title: Comparative evaluation of three computerized algorithms for prediction of antiretroviral susceptibility from HIV type 1 genotype.
abstract: OBJECTIVE
  To compare three methods for using HIV-1 genotype to predict antiretroviral drug susceptibility.
  
  
  METHODS
  We applied three genotypic interpretation algorithms to 478 reverse transcriptase (RT) and 410 protease sequences for which phenotypic data were available. Sequences were obtained from clinical practice and from published sequences in the Stanford HIV-1 RT and Protease Sequence Database. The genotypic interpretation algorithms included: Stanford HIVdb program (HIVdb), the Visible Genetics/Bayer Diagnostics Guidelines 6.0 (VGI) and a genotypic interpretation program (AntiRetroScan, ARS) developed at the University of Siena, Italy. Genotypic interpretations were normalized to a three-level output: susceptible, intermediate and resistant. Discordances were defined as differences between genotype and phenotype for the same virus isolate. Discordances for which an isolate was considered susceptible by one test but resistant by another test were considered major discordances.
  
  
  RESULTS
  The frequency of major discordances between genotype and phenotype was 10.6, 13.7 and 15.7% for ARS, VGI and HIVdb, respectively (P < 0.0001 for ARS versus HIVdb and for ARS versus VGI; P = 0.002 for VGI versus HIVdb). The correlation between genotype and phenotype was highest for non-nucleoside RT inhibitors and lowest for nucleoside RT inhibitors. Half of the major discordances involved stavudine, didanosine and zalcitabine. The concordance among the three genotypic algorithms was high, with weighted Kappa values ranging between 0.76 and 0.84 for the pairwise comparisons between each of the algorithms.
  
  
  CONCLUSIONS
  Genotype interpretation algorithms correctly predict phenotype in 85-90% of cases, but the rate of concordance is not uniformly distributed among different drugs. These data provide insight into the potential additional benefit derived from phenotyping.
authors.affiliation: Department of Molecular Biology, University of Siena, Siena, Italy. zazzi@unisi.it
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Laura
authors.initials: L
authors.lastname: Romano
authors.forename: Giulietta
authors.initials: G
authors.lastname: Venturi
authors.forename: Robert W
authors.initials: RW
authors.lastname: Shafer
authors.forename: Caroline
authors.initials: C
authors.lastname: Reid
authors.forename: Federico
authors.initials: F
authors.lastname: Dal Bello
authors.forename: Cristina
authors.initials: C
authors.lastname: Parolin
authors.forename: Giorgio
authors.initials: G
authors.lastname: Palù
authors.forename: Pier E
authors.initials: PE
authors.lastname: Valensin
mesh_terms: D000465:Algorithms; D019380:Anti-HIV Agents / Q000737:chemistry / Q000494:pharmacology*; D003198:Computer Simulation; D005838:Genotype; D006678:HIV / Q000187:drug effects* / Q000235:genetics*; D054303:HIV Reverse Transcriptase / Q000737:chemistry / Q000235:genetics; D006801:Humans; D010641:Phenotype; D018894:Reverse Transcriptase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D013329:Structure-Activity Relationship
chemical_list: D019380:Anti-HIV Agents; D018894:Reverse Transcriptase Inhibitors; D054303:HIV Reverse Transcriptase