Genotypic drug resistance interpretation algorithms display high levels of discordance when applied to non-B strains from HIV-1 naive and treated patients

Abstract

Genotypic drug resistance interpretation algorithms have been developed on patients infected with HIV-1 subtype B to interpret complex patterns of mutations. As non-B strains are characterised by the natural presence of several resistance-related mutations, we examined to what extent this might result in interalgorithm discordances in naive and treated patients. We compared the prediction by three algorithms (ANRS, Stanford and Rega) of drug susceptibilities to diverse HIV-1 strains from 272 naive and 156 treated patients. In naive patients, higher levels of interalgorithm discordance were observed for predictions of protease inhibitor (0.60-39%) than for predictions of reverse transcriptase inhibitor susceptibility (0-4%). The main reason for discordant protease inhibitor interpretation was the presence of resistance mutations that were natural protease polymorphisms. In contrast, in the treated patients, more interalgorithm discordances were observed for predictions of reverse transcriptase inhibitor (5-48%) than protease inhibitor susceptibilities (10-31%). Discordances were related to disagreement between the intermediate and susceptible scores, the intermediate and resistant scores and the interpretations of complex mutation patterns, related to cross-resistance and antagonistic interactions.