title: [Study of resistance using the TRUGENE HIV-1 genotyping system and analysis of agreement between rule-based algorithms and virtual phenotyping].
abstract: BACKGROUND
  This study attempts to describe the results obtained in the HIV resistance study performed with clinical samples obtained from patients receiving "HAART" therapy and to compare the results using different interpretation algorithms.
  
  
  METHODS
  397 samples have been analysed (TRUGENE HIV-1 GENOTYPING kit). The results were interpreted with the algorithms Visible Genetics and Retrogram. The concordance interalgorithm was done for 105 of these samples, including the virtual phenotype interpretation.
  
  
  RESULTS
  The samples corresponded to multi regimen failure (39%), first and second failure (30.7% and 27.1% respectively). A 27.6% of the samples were wild type. The more frequent mutations to the ITIAN were T215Y/F (37.2%) and M184V (32.9%) following by other NAMS. The 69 insertion and Q151M complex had low representativity. For the ITINN K103N (25.8%), Y181C (11.2%) and G190A (10.9%). For the IP: key mutations L90M (26.1%), M46I (18.1%) and V82AFTS (12.9%); and accessory mutations L63P (50.5%), A71V (27.2%), L10I (25.2%) and M36I (19.2%). Low correlation was observed between interpretation systems, mainly for ITIAN and IP, being the virtual phenotype more flexible in the assignation of resistance.
  
  
  CONCLUSIONS
  The requests for HIV resistance testing were similar for the three groups of patients. Many of the failures were the consequence of a poor adherence to the therapy. The mutation pattern found corresponded with the "TARGA" therapy. The low correlation found between interpretation systems, makes necessary the elaboration of a consensus algorithm.
authors.affiliation: Servicio de Microbiología, Hospital Universitario de Valme, Sevilla, Spain. mariac.nogales.sspa@juntadeandalucia.es
authors.forename: María Carmen
authors.initials: MC
authors.lastname: Nogales
authors.forename: María Carmen
authors.initials: MC
authors.lastname: Serrano
authors.forename: Samuel
authors.initials: S
authors.lastname: Bernal
authors.forename: Rafael
authors.initials: R
authors.lastname: Jarana
authors.forename: Luis
authors.initials: L
authors.lastname: Pérez de la Rosa
authors.forename: Ana
authors.initials: A
authors.lastname: Fernández-Palacín
authors.forename: Carmen
authors.initials: C
authors.lastname: Almeida
authors.forename: Estrella
authors.initials: E
authors.lastname: Martín-Mazuelos
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution; D019380:Anti-HIV Agents / Q000494:pharmacology; D023241:Antiretroviral Therapy, Highly Active; D003062:Codon / Q000235:genetics; D024921:Drug Resistance, Multiple, Viral; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology; D016333:HIV Protease / Q000235:genetics; D017320:HIV Protease Inhibitors / Q000494:pharmacology; D054303:HIV Reverse Transcriptase / Q000235:genetics; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics / Q000302:isolation & purification; D006801:Humans; D008828:Microbiological Techniques; D020125:Mutation, Missense; D010349:Patient Compliance; D010641:Phenotype; D017354:Point Mutation; D012367:RNA, Viral / Q000235:genetics; D015203:Reproducibility of Results; D018894:Reverse Transcriptase Inhibitors / Q000494:pharmacology; D014773:Virology / Q000379:methods*
chemical_list: D019380:Anti-HIV Agents; D003062:Codon; D017320:HIV Protease Inhibitors; D012367:RNA, Viral; D018894:Reverse Transcriptase Inhibitors; D054303:HIV Reverse Transcriptase; D016333:HIV Protease