Discrepant results in the interpretation of HIV-1 drug- resistance genotypic data among widely used algorithms GH Kijak, 1 AE Rubio, 1 SE Pampuro, 1 C Zala, 2 P Cahn, 2 R Galli, 3 JS Montaner 3 and H Salomo Ân 1 1 National Reference Center for AIDS, Department of Microbiology, School of Medicine, University of Buenos Aires, Argentina, 2 Huesped Foundation, Buenos Aires, Argentina and 3 British Columbia Center for Excellence in HIV/AIDS, St. Paul's Hospital, Vancouver, Canada Objectives The aim of this study was to assess the concordance on the interpretation of HIV-1 drug-resistance genotypic data by three widely used algorithms: Stanford University Database (SU), TruGene (Visible Genetics, Canada) (VG) and VirtualPhenotype (Virco, Belgium) (VP). Methods Genotypic data from 293 HIV-1-infected individuals with treatment failure was interpreted for 14 antiretroviral drugs by the three algorithms. Results Complete concordant results among the three systems for all the drugs studied were found in 40/293 (13.7%) samples. Low concordance in the interpretation was observed for most nucleoside reverse transcriptase inhibitors (NRTIs), while results agreed highly for all nonnucleoside reverse transcriptase inhibitors (NNRTIs) and most protease inhibitors (PIs). In pair-wise comparisons, discordant interpretations between SU and VP were found in over 50% of the samples for didanosine, zalcitabine, stavudine and abacavir, and the level of disagreement between VG and VP exceeded 40% for the same drugs. Major discrepancies (high-level resistance interpretation by one algorithm with sensitive interpretation by another) were observed between VG and VP in over 10% of the cases for didanosine, zalcitabine, stavudine and abacavir. On the other hand, the three algorithms had concordant results for lamivudine in over 90% of the cases. Conclusions This work demonstrates the great level of discordance in the interpretation of genotyping results among algorithms, clearly showing the necessity for clinical validation. Moreover, these results suggest that a joint effort from the scienti®c community as well as national and international HIV societies is needed to achieve a consensus for the interpretation of genotypic data. Keywords: algorithms, databases, genotyping tests, HIV-1 drug-resistance Received: 25 April 2002, accepted 4 November 2002 Introduction The use of highly active antiretroviral therapies (HAARTs) has brought about a decrease in the morbidity and mortality associated with HIV/AIDS in the industrialized world [1]. However, antiretroviral (ARV) treatments can fail due to the emergence of drug-resistant viral variants, among other causes. These strains of HIV can be characterized by phenotypic and genotypic assays [2±5]. The former are based on the measurement of the in vitro susceptibility of HIV to ARV-drugs. The latter are based on the detection of mutations in the viral protease (PR) and reverse transcriptase (RT), by DNA sequencing or nucleic acid hybridization assays. Several clinical trials have proved the utility of HIV-1 drug resistance testing for monitoring patients under ARV treat- ment [6±11], and guidelines regarding their use in speci®c clinical settings have been published world-wide [12±16]. These guidelines also point out the importance of the 72 Correspondence: Dr Horacio Salomo Ân, Departamento de MicrobiologõÂa, Facultad de Medicina, U.B.A, Paraguay 2155 piso 11, (1121) Buenos Aires, Argentina. Tel: 54 11 45083689; fax: 54 11 45083705; e-mail: hsalomon@fmed.uba.ar ORIGINAL RESEARCH ß 2003 British HIV Association HIV Medicine (2003), 4, 72±78