As HIV-positive people age, diagnosis and management of comorbidities associated 13 with ageing are of increasing concern. In this study we aimed to compare the self-14

Hill et al. provide a critique[1] of our recent paper, in which we developed two novel methods for estimating the rate of reactivation from latency directly from existing clinical data [2]. Our goal was to use minimal assumptions and be guided by the data. We found that across four cohorts, the best-fit frequency of HIV reactivation from latency was once every 5–8 days. Hill et al.’s modeling demonstrates an alternative approach, in which they start with a fixed rate of reactivation (four events per day, derived from modeling of time to drug resistance under therapy [3,4]) and then adjust the model and other parameters to fit their fixed reactivation rate. Importantly, amongst the various fitting described by Hill et al., they never actually fit the reactivation rate, instead always fixing it at their preferred value and adjusting the model and fitting other parameters around it. Hill et al. state that the data can be fit with “modest variation” of the reactivation rate. However, the parameters used require that ≈8% of patients reactivate less than once every 6 days (the mean rate we estimated in [2]) and, similarly, ≈8% reactivate more than 100 times per day (see S1 Methods). Below, we show that once the reactivation rate is fitted to all datasets, the results strongly support that the median frequency of HIV reactivation from latency is once every 5–8 days. The first criticism by Hill et al. is that we did not incorporate multiple reactivation events or a distribution of reactivation rates into our model. They use simulation to incorporate such a distribution because of “lack [of] an analytical expression for the model” of multiple reactivation events. In response, we now derive an analytical approximation incorporating multiple reactivation events and apply it to all four available datasets (see S1 Methods). In each case, we fitted reactivation rate and initial viral load (V0), plus the distribution in reactivation rate. Because viral growth is a measureable parameter, we fixed this in the model, firstly using Hill et al.’s chosen viral growth rate (0.4/day) and then using a more realistic value obtained directly from the data (0.8/day) (see S1 Methods). Fig 1 shows fitting to all four datasets of (i) our original two-parameter model (panels A–D), (ii) the model with multiple reactivation events using Hill et al.’s preferred low growth rate (E–H), and (iii) a model with a more realistic growth rate (I–L). We note that only in 1 out of 12 fits (cohort 3 with a low growth rate, panel G, Fig 2) did we obtain a reactivation rate more frequent than once every 2 days. In all cases with realistic growth rates, the estimated rate of HIV reactivation from latency was very similar to our original model (once every 5–8 days) even once a distribution was included (Fig 1I–1L; Fig 2). Thus, the results presented by Hill et al. are highly dependent on the choice of a low viral growth rate and require an extremely wide distribution in reactivation rates. Fig 1 Estimating HIV reactivation rate using different models. Fig 2 Frequency of HIV reactivation from latency estimated using different models: The mean frequency of reactivation estimated using the original model (red circles, corresponding to panels A–D of Fig 1), Hill’s model with slow growth rate ... Hill et al. raise additional arguments about distributions in viral growth rate. In our previous publication, we found no significant correlation between growth rate and time-to-detection. We agree with Hill et al. that there is always a risk of a type II error in such analyses. However, the simulation performed by Hill et al. significantly misrepresents the probability of this error. Stating they observed no significant correlation (p > 0.05) in 50% of simulations grossly overstates the probability of observing the correlation seen in the data. The correlation between growth rate and time-to-detection in cohort 3 was in fact positive (r = 0.029, p = 0.9) despite an expected negative correlation. Thus, it is more appropriate to ask either, “In what proportion of simulations did we see an r value of 0.029 or greater?” or, “In what proportion of simulations was the p value greater than 0.9?” Using the parameters suggested in Hill et al.’s Fig 1D [1], we find that less than 2% of simulations were as poorly correlated as the observed data. Thus, we maintain our view that although growth rate must affect time-to-detection, based on the data this contribution is likely small. Hill et al.’s primary argument is that there is strong prior evidence of high reactivation rates, and they cite four papers in support (references 1, 3–5 in their comment). None of the papers they cite in support of their reactivation rate of four-per-day actually analyses data on “reactivation from latency after treatment interruption” (the title of our earlier publication [2]). Two of these papers [3,5] base their estimates on time-to-drug-resistance under therapy, using a wide variety of assumptions on HIV replication under therapy, mutation rates, and drug selection. The third paper [6] measures T cell activation, not latent cell reactivation, so it does not provide an estimate. The fourth estimate comes from Hill et al.’s own previous work[4] but is derived directly from the estimate of reference [3] (see S1 Methods). Thus, the evidence for a reactivation rate of 4 per day hinges upon reference [3]’s use of a 13-parameter model of time to drug resistance, in which many of the input parameters are described as “roughly based on the literature”[3]. Hill et al. also argue that reactivation must be high because of a lack of correlation between reservoir size and rebound time in the literature. This appears to ignore recent publications showing such a correlation [7–9] as well as the apparent wide variation between different measures of reservoir size from peripheral blood [10]. Hill et al. have gone to some lengths to try to show that their previous estimate of HIV reactivation of 4 times a day could be made compatible with the data from the cohorts presented in Pinkevych et al. However, the failure to actually fit the key parameter significantly undermines these arguments. Once rigorous quantitative fitting is applied, the parameters suggested by Hill et al. do not provide the best fit to the data. Indeed, the additional analyses undertaken herein provide strong support for our previous estimate that HIV reactivates from latency approximately once every 5–8 days. The derivation of the analytical approximation and a detailed explanation of the modeling are provided in S1 Methods because of word limits on this reply.

