Rapid Communication: HIV-1 Protease and Reverse Transcriptase

Summary and Introduction

Several rules-based algorithms have been developed to interpret results of HIV-1 genotypic resistance tests. To assess the concordance of these algorithms and to identify sequences causing interalgorithm discordances, we applied four publicly available algorithms to the sequences of isolates from 2,045 individuals in northern California. Drug resistance interpretations were classified as S for susceptible, I for intermediate, and R for resistant. Of 30,675 interpretations (2,045 sequences

15 drugs), 4.4% were completely discordant, with at least one algorithm assigning an S and another an R; 29.2% were partially discordant, with at least one algorithm assigning an S and another an I, or at least one algorithm assigning an I and another an R; and 66.4% displayed complete concordance, with all four algorithms assigning the same interpretation. Discordances between nucleoside reverse transcriptase inhibitor interpretations usually resulted from several simple, frequently occurring mutational patterns. Discordances between protease inhibitor interpretations resulted from a larger number of more complex mutation patterns. Discordances between nonnucleoside reverse transcriptase inhibitor interpretations were uncommon and resulted from a small number of individual drug resistance mutations. Determining the clinical significance of these mutation patterns responsible for interalgorithm discordances will improve interalgorithm concordance and the accuracy of genotypic resistance interpretation.

Several expert panels have recommended that drug resistance testing be used to help select optimal drug therapy for HIV-1-infected persons, and genotypic drug resistance testing has become routine in many developed countries.^[1,2] The manual interpretation of HIV-1 genotypic assays, however, is difficult because a large number of protease and reverse transcriptase (RT) drug resistance mutations interact and emerge in complex patterns. Several genotypic resistance interpretation algorithms have been developed that use protease and RT sequences to assess HIV-1 susceptibility to each of the available antiretroviral drugs. To quantify the extent of interalgorithm concordance and to identify mutations responsible for interalgorithm discordances, we applied four publicly available algorithms to a large set of clinical sequences. Here, we describe the results of these comparisons and the mutation patterns responsible for interalgorithm discordances.