Low-frequency drug-resistant HIV-1 and risk of virological failure to first-line NNRTI-based ART: a multicohort European case-control study using centralized ultrasensitive 454 pyrosequencing

Objectives It is still debated if pre-existing minority drug-resistant HIV-1 variants (MVs) affect the virological outcomes of first-line NNRTI-containing ART. Methods This Europe-wide case-control study included ART-naive subjects infected with drug-susceptible HIV-1 as revealed by population sequencing, who achieved virological suppression on first-line ART including one NNRTI. Cases experienced virological failure and controls were subjects from the same cohort whose viraemia remained suppressed at a matched time since initiation of ART. Blinded, centralized 454 pyrosequencing with parallel bioinformatic analysis in two laboratories was used to identify MVs in the 1%-25% frequency range. ORs of virological failure according to MV detection were estimated by logistic regression. Results Two hundred and sixty samples (76 cases and 184 controls), mostly subtype B (73.5%), were used for the analysis. Identical MVs were detected in the two laboratories. 31.6% of cases and 16.8% of controls harboured pre-existing MVs. Detection of at least one MV versus no MVs was associated with an increased risk of virological failure (OR = 2.75, 95% CI = 1.35-5.60, P = 0.005); similar associations were observed for at least one MV versus no NRTI MVs (OR = 2.27, 95% CI = 0.76-6.77, P = 0.140) and at least one MV versus no NNRTI MVs (OR = 2.41, 95% CI = 1.12-5.18, P = 0.024). A dose-effect relationship between virological failure and mutational load was found. Conclusions Pre-existing MVs more than double the risk of virological failure to first-line NNRTI-based AR

Consensus statement of the European guidelines on clinical management of HIV-1 tropism testing

Tenth International Congress on Drug Therapy in HIV Infection 7-11 November 2010 Glasgow, UKIntroduction: Testing for HIV tropism is recommended before prescribing a chemokine receptor blocker. To date, in most European countries HIV tropism is determined using a phenotypic test. Recently, new data have emerged supporting the use of a genotypic HIV V3-loop sequence analysis as the basis for tropism determination. The European guidelines group on clinical management of HIV-1 tropism testing was established to make recommendations to clinicians and virologists. Methods: We searched online databases for articles from Jan 2006 until March 2010 with the terms: tropism or CCR5-antagonist or CCR5 antagonist or maraviroc or vicriviroc. Additional articles and/or conference abstracts were identified by hand searching. This strategy identified 712 potential articles and 1240 abstracts. All were reviewed and finally 57 papers and 42 abstracts were included and used by the panel to reach a consensus statement. Results: The panel recommends HIV-tropism testing for the following indications: i) drug-naïve patients in whom toxicity or limited therapeutic options are foreseen; ii) patients experiencing therapy failure whenever a treatment change is considered. Both the phenotypic Enhanced Trofile assay (ESTA) and genotypic population sequencing of the V3-loop are recommended for use in clinical practice. Although the panel does not recommend one methodology over another it is anticipated that genotypic testing will be used more frequently because of its greater accessibility, lower cost and shorter turnaround time. The panel also provides guidance on technical aspects and interpretation issues. If using genotypic methods, triplicate PCR amplification and sequencing testing is advised using the G2P interpretation tool (clonal model) with an FPR of 10%. If the viral load is below the level of reliable amplification, proviral DNA can be used, and the panel recommends performing triplicate testing and use of an FPR of 10%. If genotypic DNA testing is not performed in triplicate the FPR should be increased to 20%. Conclusions: The European guidelines on clinical management of HIV-1 tropism testing provide an overview of current literature, evidence-based recommendations for the clinical use of tropism testing and expert guidance on unresolved issues and current developments. Current data support both the use of genotypic population sequencing and ESTA for co-receptor tropism determination. For practical reasons genotypic population sequencing is the preferred method in Europe.Ye

Alternative methods to analyse the impact of HIV mutations on virological response to antiviral therapy

<p>Abstract</p> <p>Background</p> <p>Principal component analysis (PCA) and partial least square (PLS) regression may be useful to summarize the HIV genotypic information. Without pre-selection each mutation presented in at least one patient is considered with a different weight. We compared these two strategies with the construction of a usual genotypic score.</p> <p>Methods</p> <p>We used data from the ANRS-CO3 Aquitaine Cohort Zephir sub-study. We used a subset of 87 patients with a complete baseline genotype and plasma HIV-1 RNA available at baseline and at week 12. PCA and PLS components were determined with all mutations that had prevalences >0. For the genotypic score, mutations were selected in two steps: 1) p-value < 0.01 in univariable analysis and prevalences between 10% and 90% and 2) backwards selection procedure based on the Cochran-Armitage Test. The predictive performances were compared by means of the cross-validated area under the receiver operating curve (AUC).</p> <p>Results</p> <p>Virological failure was observed in 46 (53%) patients at week 12. Principal components and PLS components showed a good performance for the prediction of virological response in HIV infected patients. The cross-validated AUCs for the PCA, PLS and genotypic score were 0.880, 0.868 and 0.863, respectively. The strength of the effect of each mutation could be considered through PCA and PLS components. In contrast, each selected mutation contributes with the same weight for the calculation of the genotypic score. Furthermore, PCA and PLS regression helped to describe mutation clusters (e.g. 10, 46, 90).</p> <p>Conclusion</p> <p>In this dataset, PCA and PLS showed a good performance but their predictive ability was not clinically superior to that of the genotypic score.</p

