Literature DB >> 12139659

Comparison of virtual phenotype and HIV-SEQ program (Stanford) interpretation for predicting drug resistance of HIV strains.

E Puchhammer-Stöckl1, C Steininger, E Geringer, F X Heinz.   

Abstract

OBJECTIVE: Drug resistant HIV strains can be identified by genotypic analysis. The prediction of resistance from the HIV pol sequence, however, requires expert interpretation which is currently provided by various interpretation programs. In the present study, the comparability of two of these programs was investigated.
METHODS: One hundred and six HIV sequences obtained from patient samples were subjected to interpretation by the Stanford HIV-SEQ program (http://hivdb.stanford.edu) and, in parallel, by virtual phenotype analysis (VircoNET).
RESULTS: The overall concordance between the two assays was high with respect to nonnucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors. Highly discrepant results were obtained from 22 samples (1.5% of all comparisons), and these discrepancies were significantly associated with the interpretation of nucleoside reverse transcriptase inhibitors (NRTIs) (P < 0.01), especially regarding resistance to zalcitabine (ddC), didanosine (ddI) and abacavir (ABC). Nearly all of these discrepant samples showed a sensitive virtual phenotype. Mutations at codons 69 and 74 were associated with a discrepant interpretation for ddC.
CONCLUSIONS: The prediction of resistance to certain NRTIs from a given HIV sequence may be contradictory and requires further investigation.

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Year:  2002        PMID: 12139659     DOI: 10.1046/j.1468-1293.2002.00116.x

Source DB:  PubMed          Journal:  HIV Med        ISSN: 1464-2662            Impact factor:   3.180


  10 in total

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Review 6.  HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data.

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7.  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.

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8.  Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance.

Authors:  Majid Masso; Iosif I Vaisman
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9.  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

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

  10 in total

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