Infection with human immunodeficiency virus (HIV) may result in a variety of hair and nail changes, some of which may be the initial manifestation of the disease. Telogen effluvium, which presents as an acute to subacute diffuse noninflammatory alopecia, is the most common type of HIV-related hair loss. Hair straightening is a characteristic sign of HIV infection, especially in black patients. Onychomycosis is often a sign of HIV disease progression in an otherwise asymptomatic individual. Onychomycosis is most commonly caused by Trichophyton rubrum in both HIV-infected and non-infected individuals. The most common organism causing WSO is T. rubrum in HIV-infected patients and T. mentagrophytes in non-HIV-infected individuals. The antimicrobial susceptibility of organisms causing onychomycosis in HIV-infected patients appears to be the same as that in non-HIV-infected patients; therefore, treatment does …

16‐17 July 2010, International AIDS Society’s Workshop “Towards a Cure”: HIV Reservoirs and Strategies to Control Them, Vienna, Austria

Address: 1National Centre in HIV Epidemiology and Clinical Research, University of New South Wales; St. Vincent's Hospital, Sydney, Australia, 2Centre for Immunology, St. Vincent's Hospital, Sydney, Australia, 3National Centre in HIV Epidemiology and Clinical Research, University of New South Wales; St. Vincent's Hospital, Darlinghurst, Sydney, Australia, 4St. Vincent's Hospital, Sydney, Australia and 5National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, Sydney, Australia * Corresponding author

Methods HIV-infected adults (viral load < 50) on atazanavir/ritonavir 300/100 mg once-daily plus two NRTIs for at least 2 weeks were enrolled. Two 24-hour intensive PK were studied at baseline and 14 days after switching to atazanavir/ ritonavir 200/100 mg once-daily regimen plus two NRTIs. Atazanavir plasma concentrations were calculated using non-compartmental methods. A repeated measures GEE/ random effect model was used to compare the two dose levels. Comparison between the different subgroups were made using the Mann-Whitney U model.

