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Education > Clinical Updates

Clinical Update #6 - News from the 45 ^th Annual Meeting of the Infectious Diseases Society of America

San Diego, California
October 4-7, 2007

Current guidelines recommend the use of resistance tests to optimize the selection of active regimens when initiating or changing antiretroviral therapy. An important aspect in the development of any system that predicts response to antiretroviral therapy is its validation using a diverse clinical dataset not used for algorithm development. Lee Bacheler and colleagues from Virco presented a poster at the 45th Annual Meeting of the Infectious Diseases Society of America that established the higher accuracy of the virco®TYPE system in predicting 8-week response to antiretroviral therapy compared with 3 genotypic algorithms. The study also showed that a continuous susceptibility score based on the virco®TYPE system provided the best accuracy for prediction of response to tipranivir (TPV) or darunavir (DRV)-containing regimens in treatment-experienced patients.

This retrospective analysis utilized a dataset that had never been used for algorithm development (n=2108 Treatment Change Episodes). The 4 resistance interpretation systems evaluated were HIVdb (Stanford version 4.2.6), ANRS (August 2006), REGA (version 6.4.1), and virco®TYPE assay (version 4.0.01). Baseline regimen sensitivity scores (genotypic sensitivity score [GSS] or continuous phenotypic sensitivity score [cPSS]) for each regimen by each algorithm were calculated as the number of active drugs in the regimen. A responder was defined as a patient who achieved a 1 log drop in viral load from baseline at 8 weeks or a viral load measurement below the detection limit of the viral load assay. The association of baseline regimen sensitivity scores with virologic outcome was evaluated using 3 metrics: namely, Pearson Correlation, diagnostic accuracy (as area under the ROC curve [AUC]), and Responder- Non-responder Classification. Separate analyses were performed for the entire unseen dataset and for subgroups of patients with a difference of >0.5 in sensitivity score between virco®TYPE and each of the other algorithms.

The baseline sensitivity score from the virco®TYPE system was most strongly associated with virologic response at week 8 compared with the HIVdb, ANRS, and REGA resistance interpretation systems. When focusing on those regimens for which resistance interpretations varied, the virco®TYPE system remained more strongly correlated with response than the other interpretation systems.

In addition to the above analysis, correlation of protease inhibitor (PI) resistance interpretation (virco®TYPE, HIVdb, PI mutation list, TPV or DRV mutation count) for TPV and DRV with outcome in the RESIST and POWER studies respectively was evaluated. As each of the PI resistance algorithms was based on baseline resistance and outcome in RESIST and POWER studies, the data sets represent "seen" data for all algorithms. In order to highlight the contribution of the PI resistance call, a cPSS score for the individual optimized background regimen was calculated in each case based on virco®TYPE and combined with the sensitivity score by each algorithm for each PI. A responder was defined as a patient who achieved a 1 log or better drop in viral load from baseline at 8 weeks or a viral load measurement below the detection limit of the viral load assay. The association of baseline regimen sensitivity scores with virologic outcome was evaluated using the same 3 metrics described previously. Again, the best accuracy and correlation with observed response to DRV or TPV-containing regimens in triple-class experienced patients were achieved using the PI continuous phenotypic susceptibility score based on virco®TYPE HIV-1.

In interpreting the results, the authors surmise that the better performance of a predicted phenotype approach could be due to the ability of the method to factor complex mutational profiles in its resistance interpretation and more accurately predict expected activity of drugs against viruses with varying degrees of drug resistance. Please feel free to view the complete poster on Correlation of Genotypic and Phenotypic HIV-1 Resistance Algorithms.

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