title: Simple algorithm derived from a geno-/phenotypic database to predict HIV-1 protease inhibitor resistance.
abstract: BACKGROUND
  Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance.
  
  
  OBJECTIVE
  Development of an algorithm to predict PI phenotype from genotypic data.
  
  
  METHODS
  Recombinant viruses containing patient-derived protease genes were analysed for sensitivity to indinavir, saquinavir, ritonavir and nelfinavir. Drug resistance-associated mutations were determined by direct sequencing. geno- and phenotypic data were compared for 119 samples from 97 HIV-1 infected patients.
  
  
  RESULTS
  Samples with one or two mutations in the gene for the protease were phenotypically sensitive in 74.3%, whereas 83.6% of samples with five or more mutations were resistant against all PI tested. Some mutations (361, 63P, 71V/T, 771) were frequent both in sensitive and resistant samples, whereas others (241, 30N, 461/L, 48V, 54V, 82A/F/T/S, 84V, 90M) were predominantly present in resistant samples. Therefore, the presence or absence of a single drug resistance-associated mutation predicted phenotypic PI resistance with high sensitivity (96.5-100%) but low specificity (13.3-57.4%). A more specific algorithm was obtained by taking into account the total number of drug resistance-associated mutations in the gene for the protease and restricting these to certain key positions for the PI. The algorithm was subsequently validated by analysis of 72 independent samples.
  
  
  CONCLUSIONS
  With an optimized algorithm, phenotypic PI resistance can be predicted by viral genotype with good sensitivity (89.1-93.0%) and specificity (82.6-93.3%). The reliability and relevance of this algorithm should be further evaluated in clinical practice.
authors.affiliation: Institute of Clinical and Molecular Virology, German National Reference Centre for Retroviruses, University of Erlangen-Nürnberg, Erlangen.
authors.forename: B
authors.initials: B
authors.lastname: Schmidt
authors.forename: H
authors.initials: H
authors.lastname: Walter
authors.forename: B
authors.initials: B
authors.lastname: Moschik
authors.forename: C
authors.initials: C
authors.lastname: Paatz
authors.forename: K
authors.initials: K
authors.lastname: van Vaerenbergh
authors.forename: A M
authors.initials: AM
authors.lastname: Vandamme
authors.forename: M
authors.initials: M
authors.lastname: Schmitt
authors.forename: T
authors.initials: T
authors.lastname: Harrer
authors.forename: K
authors.initials: K
authors.lastname: Uberla
authors.forename: K
authors.initials: K
authors.lastname: Korn
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy* / Q000821:virology; D000465:Algorithms; D016208:Databases, Factual; D004352:Drug Resistance, Microbial / Q000235:genetics; D005838:Genotype; D017320:HIV Protease Inhibitors / Q000494:pharmacology* / Q000627:therapeutic use; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans; D008969:Molecular Sequence Data; D010641:Phenotype; D017354:Point Mutation; D012680:Sensitivity and Specificity
chemical_list: D017320:HIV Protease Inhibitors