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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

机译:使用基因型表型和虚拟表型耐药模式预测替普那韦和达那那韦耐药性:对所有其他蛋白酶抑制剂均高度耐药的临床分离株的独立队列分析

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摘要

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 (R2 = 0.61 to 0.69); the R2 value for the Stanford HIV database discrete scoring system was 0.38. Other than the system that provided the Vircotype (R2 = 0.80), all GISs performed poorly in predicting the tipranavir resistance phenotype (R2 = 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.
机译:darunavir和tipranavir敏感性的基因型解释系统(GIS)很少通过使用独立的数据集进行测试。虚拟表型(由Virco确定的表型[“ Vircotype”)用于解释魁北克省加拿大的所有基因型,并确定了预期对除darunavir和Tipranavir以外的所有蛋白酶抑制剂均具有抗性的分离株的表型。我们使用多变量分析来预测对达那那韦和替普那韦的相对表型敏感性。我们比较了Sida国家研究中心评分算法,斯坦福HIV数据库评分算法(对离散和数值评分进行单独分析),Vircotype以及darunavir和Tipranavir制造商评分的性能特征,以预测表型。在确定其表型的100个分离株中,分别有89株和72株对darunavir和Tipranavir敏感。在多变量分析中,I84V和V82T的存在以及L10F的缺乏预示着分离株比地拉那韦更易受darunavir的影响。 I54L,V32I和I47V的存在预示了分离株将更易吸毒。除提供斯坦福HIV数据库离散评分的系统外,所有GIS在预测darunavir耐药表型方面均表现良好(R 2 = 0.61至0.69);斯坦福大学HIV数据库离散评分系统的R 2 值为0.38。除了提供Vircotype(R 2 = 0.80)的系统外,所有GIS在预测耐提普那韦表型时均表现不佳(R 2 = 0.00至0.31)。在这个具有高度蛋白酶抵抗性的HIV分离株的独立队列中,罕见的是降低了对darunavir和Tipranavir的表型敏感性。通常,GIS预测对达那那韦的敏感性要好于他们预测对地普那韦的敏感性。

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