JAC Advance Access published online on December 19, 2003
Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkh021
© 2003 by The British Society for Antimicrobial Chemotherapy
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Original article
1 Department of Molecular
Biology, University of Siena, Siena, Italy;
* Corresponding author. E-mail: zazzi{at}unisi.it.
Received 29 April 2003
; revised 23 July 2003
; accepted 13 October 2003
Objectives: 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.
Keywords: antiretroviral drug resistance, genotype, phenotype,
virtual phenotype
Comparative evaluation of three computerized algorithms
for prediction of antiretroviral susceptibility from HIV type 1
genotype
2 Division of Infectious Diseases,
Stanford University, Stanford, CA;
3 Bayer HealthCare, Diagnostics Division, Toronto,
Canada;
4 Department
of Histology, Microbiology and Medical Biotechnologies,University
of Padova, Padova, Italy
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