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

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.

FIG. 1.

Frequency of mutations by protease position (n = 100). T, position of IAS-USA major tipranavir resistance-conferring mutation; D, position of IAS-USA major darunavir resistance-conferring mutation (13).

FIG. 2.

Correlation between genotypic interpretation scores and natural log of fold change for darunavir (DRV). (a) ANRS score versus natural log of the darunavir fold change; (b) Stanford HIV database (HIVDB) discrete score versus natural log of the darunavir fold change; (c) Stanford HIV database numerical score versus natural log of the darunavir fold change; (d) Vircotype versus natural log of the darunavir fold change; (e) darunavir score versus natural log of the darunavir fold change.

FIG. 3.

Correlation between genotypic interpretation scores and natural log of fold change for tipranavir (TPV). (a) ANRS score versus natural log of the tipranavir fold change; (b) Stanford HIV Database discrete score versus natural log of the tipranavir fold change; (c) Stanford HIV Database numerical score versus natural log of the tipranavir fold change; (d) Vircotype versus natural log of the tipranavir fold change; (e) tipranavir score versus natural log of tipranavir fold change.