Literature DB >> 19697398

A comparison of HIV-1 drug susceptibility as provided by conventional phenotyping and by a phenotype prediction tool based on viral genotype.

Margriet Van Houtte1, Gaston Picchio, Koen Van Der Borght, Theresa Pattery, Pierre Lecocq, Lee T Bacheler.   

Abstract

Concordance between the conventional HIV-1 phenotypic drug resistance assay, PhenoSense (PS), and vircoTYPE HIV-1 (vT), a drug resistance assay based on prediction of the phenotype, was investigated in a data set from the Stanford HIV Resistance database (hivdb). Depending on the drug, between 287 and 902 genotype-phenotype data pairs were available for comparisons. Test results (fold-change values) in the two assays were highly correlated, with an overall mean correlation coefficient of 0.90 using single PS measurements. This coefficient rose to 0.94 when the vT results were compared to the mean of repeat PS measurements. These results are comparable with the corresponding correlation coefficients of 0.87 and 0.95, calculated using single measurements, and the mean of repeat measurements, respectively, as obtained in the Antivirogram assay, the conventional HIV-1 phenotypic drug resistance test on which vT is based. The proportion of resistance calls resulting in a "major" discordance (fully susceptible or maximal response by one assay but fully resistant or minimal response by the other) ranged from 0% to 8.1% for drugs for which two clinical test cut-offs were available in both assays (didanosine, abacavir, tenofovir, saquinavir/r, fosamprenavir/r, and lopinavir/r), from 2.4% to 8.1% for the drugs for which two clinical test cut-offs were available in the vT assay and one clinical test cut-off in the PS assay (lamivudine, stavudine, indinavir/r, and atazanavir/r) and from 3.1% to 10.3% for drugs for which biological test cut-offs were used (zidovudine, nevirapine, delavirdine, efavirenz, indinavir, ritonavir, nelfinavir, saquinavir, and fosamprenavir). Our analyses suggest that these assays provide comparable resistance information, which will be of value to physicians who may be presented with either or both types of test report in their practice.

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Year:  2009        PMID: 19697398     DOI: 10.1002/jmv.21585

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   2.327


  14 in total

1.  Comparison of genotypic and virtual phenotypic drug resistance interpretations with laboratory-based phenotypes among CRF01_AE and subtype B HIV-infected individuals.

Authors:  Awachana Jiamsakul; Romanee Chaiwarith; Nicolas Durier; Sunee Sirivichayakul; Sasisopin Kiertiburanakul; Peter Van Den Eede; Rossana Ditangco; Adeeba Kamarulzaman; Patrick C K Li; Winai Ratanasuwan; Thira Sirisanthana
Journal:  J Med Virol       Date:  2015-07-17       Impact factor: 2.327

2.  Novel method for simultaneous quantification of phenotypic resistance to maturation, protease, reverse transcriptase, and integrase HIV inhibitors based on 3'Gag(p2/p7/p1/p6)/PR/RT/INT-recombinant viruses: a useful tool in the multitarget era of antiretroviral therapy.

Authors:  Jan Weber; Ana C Vazquez; Dane Winner; Justine D Rose; Doug Wylie; Ariel M Rhea; Kenneth Henry; Jennifer Pappas; Alison Wright; Nizar Mohamed; Richard Gibson; Benigno Rodriguez; Vicente Soriano; Kevin King; Eric J Arts; Paul D Olivo; Miguel E Quiñones-Mateu
Journal:  Antimicrob Agents Chemother       Date:  2011-05-31       Impact factor: 5.191

3.  Characterization of the E138K resistance mutation in HIV-1 reverse transcriptase conferring susceptibility to etravirine in B and non-B HIV-1 subtypes.

Authors:  Eugene L Asahchop; Maureen Oliveira; Mark A Wainberg; Bluma G Brenner; Daniela Moisi; Thomas d'Aquin Toni; Cecile L Tremblay
Journal:  Antimicrob Agents Chemother       Date:  2010-12-06       Impact factor: 5.191

4.  Next-Generation Sequencing to Help Monitor Patients Infected with HIV: Ready for Clinical Use?

Authors:  Richard M Gibson; Christine L Schmotzer; Miguel E Quiñones-Mateu
Journal:  Curr Infect Dis Rep       Date:  2014-04       Impact factor: 3.725

5.  Profile of etravirine for the treatment of HIV infection.

Authors:  Alice Tseng; Rodger D Macarthur
Journal:  Ther Clin Risk Manag       Date:  2010-02-02       Impact factor: 2.423

6.  Performance of HIV-1 drug resistance testing at low-level viremia and its ability to predict future virologic outcomes and viral evolution in treatment-naive individuals.

Authors:  A Gonzalez-Serna; J E Min; C Woods; D Chan; V D Lima; J S G Montaner; P R Harrigan; L C Swenson
Journal:  Clin Infect Dis       Date:  2014-01-14       Impact factor: 9.079

7.  Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.

Authors:  Gerard J P van Westen; Alwin Hendriks; Jörg K Wegner; Adriaan P Ijzerman; Herman W T van Vlijmen; Andreas Bender
Journal:  PLoS Comput Biol       Date:  2013-02-21       Impact factor: 4.475

8.  K70Q adds high-level tenofovir resistance to "Q151M complex" HIV reverse transcriptase through the enhanced discrimination mechanism.

Authors:  Atsuko Hachiya; Eiichi N Kodama; Matthew M Schuckmann; Karen A Kirby; Eleftherios Michailidis; Yasuko Sakagami; Shinichi Oka; Kamalendra Singh; Stefan G Sarafianos
Journal:  PLoS One       Date:  2011-01-13       Impact factor: 3.240

9.  Predicting protein phenotypes based on protein-protein interaction network.

Authors:  Lele Hu; Tao Huang; Xiao-Jun Liu; Yu-Dong Cai
Journal:  PLoS One       Date:  2011-03-10       Impact factor: 3.240

10.  Targeted resequencing of HIV variants by microarray thermodynamics.

Authors:  Wahyu W Hadiwikarta; Bieke Van Dorst; Karen Hollanders; Lieven Stuyver; Enrico Carlon; Jef Hooyberghs
Journal:  Nucleic Acids Res       Date:  2013-08-08       Impact factor: 16.971

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