and tipranavir susceptibility are rarely tested by the use of independent data sets. The virtual phenotype (the phenotype determined by Virco [the "Vircotype"]) was used to interpret all genotypes in Québec, Canada, and phenotypes were determined for isolates predicted to be resistant to all protease inhibitors other than darunavir and tipranavir. We used multivariate analyses to predict relative phenotypic susceptibility to darunavir and tipranavir. We compared the performance characteristics of the Agence Nationale de Recherche sur le Sida scoring algorithm, the Stanford HIV database scoring algorithm (with separate analyses of the discrete and numerical scores), the Vircotype, and the darunavir and tipranavir manufacturers' scores for prediction of the phenotype. Of the 100 isolates whose phenotypes were determined, 89 and 72 were susceptible to darunavir and tipranavir, respectively. In multivariate analyses, the presence of I84V and V82T and the lack of L10F predicted that the isolates would be more susceptible to darunavir than tipranavir. The presence of I54L, V32I, and I47V predicted that the isolates would be more susceptible to tipranavir. All GISs except the system that provided the Stanford HIV database discrete score performed well in predicting the darunavir resistance phenotype (R(2) = 0.61 to 0.69); the R(2) value for the Stanford HIV database discrete scoring system was 0.38. Other than the system that provided the Vircotype (R(2) = 0.80), all GISs performed poorly in predicting the tipranavir resistance phenotype (R(2) = 0.00 to 0.31). In this independent cohort harboring highly protease inhibitor-resistant HIV isolates, reduced phenotypic susceptibility to darunavir and tipranavir was rare. Generally, GISs predict susceptibility to darunavir substantially better than they predict susceptibility to tipranavir.

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Year:  2010        PMID: 20368406      PMCID: PMC2876425          DOI: 10.1128/AAC.00096-10

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


  20 in total

1.  HIV-1 protease and reverse transcriptase mutation patterns responsible for discordances between genotypic drug resistance interpretation algorithms.

Authors:  Jaideep Ravela; Bradley J Betts; Francoise Brun-Vézinet; Anne-Mieke Vandamme; Diane Descamps; Kristel van Laethem; Kate Smith; Jonathan M Schapiro; Dean L Winslow; Caroline Reid; Robert W Shafer
Journal:  J Acquir Immune Defic Syndr       Date:  2003-05-01       Impact factor: 3.731

Review 2.  Impact of different HIV resistance interpretation by distinct systems on clinical utility of resistance testing.

Authors:  Andrea De Luca; Carlo-Federico Perno
Journal:  Curr Opin Infect Dis       Date:  2003-12       Impact factor: 4.915

3.  Discrepant results in the interpretation of HIV-1 drug-resistance genotypic data among widely used algorithms.

Authors:  G H Kijak; A E Rubio; S E Pampuro; C Zala; P Cahn; R Galli; J S Montaner; H Salomón
Journal:  HIV Med       Date:  2003-01       Impact factor: 3.180

4.  Correlation between rules-based interpretation and virtual phenotype interpretation of HIV-1 genotypes for predicting drug resistance in HIV-infected individuals.

Authors:  Oscar Gallego; Luz Martin-Carbonero; Jesus Aguero; Carmen de Mendoza; Angelica Corral; Vincent Soriano
Journal:  J Virol Methods       Date:  2004-10       Impact factor: 2.014

5.  Durable efficacy of tipranavir-ritonavir in combination with an optimised background regimen of antiretroviral drugs for treatment-experienced HIV-1-infected patients at 48 weeks in the Randomized Evaluation of Strategic Intervention in multi-drug reSistant patients with Tipranavir (RESIST) studies: an analysis of combined data from two randomised open-label trials.

Authors:  Charles B Hicks; Pedro Cahn; David A Cooper; Sharon L Walmsley; Christine Katlama; Bonaventura Clotet; Adriano Lazzarin; Margaret A Johnson; Dietmar Neubacher; Douglas Mayers; Hernan Valdez
Journal:  Lancet       Date:  2006-08-05       Impact factor: 79.321

6.  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.

Authors:  Carlo Torti; 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
Journal:  J Infect Dis       Date:  2003-07-01       Impact factor: 5.226

7.  Relative antiviral efficacy of ritonavir-boosted darunavir and ritonavir-boosted tipranavir vs. control protease inhibitor in the POWER and RESIST trials.

