ArticleAntimicrobial agents and chemotherapy2010
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.
Article in Antimicrobial agents and chemotherapy, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 31 citations in OpenAlex.
- Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy.PeerJ · 2023Article
- Article
- High prevalence of PI resistance in patients failing second-line ART in Vietnam.The Journal of antimicrobial chemotherapy · 2016Article
- Drug susceptibility to etravirine and darunavir among Human Immunodeficiency Virus Type 1-derived pseudoviruses in treatment-experienced patients with HIV/AIDS in South Korea.Virology journal · 2015Article
- Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure.BMC genomics · 2014Article
- Collinearity of protease mutations in HIV-1 samples with high-level protease inhibitor class resistance.The Journal of antimicrobial chemotherapy · 2013Article
- Review
- Genotypic resistance profiles associated with virological failure to darunavir-containing regimens: a cross-sectional analysis.Infection · 2012Article
- Comparison of drug resistance scores for tipranavir in protease inhibitor-naive patients infected with HIV-1 B and non-B subtypes.Antimicrobial agents and chemotherapy · 2011Article
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
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.
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