HIV-1 Protease and Reverse Transcriptase Mutation Patterns Responsible for Discordances Between Genotypic Drug Resistance Interpretation Algorithms

Abstract

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 x 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.

Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance

“…We have rediscovered the majority of known resistant mutations to the three drugs (10) and uncovered several interacting structures for these mutations. Particularly, for protease we have discovered a conditional independence structure among the mutations M46I, I54V, and V82A that is consistent with several previous experimental results (3,5,6, but has not been documented in the literature. Our MD simulations and free energy analyses have further confirmed and provided the molecular basis and implication of this conditional independence.…”

“…There are also rule-based systems that infer drug-resistance levels from sequence information such as the Stanford University HIV Drug Resistance Database (Stanford HIVdb). However, these methods provide little insight on the genetic and molecular basis of drug resistance and often give inconsistent results when analyzing the same input mutation data (4,6).…”

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

“…As new drugs are developed for the care of HIV-positive patients, novel mutations are recognized, which prompt the International AIDS Society of USA [Johnson et al, 2005], whose authority is recognized worldwide, to update regularly the spectrum of HIV-1 resistance-associated mutations. However, even though an exhaustive IAS mutation list is accessible to clinicians and many free-of-charge websites [Beerenwinkel et al, 2003;Kuiken et al, 2003;Rhee et al, 2003] furnish a computer-assisted interpretation of mutational profiles, some discrepancies continue to exist between these resistance profiles and response to therapy [Kijak et al, 2003;Ravela et al, 2003;Sturmer et al, 2003;Torti et al, 2003;De Luca et al, 2004]. Although good compliance with treatment regimens [Paterson et al, 2000;Cingolani et al, 2002], optimal antiviral potency [Daar, 2003;Gathe, 2003], and adequate drug concentrations [Yasuda et al, 2004] are major concerns for obtaining a sustained control of viral replication, the major obstacle to realizing a successful regimen capable of providing a sustained control of viral replication regards some unresolved questions related to HIV-1 resistance.…”

Résistance du VIH aux antirétroviraux

“…Les études cliniques VIRADAPT et CPCRA 046 (GART) ont contribué à valider ce test [37,38]. La décision clinique consécutive à un test de génotypage est souvent prise après analyse d'algorithmes (conçus pour interpréter les résultats de ces tests) ou d'opinions d'experts qui peuvent présen-ter certaines discordances [39]. La mise en place de laboratoires et d'infrastructures bio-informatiques répon-dant aux conditions d'utilisation d'une telle technologie demeure relativement lourde et coûteuse.…”