It is not fully elucidated whether patients who receive antiretroviral therapy (ART) can maintain continued CD4 count increases. Previous studies suggested a plateau 2-4 years after treatment initiation. We aimed to characterize the evolution of CD4 counts in HIV-infected individuals receiving long-term suppressive ART, by performing a retrospective study of patients who maintained viral suppression (HIV RNA <400 copies/ml) for > or =5 years. We used linear regression models to determine for each individual whether the CD4 count continued to increase or plateau. Furthermore, we estimated whether the slope of the CD4 count for each individual became zero, which we defined as the CD4 set-point. We assessed factors associated with continued CD4 count rise, reaching a CD4 set-point and time to the CD4 set-point. Fifty-nine patients were included. The median baseline CD4 count was 238 (IQR, 120-360) cells/microl and the median duration on ART was 7.6 (IQR, 5.9-9.3) years. On ART, CD4 count continued to increase in 37 subjects (63%). Significant predictors of continued CD4 count increase included a lower baseline log10 HIV RNA (OR, 0.35; 95% CI, 0.14-0.89; p=0.026) and a shorter duration on ART (OR, 0.65; 95% CI, 0.47-0.91; p=0.021). Twenty-four (41%) subjects reached a set-point after a median 4.3 (IQR 1.8-6.4) years on ART. A lower baseline CD4 percentage was associated with both a longer time to reach the CD4 set-point and a lower CD4 count at the CD4 set-point. These findings suggest that CD4 count may continue to increase in some patients after several years of ART. Our results point to an advantage to commencing ART at higher CD4+ T cell strata. These data should be considered when estimating the optimal time to initiate ART.

The nonnucleoside reverse transcriptase inhibitors (NNRTI) have low genetic barriers to resistance. Resistance can sometimes be overcome by increasing drug exposure. We assessed factors associated with 48-week virological response in treatment-experienced individuals receiving NNRTI therapy including resistance testing results and plasma drug exposure. Of 62 individuals assigned a new NNRTI-based regimen following resistance testing therapy consisted of efavirenz in 35 (56%) and nevirapine in 27 (44%) individuals. NNRTI fold change (FC) was determined from resistance test at baseline and plasma drug concentration at week 4. Mean time weighted change from baseline VL was -0.68 log over 48 weeks. Significant associations with change from baseline VL included baseline VL and FC whereas plasma drug concentration was not associated. In this cohort of highly treatment-experienced individuals treated with NNRTI regimens, we did not observe a significant association between NNRTI plasma concentration and virological response.

BACKGROUND:Interleukin (IL)-7 levels are increased in patients with human immunodeficiency virus type 1 (HIV-1)-associated lymphopenia; however, the effects of this on IL-7 receptor (IL-7R) expression, disease progression, and immune reconstitution remain unclear.METHODS:Plasma IL-7 levels were measured, by enzyme-linked immunoassay, in patients with primary, chronic, or long-term nonprogressive HIV-1 infection (PHI, CHI, and LTNP, respectively) before and after 40-48 weeks of antiretroviral therapy (ART). Cell-surface expression and intracellular expression of the IL-7R components CD127 and CD132 were measured by flow cytometry. The effects of IL-7 and cycloheximide on IL-7R expression by peripheral blood mononuclear cells were examined in vitro.RESULTS:Plasma IL-7 levels were increased in both patients with PHI and those with CHI; administration of ART resulted in normalized plasma IL-7 levels in patients with PHI but not in those with CHI. Plasma IL-7 levels positively correlated with CD4(+) T cell immune reconstitution in patients with PHI. In vitro, exogenous IL-7 rapidly down-regulated cell-surface CD127 expression, but not CD132 expression, whereas subsequent reexpression required active protein synthesis. HIV-1 infection resulted in progressive decreases in the CD127(+)132(-) subset and increases in the CD127(-)132(+) subset of CD4(+) and CD8(+) T cells. Changes in CD4(+) T cell expression of IL-7R components were evident in patients with LTNP who lost viral control, and these changes preceded increases in plasma IL-7 levels.CONCLUSIONS:Perturbations in the IL-7/IL-7R system were clearly associated with disease progression but did not reliably predict immune reconstitution.

