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Short communication: Phenotypic protease inhibitor resistance and cross-resistance in the clinic from 2006 to 2008 and mutational prevalences in HIV from patients with discordant tipranavir and darunavir susceptibility phenotypes
Authors:Bethell Richard  Scherer Joseph  Witvrouw Myriam  Paquet Agnes  Coakley Eoin  Hall David
Institution:Boehringer Ingelheim (Canada) Ltd., Laval, Québec, Canada.
Abstract:To test tipranavir (TPV) or darunavir (DRV) as treatment options for patients with phenotypic resistance to protease inhibitors (PIs), including lopinavir, saquinavir, atazanavir, and fosamprenavir, the PhenoSense GT database was analyzed for susceptibility to DRV or TPV among PI-resistant isolates. The Monogram Biosciences HIV database (South San Francisco, CA) containing 7775 clinical isolates (2006-2008) not susceptible to at least one first-generation PI was analyzed. Phenotypic responses resistant (R), partially susceptible (PS), or susceptible (S)] were defined by upper and lower clinical cut-offs to each PI. Genotypes were screened for amino acid substitutions associated with TPV-R/DRV-S and TPV-S/DRV-R phenotypes. In all, 4.9% (378) of isolates were resistant to all six PIs and 31.0% (2407) were resistant to none. Among isolates resistant to all four first-generation PIs, DRV resistance increased from 21.2% to 41.9% from 2006 to 2008, respectively, and resistance to TPV remained steady (53.9 to 57.3%, respectively). Higher prevalence substitutions in DRV-S/TPV-R isolates versus DRV-R/TPV-S isolates, respectively, were 82L/T (44.4% vs. 0%) and 83D (5.8% vs. 0%). Higher prevalence substitutions in DRV-R/TPV-S virus were 50V (0.0% vs. 28.9%), 54L (1.0% vs. 36.1%), and 76V (0.4% vs. 15.5%). Mutations to help predict discordant susceptibility to DRV and TPV in isolates with reduced susceptibility to other PIs were identified. DRV resistance mutations associated with improved virologic response to TPV were more prevalent in DRV-R/TPV-S isolates. TPV resistance mutations were more prevalent in TPV-R and DRV-S isolates. These results confirm the impact of genotype on phenotype, illustrating how HIV genotype and phenotype data assist regimen optimization.
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