Kristien Van Vaerenbergh

Simple algorithm derived from a geno-/phenotypic database to predict HIV-1 protease inhibitor resistance

BackgroundResistance 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. ObjectiveDevelopment of an algorithm to predict PI phenotype from genotypic data. MethodsRecombinant 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. ResultsSamples 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 (36I, 63P, 71V/T, 77I) were frequent both in sensitive and resistant samples, whereas others (24I, 30N, 46I/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. ConclusionWith 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.

Evaluation of two commercial kits for the detection of genotypic drug resistance on a panel of HIV type 1 subtypes A through J.

We compared the two commercially available sequencing kits for HIV-1 drug resistance testing, the ViroSeq Genotyping System (Applied Biosystems, Foster City, CA, U.S.A.) and the TRUGENE HIV-1 Genotyping Kit (Visible Genetics, Inc., Toronto, Ontario, Canada), with our in-house genotyping system. Fifteen viral isolates from African patients (6 treated and 9 untreated) covering a panel of HIV-1 subtypes A through J and 7 plasma samples from Belgian and African patients (2 treated and 5 untreated) were tested. All the samples could be amplified and sequenced by the three systems; however, for all systems, alternative amplification/sequencing primers had to be used for some samples belonging to subtype B as well as to other subtypes. The consensus sequence was partially derived from only one strand for the in-house system and for the ViroSeq Genotyping System. The TRUGENE HIV-1 Genotyping Kit scored the highest number of ambiguities, followed by the ViroSeq Genotyping System and the in-house system. For 11 samples, these differences in reporting mixtures affected 14 resistance-related positions, which altered the interpretation toward protease inhibitors for 2 samples when using version 1.2 RetroGram software (Virology Networks, Utrecht, The Netherlands). All three systems were able to sequence diluted samples with a viral load down to 10 3 or 10 4 RNA copies/ml. Our data therefore suggest that the performance of amplification and sequencing primers must be improved to allow fast and reliable resistance testing for all HIV-1 subtypes.

Nonadherence to highly active antiretroviral therapy: clinically relevant patient categorization based on electronic event monitoring.

Adherence to highly active antiretroviral therapy (HAART) is crucial, but which aspects of drug-taking behavior are important remain largely unknown. In a prospective observational study, 43 HIV-1-infected patients taking HAART underwent electronic event monitoring (EEM). Taking adherence was defined as the percentage of doses taken compared with the number prescribed, dosing adherence was defined as the percentage of days on which all doses were taken, and timing adherence was defined as the percentage of doses taken within 1 hr of the time prescribed. Drug holidays were defined as periods of no drug intake for >24 hr. Cluster analysis, including the four EEM parameters, was used and refined to construct an algorithm to discriminate patients. Patients were categorized as nonadherent if they had a taking adherence of <90%, or a dosing adherence of <75% and at least 1 drug holiday, or a timing adherence of <80% and at least 1 drug holiday, or >6 drug holidays per 100 days. All four EEM parameters differed significantly (p < 0.0001) between the two groups. Adherent patients had a better outcome, as shown by a larger drop in viral load (p = 0.011) and rise in CD4+ cell count (p = 0.035), showing that the algorithm-based categorization is clinically relevant.

A Combination of Poor Adherence and a Low Baseline Susceptibility Score is Highly Predictive for HAART Failure

The relationship between adherence, virological response to highly active antiretroviral therapy (HAART) and the presence and development of genotypic resistance was assessed in 41 HIV-infected patients on HAART. Four adherence parameters (drug taking adherence, dosing adherence, timing adherence and drug holidays) were scored prospectively using electronic event monitoring. Genotypic resistance at baseline and after therapy failure was scored retrospectively and a genotype-based susceptibility score was calculated. Overall median adherence rates were high. All adherence parameters were better in virological responders (n=31) compared to non-responders (n=10), drug taking adherence and number of drug holidays being significantly different. Responders had a significantly higher susceptibility score. Stepwise logistic regression showed that the number of drug holidays and a low susceptibility score were highly predictive for therapy failure. Despite the presence of a limited number of baseline resistance mutations, perfectly adherent patients can control virus replication for a prolonged period.