Estimating the prevalence of accumulated HIV drug resistance in patients receiving antiretroviral therapy (ART) is difficult due to lack of resistance testing at all occasions of virological failure and in patients with undetectable viral load. A method to estimate this for 6498 EuroSIDA patients who were under follow-up on ART at 1 July 2008 was therefore developed by imputing data on patients with no prior resistance test results, based on the probability of detecting resistance in tested patients with similar profiles.Using all resistance test results available, predicted intermediate/high-level resistance to specific drug classes [nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs)] was derived using the Stanford algorithm v5.1.2. Logistic regression models were then employed to estimate predicted probability of resistance to each drug class for given values of current viral load, history of virological failure and previous virological suppression. Based on these predicted probabilities and patients' covariate profiles, estimates of prevalence in 5355 patients with no prior test results were obtained. Overall prevalence of resistance was estimated by pooling these data with those observed in the remaining 1143 tested patients.Prevalence of NRTI, NNRTI and PI resistance was estimated as 43% (95% confidence interval: 39%-46%), 15% (13%-18%) and 25% (22%-28%), respectively.This method provides estimates for the proportion of treated patients in a cohort who harbour resistance on a given date, which are less likely to be affected by selection bias due to missing resistance data and will allow us to estimate prevalence of resistance to different drug classes at specific timepoints in HIV-infected populations on ART.
Objectives To investigate the ability of several HIV-1 drug-resistance interpretation systems, as well as the number of pre-specified combinations of abacavir-related mutations, to predict virological response to abacavir-containing regimens in antiretroviral therapy-experienced, abacavir-naive patients starting an abacavir-containing regimen in the EuroSIDA cohort. Patients and methods A total of 100 HIV-infected patients with viral load (VL) >500 copies/ml who had a plasma sample available at the time of starting abacavir (baseline) were included. Resistance to abacavir was interpreted by using eight different commonly used systems that consisted of rules-based algorithms or tables of mutations. Correlation between baseline abacavir-resistance mutations and month 6 virological response was performed on this population using a multivariable linear regression model accounting for censored data. Results The baseline VL was 4.36 log 10 RNA copies/ml [interquartile range (IQR): 3.65–4.99 log 10 RNA copies/ml] and the median CD4 cell count was 210 cells/μl (IQR: 67–305 cells/μl). Our patients were pre-exposed to a median of seven antiretrovirals (2–12) before starting abacavir therapy. The median (range) number of abacavir mutations (according to the International AIDS Society-USA) detected at baseline was 3.5 (0–8). Overall, the Kaplan–Meier estimate of the median month 6 VL decline was 0.86 log 10 RNA copies/ml [95% confidence intervals (95% CI): 0.45–1.24]. The VL in those patients ( n=31) who intensified treatment by adding only abacavir decreased by a median 0.20 log 10 RNA copies/ml (95% CI: -0.18; +0.94). The proportion of patients who harboured viruses fully resistant to abacavir among the eight genotypic resistance interpretation algorithms ranged from 12% [Agence Nationale de Recherches sur le SIDA (ANRS)] to 79% [Stanford HIV RT and PR Sequence Database (HIVdb)]. Some interpretation systems showed statistically significant associations between the predicted resistance status and the virological response while others showed no consistent association. The number of active drugs in the regimen was associated with greater virological suppression (additional month 6 VL reduction per additional sensitive drug=0.51, 95% CI: 0.15–0.88, P=0.006); baseline VL was also weakly associated (additional month 6 VL reduction per log 10 higher=0.30, 95% CI: -0.02; +0.62, P=0.06). In contrast, the number of drugs previously received was associated with diminished viral reduction (additional month 6 VL reduction per additional drug=-0.14, 95% CI: -0.28; 0.00, P=0.05). Conclusions Our results revealed a high degree of variability among several genotypic resistance interpretation algorithms currently in use for abacavir. Therefore, the interpretation of genotypic resistance for predicting response to regimens containing abacavir remains a major challenge.
Recently, a IL28B (rs 12979860) gene polymorphism was identified as a predictor for response to hepatits C virus–specific treatment in human immunodeficiency virus (HIV)–uninfected and –infected patients with chronic hepatitis C. In an analysis of HIV-infected patients with acute hepatitis C, we found that the IL28B genotype was associated with serum levels of hepatitis C virus RNA, g-GT, and CD4 cell count. In contrast to HIV-infected patients with chronic hepatitis C, the IL28B genotype was not significantly associated with treatment response rates in patients with acute hepatitis C. Thus, effects of the IL28B single-nucleotide polymorphism may differ in HIV-infected patients with chronic and acute hepatitis C.
Objectives The aim of the study was to establish a methodology for evaluating the hepatitis C continuum of care in HIV /hepatitis C virus ( HCV )‐coinfected individuals and to characterize the continuum in Europe on 1 January 2015, prior to widespread access to direct‐acting antiviral ( DAA ) therapy. Methods Stages included in the continuum were as follows: anti‐ HCV antibody positive, HCV RNA tested, currently HCV RNA positive, ever HCV RNA positive, ever received HCV treatment, completed HCV treatment, follow‐up HCV RNA test, and cure. Sustained virological response ( SVR ) could only be assessed for those with a follow‐up HCV RNA test and was defined as a negative HCV RNA result measured > 12 or 24 weeks after stopping treatment. Results Numbers and percentages for the stages of the HCV continuum of care were as follows: anti‐ HCV positive ( n = 5173), HCV RNA tested (4207 of 5173; 81.3%), currently HCV RNA positive (3179 of 5173; 61.5%), ever HCV RNA positive ( n = 3876), initiated HCV treatment (1693 of 3876; 43.7%), completed HCV treatment (1598 of 3876; 41.2%), follow‐up HCV RNA test to allow SVR assessment (1195 of 3876; 30.8%), and cure (629 of 3876; 16.2%). The proportion that achieved SVR was 52.6% (629 of 1195). There were significant differences between regions at each stage of the continuum ( P < 0.0001). Conclusions In the proposed HCV continuum of care for HIV / HCV ‐coinfected individuals, we found major gaps at all stages, with almost 20% of anti‐ HCV ‐positive individuals having no documented HCV RNA test and a low proportion achieving SVR , in the pre‐ DAA era.