Background/aims: Non-occupational HIV post-exposure prophylaxis (NPEP) is routinely prescribed after high risk sexual exposure. This provides an opportunity to screen and treat individuals at risk of concurrent sexually transmitted infections (STI). The aim of this study was to assess the efficacy of an STI screening programme in individuals receiving NPEP.Methods: STI screens were offered to all individuals receiving NPEP from March 2001 to May 2004. Screen results were compared to type of sexual exposure and baseline patient characteristics.Results: A total of 253 subjects were screened, representing 85% of the target population. All were men who have sex with men (MSM). Common exposure risks were receptive anal intercourse (RAI) in 61% and insertive anal intercourse (IAI) in 33%. 32 (13%) individuals had one or more STI. The most common STIs were rectal infections with Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) in 11 (4.5%) and six (2.5%) individuals, respectively. Subjects with rectal CT were significantly more likely to be co-infected with rectal NG (p < 0.001). There was no association between the presence of a rectal STI and age or exposure risk. Only six (19%) individuals with an STI were symptomatic at screening.Conclusion: In this cohort of MSM receiving NPEP, high rates of concomitant STIs are observed highlighting the importance of STI screening in this setting.

Background Tenofovir disoproxil fumarate (Tenofovir DF, TDF), the first nucleotide reverse transcriptase inhibitor approved for the treatment of HIV disease, has been associated with renal dysfunction in isolated cases. The aim of this study was to assess changes in renal parameters in individuals receiving TDF- and non-TDF-containing highly active antiretroviral therapy (HAART).Methods All individuals on HAART attending our clinic were included in the analysis. Time-weighted changes in serum creatinine, calculated creatinine clearance (CCrCl) and anion-gap were assessed for individuals on TDF- and non-TDF HAART.Results Of 948 individuals on HAART, 290 (31%) and 618 (65%) were on TDF- and non-TDF HAART, with 40 (4%) having ceased TDF HAART. Baseline values for serum creatinine, CCrCl and anion-gap were similar for those on TDF- and non-TDF HAART. In a multivariate analysis, statistically significant differences were observed in time-weighted change from baseline in anion-gap and CCrCl between individuals on TDF- and non-TDF HAART [mean difference in change between groups: anion-gap 0.78 mmol/L (standard error, 0.19) and CCrCl-6.80 (standard error 2.2); P = 0.005 and P = 0.032, respectively] after adjusting for baseline anion-gap and CCrCl, respectively. Two cases of TDF-associated renal failure were observed.Conclusion Overt renal failure with TDF HAART is rare. However, subtle but statistically significant changes in anion-gap and CCrCl were observed which were associated with TDF HAART. These parameters may be of use in monitoring individuals on HAART.

Introduction The use of HIV protease inhibitors (PIs) in a ritonavir (RTV)-boosted form is now common. However, randomized data comparing boosted with unboosted PI strategies are scarce. Methods This randomized, open-label trial compared indinavir (IDV) 800 mg three times daily with IDV/RTV 800/100 mg twice daily, both given with zidovudine (AZT)/lamivudine (3TC) twice daily in individuals with at least 3 months previous AZT experience. The primary endpoint was the time-weighted average change in HIV RNA from baseline. Designed as a 48-week study, follow-up continued until week 112. Primary analysis is by intention to treat. Results One hundred and three patients commenced therapy and are included in the analysis. Patients had a median of 29 months past nucleoside reverse transcriptase inhibitor (NRTI) exposure. Baseline median (interquartile range) log10 HIV RNA was 4.0 (3.3–4.5) and CD4+T-cell count 166 (40–323) cells/μl. After 112-weeks of study there was no significant difference observed between arms in the mean (sd) change in time-weighted average HIV RNA from baseline (-1.6 [1.1] HIV RNA copies/week/ml three times daily arm; -1.4 [1.1] HIV RNA copies/week/ml twice daily arm; P=0.3). Both arms were associated with substantial toxicity expressed as serious adverse events and study drug interruptions. The twice daily arm experienced greater dyslipidaemia. Mean (sd) changes in time-weighted CD4+ T-cell count from baseline were similar [88 (84) cells/week/μl three times daily arm; 70 [109] cells/week/μl twice daily arm; P=0.3). Conclusions RTV-boosted IDV 800/100 mg twice daily demonstrated comparable efficacy to unboosted IDV 800mg three times daily dosing. Both regimens were associated with substantial toxicity. Use of lower doses of RTV-boosted IDV may result in better tolerability without loss of efficacy and warrant further research.

