title: A direct comparison of drug susceptibility to HIV type 1 from antiretroviral experienced subjects as assessed by the antivirogram and PhenoSense assays and by seven resistance algorithms.
abstract: HIV-1 drug resistance methodologies are being increasingly utilized to guide treatment decisions; however, information comparing the various assays is limited. Duplicate plasma samples from 70 ART-experienced subjects were analyzed by both the Antivirogram and PhenoSense phenotypic assays and the results compared. HIV genotypes were also obtained and analyzed using seven different resistance algorithms. These results were also compared with the phenotypic assay results. Concordances between the phenotypic tests and between each algorithm, and between the two phenotypic assays were calculated and kappa coefficients (KC) determined. Overall agreement between the two phenotypic assays was good (86.9% concordance; KC 0.621). The highest concordance by drug class was seen for protease inhibitors (93.4%; KC 0.679) and the lowest (79.8%; KC 0.549) for nucleoside reverse transcriptase inhibitors. Concordance between the two phenotypic assays, when evaluating individual drugs, was good for all drugs tested except for abacavir, zalcitabine, and indinavir. Agreement between the seven algorithms and each phenotypic assay was variable, though most had good or excellent agreement. The highest overall level of agreement for an individual drug was observed when comparing lamivudine susceptibility to either assay. Concordance for abacavir, didanosine, zalcitabine, and saquinavir was generally problematic when comparing one or more phenotypic assays to the drug resistance predictive algorithms. In conclusion, results comparing these two phenotypic tests were mostly similar, but comparisons of the predictive resistance algorithms for specific drugs, as well as to specific phenotypic assays, were more inconsistent.
authors.affiliation: International Clinical Virology, GlaxoSmithKline, Research Triangle Park, NC 27709, USA. lisa.l.ross@gsk.com
authors.forename: Lisa
authors.initials: L
authors.lastname: Ross
authors.forename: Ronan
authors.initials: R
authors.lastname: Boulmé
authors.forename: Robin
authors.initials: R
authors.lastname: Fisher
authors.forename: Jaime
authors.initials: J
authors.lastname: Hernandez
authors.forename: Allison
authors.initials: A
authors.lastname: Florance
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Schmit
authors.forename: Vanessa
authors.initials: V
authors.lastname: Williams
mesh_terms: D000328:Adult; D000465:Algorithms; D024882:Drug Resistance, Viral; D005260:Female; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology*; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics*; D006801:Humans; D008297:Male; D008826:Microbial Sensitivity Tests / Q000379:methods*; D010641:Phenotype; D011237:Predictive Value of Tests; D017422:Sequence Analysis, DNA; D014764:Viral Proteins / Q000235:genetics
chemical_list: D014764:Viral Proteins