Improved virological outcome in White patients infected with HIV-1 non-B subtypes compared to subtype B.

BACKGROUND: Antiretroviral compounds have been predominantly studied in human immunodeficiency virus type 1 (HIV-1) subtype B, but only ~10% of infections worldwide are caused by this subtype. The analysis of the impact of different HIV subtypes on treatment outcome is important. METHODS: The effect of HIV-1 subtype B and non-B on the time to virological failure while taking combination antiretroviral therapy (cART) was analyzed. Other studies that have addressed this question were limited by the strong correlation between subtype and ethnicity. Our analysis was restricted to white patients from the Swiss HIV Cohort Study who started cART between 1996 and 2009. Cox regression models were performed; adjusted for age, sex, transmission category, first cART, baseline CD4 cell counts, and HIV RNA levels; and stratified for previous mono/dual nucleoside reverse-transcriptase inhibitor treatment. RESULTS: Included in our study were 4729 patients infected with subtype B and 539 with non-B subtypes. The most prevalent non-B subtypes were CRF02_AG (23.8%), A (23.4%), C (12.8%), and CRF01_AE (12.6%). The incidence of virological failure was higher in patients with subtype B (4.3 failures/100 person-years; 95% confidence interval [CI], 4.0-4.5]) compared with non-B (1.8 failures/100 person-years; 95% CI, 1.4-2.4). Cox regression models confirmed that patients infected with non-B subtypes had a lower risk of virological failure than those infected with subtype B (univariable hazard ratio [HR], 0.39 [95% CI, .30-.52; P < .001]; multivariable HR, 0.68 [95% CI, .51-.91; P = .009]). In particular, subtypes A and CRF02_AG revealed improved outcomes (multivariable HR, 0.54 [95% CI, .29-.98] and 0.39 [95% CI, .19-.79], respectively). CONCLUSIONS: Improved virological outcomes among patients infected with non-B subtypes invalidate concerns that these individuals are at a disadvantage because drugs have been designed primarily for subtype B infections.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

Superinfection with drug-resistant HIV is rare and does not contribute substantially to therapy failure in a large European cohort.

BACKGROUND: Superinfection with drug resistant HIV strains could potentially contribute to compromised therapy in patients initially infected with drug-sensitive virus and receiving antiretroviral therapy. To investigate the importance of this potential route to drug resistance, we developed a bioinformatics pipeline to detect superinfection from routinely collected genotyping data, and assessed whether superinfection contributed to increased drug resistance in a large European cohort of viremic, drug treated patients. METHODS: We used sequence data from routine genotypic tests spanning the protease and partial reverse transcriptase regions in the Virolab and EuResist databases that collated data from five European countries. Superinfection was indicated when sequences of a patient failed to cluster together in phylogenetic trees constructed with selected sets of control sequences. A subset of the indicated cases was validated by re-sequencing pol and env regions from the original samples. RESULTS: 4425 patients had at least two sequences in the database, with a total of 13816 distinct sequence entries (of which 86% belonged to subtype B). We identified 107 patients with phylogenetic evidence for superinfection. In 14 of these cases, we analyzed newly amplified sequences from the original samples for validation purposes: only 2 cases were verified as superinfections in the repeated analyses, the other 12 cases turned out to involve sample or sequence misidentification. Resistance to drugs used at the time of strain replacement did not change in these two patients. A third case could not be validated by re-sequencing, but was supported as superinfection by an intermediate sequence with high degenerate base pair count within the time frame of strain switching. Drug resistance increased in this single patient. CONCLUSIONS: Routine genotyping data are informative for the detection of HIV superinfection; however, most cases of non-monophyletic clustering in patient phylogenies arise from sample or sequence mix-up rather than from superinfection, which emphasizes the importance of validation. Non-transient superinfection was rare in our mainly treatment experienced cohort, and we found a single case of possible transmitted drug resistance by this route. We therefore conclude that in our large cohort, superinfection with drug resistant HIV did not compromise the efficiency of antiretroviral treatment.

Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel.

CONTEXT: New trial data and drug regimens that have become available in the last 2 years warrant an update to guidelines for antiretroviral therapy (ART) in human immunodeficiency virus (HIV)-infected adults in resource-rich settings. OBJECTIVE: To provide current recommendations for the treatment of adult HIV infection with ART and use of laboratory-monitoring tools. Guidelines include when to start therapy and with what drugs, monitoring for response and toxic effects, special considerations in therapy, and managing antiretroviral failure. DATA SOURCES, STUDY SELECTION, AND DATA EXTRACTION: Data that had been published or presented in abstract form at scientific conferences in the past 2 years were systematically searched and reviewed by an International Antiviral Society-USA panel. The panel reviewed available evidence and formed recommendations by full panel consensus. DATA SYNTHESIS: Treatment is recommended for all adults with HIV infection; the strength of the recommendation and the quality of the evidence increase with decreasing CD4 cell count and the presence of certain concurrent conditions. Recommended initial regimens include 2 nucleoside reverse transcriptase inhibitors (tenofovir/emtricitabine or abacavir/lamivudine) plus a nonnucleoside reverse transcriptase inhibitor (efavirenz), a ritonavir-boosted protease inhibitor (atazanavir or darunavir), or an integrase strand transfer inhibitor (raltegravir). Alternatives in each class are recommended for patients with or at risk of certain concurrent conditions. CD4 cell count and HIV-1 RNA level should be monitored, as should engagement in care, ART adherence, HIV drug resistance, and quality-of-care indicators. Reasons for regimen switching include virologic, immunologic, or clinical failure and drug toxicity or intolerance. Confirmed treatment failure should be addressed promptly and multiple factors considered. CONCLUSION: New recommendations for HIV patient care include offering ART to all patients regardless of CD4 cell count, changes in therapeutic options, and modifications in the timing and choice of ART in the setting of opportunistic illnesses such as cryptococcal disease and tuberculosis.

Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1: a pharmacogenetics study.

BACKGROUND:HIV-1-infected patients vary considerably by their response to antiretroviral treatment, drug concentrations in plasma, toxic events, and rate of immune recovery. This variability could have a genetic basis. We did a pharmacogenetics study to analyse the association between response to antiretroviral treatment and allelic variants of several genes. METHODS:In 123 patients, we did PCR analyses of the gene for the multidrug-resistance transporter (MDR1), which codes for P-glycoprotein, of genes coding for isoenzymes of cytochrome P450, CYP3A4, CYP3A5, CYP2D6, and CYP2C19, and of the gene for the chemokine receptor CCR5. We measured concentrations in plasma of the antiretroviral agents efavirenz and nelfinavir by high-performance liquid-chromatography, and measured levels of P-glycoprotein expression, CD4-cell count, and HIV-1 viraemia. FINDINGS: Median drug concentrations in patients with the MDR1 3435 TT, CT, and CC genotypes were at the 30th, 50th, and 75th percentiles, respectively (p=0.0001). In patients with CYP2D6 extensive-metaboliser or poor-metaboliser alleles, median drug concentrations were at percentiles 45 and 62.5, respectively (p=0.04). Patients with the MDR1 TT genotype 6 months after starting treatment had a greater rise in CD4-cell count (257 cells/microL) than patients with the CT (165 cells/microL) and CC (121 cells/microL) genotype (p=0.0048), and the best recovery of naïve CD4-cells. INTERPRETATION:The polymorphism MDR1 3435 C/T predicts immune recovery after initiation of antiretroviral treatment. This finding suggests that P-glycoprotein has an important role in admittance of antiretroviral drugs to restricted compartments in vivo.