A historical reflection on the discovery of human retroviruses

The discovery of HIV-1 as the cause of AIDS was one of the major scientific achievements during the last century. Here the events leading to this discovery are reviewed with particular attention to priority and actual contributions by those involved. Since I would argue that discovering HIV was dependent on the previous discovery of the first human retrovirus HTLV-I, the history of this discovery is also re-examined. The first human retroviruses (HTLV-I) was first reported by Robert C. Gallo and coworkers in 1980 and reconfirmed by Yorio Hinuma and coworkers in 1981. These discoveries were in turn dependent on the previous discovery by Gallo and coworkers in 1976 of interleukin 2 or T-cell growth factor as it was called then. HTLV-II was described by Gallo's group in 1982. A human retrovirus distinct from HTLV-I and HTLV-II in that it was shown to have the morphology of a lentivirus was in my mind described for the first time by Luc Montagnier in an oral presentation at Cold Spring Harbor in September of 1983. This virus was isolated from a patient with lymphadenopathy using the protocol previously described for HTLV by Gallo. The first peer reviewed paper by Montagnier's group of such a retrovirus, isolated from two siblings of whom one with AIDS, appeared in Lancet in April of 1984. However, the proof that a new human retrovirus (HIV-1) was the cause of AIDS was first established in four publications by Gallo's group in the May 4th issue of Science in 1984

Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time

Background: Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford's HIVdb) to predict virological outcome at 12, 24, and 48 weeks. Methodology/Principal Findings: Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8-16) weeks (2152 TCEs), 24 (16-32) weeks (2570 TCEs), and 48 (44-52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92±1.17, compared to Rega and ANRS, with 2.22±1.09 and 2.23±1.05, respectively. However, similar odds ratio's were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5-1.7] for HIVdb, 1.7 [1.5-1.8] for ANRS, and 1.7 [1.9-1.6] for Rega. Odds ratio's increased over time, but remained comparable (odds ratio's ranging between 1.9-2.1 at 24 weeks and 1.9-2.

Factors Influencing the Emergence and Spread of HIV Drug Resistance Arising from Rollout of Antiretroviral Pre-Exposure Prophylaxis (PrEP)

Background: The potential for emergence and spread of HIV drug resistance from rollout of antiretroviral (ARV) pre-exposure prophylaxis (PrEP) is an important public health concern. We investigated determinants of HIV drug resistance prevalence after PrEP implementation through mathematical modeling. Methodology: A model incorporating heterogeneity in age, gender, sexual activity, HIV infection status, stage of disease, PrEP coverage/discontinuation, and HIV drug susceptibility, was designed to simulate the impact of PrEP on HIV prevention and drug resistance in a sub-Saharan epidemic. Principal Findings: Analyses suggest that the prevalence of HIV drug resistance is influenced most by the extent and duration of inadvertent PrEP use in individuals already infected with HIV. Other key factors affecting drug resistance prevalence include the persistence time of transmitted resistance and the duration of inadvertent PrEP use in individuals who become infected on PrEP. From uncertainty analysis, the median overall prevalence of drug resistance at 10 years was predicted to be 9.2% (interquartile range 6.9%-12.2%). An optimistic scenario of 75% PrEP efficacy, 60% coverage of the susceptible population, and 5% inadvertent PrEP use predicts a rise in HIV drug resistance prevalence to only 2.5% after 10 years. By contrast, in a pessimistic scenario of 25% PrEP efficacy, 15% population coverage, and 25% inadvertent PrEP use, resistance prevalence increased to over 40%. Conclusions: Inadvertent PrEP use in previously-infected individuals is the major determinant of HIV drug resistance prevalence arising from PrEP. Both the rate and duration of inadvertent PrEP use are key factors. PrEP rollout programs should include routine monitoring of HIV infection status to limit the spread of drug resistance. © 2011 Abbas et al

CD4 cell count response to first-line combination ART in HIV-2+patients compared with HIV-1+patients: a multinational, multicohort European study

BACKGROUND: CD4 cell recovery following first-line combination ART (cART) is poorer in HIV-2+ than in HIV-1+ patients. Only large comparisons may allow adjustments for demographic and pretreatment plasma viral load (pVL). METHODS: ART-naive HIV+ adults from two European multicohort collaborations, COHERE (HIV-1 alone) and ACHIeV2e (HIV-2 alone), were included, if they started first-line cART (without NNRTIs or fusion inhibitors) between 1997 and 2011. Patients without at least one CD4 cell count before start of cART, without a pretreatment pVL and with missing a priori-defined covariables were excluded. Evolution of CD4 cell count was studied using adjusted linear mixed models. RESULTS: We included 185 HIV-2+ and 30321 HIV-1+ patients with median age of 46 years (IQR 36–52) and 37 years (IQR 31–44), respectively. Median observed pretreatment CD4 cell counts/mm3 were 203 (95% CI 100–290) in HIV-2+ patients and 223 (95% CI 100–353) in HIV-1+ patients. Mean observed CD4 cell count changes from start of cART to 12 months were +105 (95% CI 77–134) in HIV-2+ patients and +202 (95% CI 199–205) in HIV-1+ patients, an observed difference of 97 cells/mm3 in 1 year. In adjusted analysis, the mean CD4 cell increase was overall 25 CD4 cells/mm3/year lower (95% CI 5–44; P = 0.0127) in HIV-2+ patients compared with HIV-1+ patients. CONCLUSIONS: A poorer CD4 cell increase during first-line cART was observed in HIV-2+ patients, even after adjusting for pretreatment pVL and other potential confounders. Our results underline the need to identify more potent therapeutic regimens or strategies against HIV-2