Background:Little is known about the prevalence and pattern of hepatitis B virus (HBV) mutations in HIV/HBV co-infected individuals on long-term lamivudine (3TC) therapy. Methods:HBV polymerase/envelope/basal core promoter/pre-core sequences from 81 HIV–HBV co-infected persons who received at least 6 months 3TC were compared to HBV reference sequences. Host and viral characteristics associated with HBV mutations were determined. Results:HBV viraemia was detected in 53 persons (65%) and was associated with lower CD4 cell count nadir and higher HIV RNA at the time of testing but not with 3TC duration. Known 3TC-resistant mutations occurred in 50% and 94% of viremic patients with < 2 years and > 4 years 3TC, respectively. The CD4 cell count at testing was significantly higher in those with 3TC-resistant mutations. The triple polymerase mutant (rtL173V, rtL180M, rtM204V), which behaves as a vaccine escape mutant in vitro, occurred in 17% of viraemic patients. Polymerase mutations that may confer resistance to other anti-HBV agents were also detected. Conclusions:In HIV–HBV co-infected patients, greater immunocompromise is associated with continued HBV viraemia while on 3TC, and development of 3TC-resistant mutations are inevitable with prolonged 3TC use. These mutant viruses may limit future therapeutic options due to cross-resistance or may produce HBV vaccine escape mutants. Thus, timing and selection of antiretroviral therapy is critical in this population.

Objective To compare three versions of the objective HIV-associated lipodystrophy (HIVLD) case definition (LDCD) and derived severity scale to spontaneous clinical LD assessment in adults initiating antiretroviral therapy. Design and main outcome measures: The LDCD versions were the ‘primary’ LDCD [which includes dual-energy X-ray absorptiometry (DXA) and computerized tomography (CT)], a simpler ‘central’ LDCD that omits CT data, and a simpler but probably less accurate ‘non-imaging’ LDCD. Physician LD assessments were passively reported. Two of the 10 parameters in the primary LDCD were not collected and were imputed. Setting, participants and interventions Retrospective analysis of a randomized, placebo-controlled, 144-week study of tenofovir DF or stavudine (d4T) in 600 anti-retroviral-naive adults. Results Central LDCD and clinical assessment diagnosed LD in 27% and 19% of d4T recipients at week 144, respectively ( P<0.001), and 3% and 3% of tenofovir DF recipients, respectively ( P=0.248). The central LDCD performed at least as well as the primary LDCD; both were more sensitive than the non-imaging model. There was poor concordance between clinical and LDCD-based diagnosis (kappa 0.02–0.20); most clinical cases did not fulfill any LDCD. Using the central LDCD, most LD was grade 1; 6% of d4T recipients and no tenofovir DF recipient had grade 3–4 LD at week 144 ( P=0.007). Independent risk factors for LD using the central LDCD were d4T, increasing age, female sex and higher baseline triglycerides, whereas clinical assessment consistently identified only d4T. The LDCD score was more sensitive than DXA for assessing LD severity. Conclusions In this prospective study of a first antiretroviral regimen, the LDCD was more sensitive for LD diagnosis and identified more lipodystrophy risk factors than spontaneous clinical assessment or DXA, and also objectively quantified LD severity. The central LDCD should make objective LD assessment cheaper and simpler. Spontaneous clinical LD assessment of is of limited value, even in placebo-controlled trials.