Darunavir/cobicistat/emtricitabine/tenofovir alafenamide (D/C/F/TAF) 800/150/200/10 mg is being investigated in two Phase III trials, AMBER (NCT02431247; treatment-naive adults) and EMERALD (NCT02269917; treatment-experienced, virologically suppressed adults). Week 48 AMBER and EMERALD resistance analyses are presented. Postbaseline samples for genotyping/phenotyping were analyzed from protocol-defined virologic failures (PDVFs) with viral load (VL) ≥400 copies/mL at failure/later time points. Post hoc analyses were deep sequencing in AMBER, and HIV-1 proviral DNA from baseline samples (VL <50 copies/mL) in EMERALD. Through week 48 across both studies, no darunavir, primary PI, or tenofovir resistance-associated mutations (RAMs) were observed in HIV-1 viruses of 1,125 participants receiving D/C/F/TAF or 629 receiving boosted darunavir plus emtricitabine/tenofovir-disoproxil-fumarate. In AMBER, the nucleos(t)ide analog reverse transcriptase inhibitor (N(t)RTI) RAM M184I/V was identified in HIV-1 of one participant during D/C/F/TAF treatment. M184V was detected pretreatment as a minority variant (9%). In EMERALD, in participants with prior VF and genoarchive data (N = 140; 98 D/C/F/TAF and 42 control), 4% had viruses with darunavir RAMs, 38% with emtricitabine RAMs, mainly at position 184 (41% not fully susceptible to emtricitabine), 4% with tenofovir RAMs, and 21% ≥ 3 thymidine analog-associated mutations (24% not fully susceptible to tenofovir) detected at screening. All achieved VL <50 copies/mL at week 48 or prior discontinuation. D/C/F/TAF has a high genetic barrier to resistance; no darunavir, primary PI, or tenofovir RAMs were observed through 48 weeks in AMBER and EMERALD. Only one postbaseline M184I/V RAM was observed in HIV-1 of an AMBER participant. In EMERALD, baseline archived RAMs to darunavir, emtricitabine, and tenofovir in participants with prior VF did not preclude virologic response.
Einleitung: Erste Daten einer retrospektiven Analyse der akuten Hepatitis C Infektion (HCV) bei mit humanem Immundefizienz Virus (HIV) koinfizierten Patienten zeigten ein langanhaltendes virologisches Ansprechens bei 90% der Patienten. In dieser Studie wurde die Effektivität und Sicherheit der frühen Behandlung der akuten Hepatitis C bei HIV-koinfizierten prospektiv untersucht.
Aim To estimate the prevalence of HIV-TDR and ADR in one resistance study of the German ClinSurv-HIV cohort. Method The ClinSurv study is a national open multi-centre long term observational cohort with 15 participating clinical centres (n = 16,750 patients; 31.12.2011). In a resistance study all ClinSurv patients in five centres were identified. Sequences were processed through the Stanford University Genotypic Resistance Interpretation Algorithm ( www. hivdb.stanford.edu; HIVdb version 6.1.1F; 2012 webService version beta-1.0.1) to identify mutations and to determine drug susceptibility. Sequences were analysed by using different lists of mutations (Bennett D. 2009; Johnson V. 2011). Trends in the prevalence of drug resistance mutations were calculated by logistic regression. Results A total of 9,528 patients from five study centres were included into analysis. 4,989 viral sequences were collected from 34% (3,267/9,528) of these patients. 47% (2,365/4,989) of sequences were produced from patients being treatment naïve and 50% (2,495/4,989) from patients under treatment. TDR was identified in 10% (203/1,950) of viral strains. The prevalence of TDR over time was stable at 10.4% (95% CI 9.1–11.8; OR: 0.98; 95% CI 0.92–1.04; p for trend = 0.6; 2001–2011). NRTI-resistance was determined in 7% (128/1,950), followed by 3% NNRTI- and PI-resistance, respectively (NNRTI: 61/1,950; PI: 56/1,950). Prevalence of ADR in treated patients was high (61%; 1,500/2,453 of sequences) but declined significantly over time (OR: 0.8; 95% CI 0.77–0.83; p for trend < 0.001; 2001–2011). Within drug classes NNRTI-resistance was predominant (56%; 834/1503), followed by NRTI-resistance in 52% (1,139/2,194) of sequences of patients with ADR exposed to these drug classes. PI-resistance was identified in 30% (543/1778). Integrase-resistance was determined in 8% (13/161) of integrase-sequences. Discussion Prevalence of TDR is highly stable in this unselected study population, whereas ADR declined significantly over the time, indicating that this decline was presumably influenced by ART related effects, broader resistance testing and resistance test guided therapy.
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