Authors:  A Hill; G Moyle
Journal:  HIV Med       Date:  2007-05       Impact factor: 3.180

8.  Efficacy and safety of darunavir-ritonavir compared with that of lopinavir-ritonavir at 48 weeks in treatment-experienced, HIV-infected patients in TITAN: a randomised controlled phase III trial.

Authors:  José Valdez Madruga; Daniel Berger; Marilyn McMurchie; Fredy Suter; Denes Banhegyi; Kiat Ruxrungtham; Dorece Norris; Eric Lefebvre; Marie-Pierre de Béthune; Frank Tomaka; Martine De Pauw; Tony Vangeneugden; Sabrina Spinosa-Guzman
Journal:  Lancet       Date:  2007-07-07       Impact factor: 79.321

9.  Prevalence of darunavir resistance mutations in HIV-1-infected patients failing other protease inhibitors.

Authors:  Eva Poveda; Carmen de Mendoza; Luz Martin-Carbonero; Angélica Corral; Verónica Briz; Juan González-Lahoz; Vincent Soriano
Journal:  J Antimicrob Chemother       Date:  2007-07-23       Impact factor: 5.790

10.  Efficacy and safety of darunavir-ritonavir at week 48 in treatment-experienced patients with HIV-1 infection in POWER 1 and 2: a pooled subgroup analysis of data from two randomised trials.

Authors:  Bonaventura Clotet; Nicholas Bellos; Jean-Michel Molina; David Cooper; Jean-Christophe Goffard; Adriano Lazzarin; Andrej Wöhrmann; Christine Katlama; Timothy Wilkin; Richard Haubrich; Calvin Cohen; Charles Farthing; Dushyantha Jayaweera; Martin Markowitz; Peter Ruane; Sabrina Spinosa-Guzman; Eric Lefebvre
Journal:  Lancet       Date:  2007-04-07       Impact factor: 79.321

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  7 in total

1.  Comparison of drug resistance scores for tipranavir in protease inhibitor-naive patients infected with HIV-1 B and non-B subtypes.

Authors:  Martin Stürmer; Christoph Stephan; Peter Gute; Gaby Knecht; Markus Bickel; Hans-Reinhard Brodt; Hans W Doerr; Lutz Gürtler; Pierre Lecocq; Margriet van Houtte
Journal:  Antimicrob Agents Chemother       Date:  2011-08-08       Impact factor: 5.191

2.  Genotypic resistance profiles associated with virological failure to darunavir-containing regimens: a cross-sectional analysis.

Authors:  G Sterrantino; M Zaccarelli; G Colao; F Baldanti; S Di Giambenedetto; T Carli; F Maggiolo; M Zazzi
Journal:  Infection       Date:  2012-01-12       Impact factor: 3.553

Review 3.  HIV-1 antiretroviral resistance: scientific principles and clinical applications.

Authors:  Michele W Tang; Robert W Shafer
Journal:  Drugs       Date:  2012-06-18       Impact factor: 9.546

4.  Drug susceptibility to etravirine and darunavir among Human Immunodeficiency Virus Type 1-derived pseudoviruses in treatment-experienced patients with HIV/AIDS in South Korea.

Authors:  Oh-Kyung Kwon; Sung Soon Kim; Jee Eun Rhee; Mee-Kyung Kee; Mina Park; Hye-Ri Oh; Ju-Yeon Choi
Journal:  Virol J       Date:  2015-04-09       Impact factor: 4.099

5.  Collinearity of protease mutations in HIV-1 samples with high-level protease inhibitor class resistance.

Authors:  Farbod Babrzadeh; Vici Varghese; Mary Pacold; Tommy F Liu; Pål Nyrén; Celia Schiffer; W Jeffrey Fessel; Robert W Shafer
Journal:  J Antimicrob Chemother       Date:  2012-10-19       Impact factor: 5.790

6.  Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure.

Authors:  Xiaxia Yu; Irene T Weber; Robert W Harrison
Journal:  BMC Genomics       Date:  2014-07-14       Impact factor: 3.969

7.  High prevalence of PI resistance in patients failing second-line ART in Vietnam.

Authors:  Vu Phuong Thao; Vo Minh Quang; Jeremy N Day; Nguyen Tran Chinh; Cecilia M Shikuma; Jeremy Farrar; Nguyen Van Vinh Chau; Guy E Thwaites; Sarah J Dunstan; Thuy Le
Journal:  J Antimicrob Chemother       Date:  2015-12-11       Impact factor: 5.790

  7 in total

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