ABSTRACT All current human immunodeficiency virus (HIV) vaccine candidates contain multiple viral components and elicit antibodies that react positively in licensed HIV diagnostic tests, which contain similar viral products. Thus, vaccine trial participants could be falsely diagnosed as infected with HIV. Additionally, uninfected, seropositive vaccinees may encounter long-term social and economic harms. Moreover, this also interferes with early detection of true HIV infections during preventive HIV vaccine trials. An HIV-seropositive test result among uninfected vaccine trial participants is a major public health concern for volunteers who want to participate in future HIV vaccine trials. Based on the increased number of HIV vaccines being tested globally, it is essential to differentiate vaccine- from virus-induced antibodies. Using a whole-HIV-genome phage display library, we identified conserved sequences in Env-gp41 and Gag-p6 which are recognized soon after infection, do not contain protective epitopes, and are not part of most current HIV vaccines. We established a new HIV serodetection assay based on these peptides. To date, this assay, termed HIV-SELECTEST, demonstrates >99% specificity and sensitivity. Importantly, in testing of plasma samples from multiple HIV vaccine trials, uninfected trial participants scored negative, while all intercurrent infections were detected within 1 to 3 months of HIV infection. The new HIV-SELECTEST is a simple but robust diagnostic tool for easy implementation in HIV vaccine trials and blood banks worldwide.

The immune response in HIV-infected individuals who carry HLA-B27 is characterized by an immunodominant cytotoxic T lymphocyte (CTL) response to a conserved epitope corresponding to amino acids 263-272 of HIV-1 p24 gag. The arginine at position 264 is a crucial anchor residue. Amino acid substitution at 264 from arginine (R) to glycine (G), lysine (K), or threonine (T) results in a low affinity peptide that binds to HLA-B27 inefficiently and is poorly recognized by T cells that respond to the wild-type peptide. These mutants have been characterized as CTL escape mutations. We studied the plasma virus of 20 HLA-B27 long-term nonprogressors: 14 were wild type and 6 were found to be mutant. Five of these carried known escape mutations coding for K or G at position 264. One patient demonstrated a previously undescribed R264Q mutation in 30/31 clones. This altered epitope failed to elicit an IFN-gamma response from PBMC isolated from any of four HLA-B27-positive individuals with strong responses to wild-type peptide. A peptide binding assay confirmed that the R264Q mutant peptide had 30-fold lower binding affinity to HLA-B27 compared to wild type. Therefore, the R264Q variant is a likely novel escape mutation in HLA-B27-positive individuals.

OBJECTIVESAtazanavir is a recently approved HIV protease inhibitor (PI). As with other PIs, careful attention to potential pharmacokinetic drug interactions in clinical practice is necessary. The aim of this study was to assess the clinical associations with plasma atazanavir concentrations in HIV-positive individuals.METHODSIndividuals established on an atazanavir-containing regimen, completed an interviewer-administered questionnaire recording atazanavir dosing characteristics, concomitant medication use and adherence. After completion, plasma atazanavir concentrations were measured.RESULTSOf 100 individuals, mean trough plasma atazanavir concentrations (mug/L) were 282 (95% CI 95-468, n = 19) and 774 (95% CI 646-902, n = 81) in those on non- and ritonavir-boosted atazanavir regimens, respectively. Eighty-five individuals had HIV RNA <50 copies/mL. Seven individuals had atazanavir plasma concentrations below the assay limit of detection (<50 microg/L), all of whom had undetectable plasma HIV RNA. In a multivariate analysis, nevirapine use was associated with significantly lower trough atazanavir concentrations (P = 0.011) and lopinavir/ritonavir use with higher trough atazanavir concentrations (P = 0.032). Dosing characteristics (including food taken), concomitant medications (including drugs used for dyspepsia) and HIV RNA were not significantly associated with trough atazanavir concentrations.CONCLUSIONSIn this cohort, despite the wide inter-individual variability of atazanavir trough concentrations, no significant association with dosing characteristics, concomitant medication (with the exception of nevirapine and lopinavir/ritonavir) or virological response was observed. Further work is needed to assess the optimal dosing regimen when using atazanavir with nevirapine.