Objectives
To explore the effectiveness and durability of integrase strand transfer inhibitor (INSTI)‐based regimens in pre‐treated subjects.
Methods
Treatment‐experienced individuals starting an ...INSTI‐based regimen during 2012–2019 were selected from the INTEGRATE collaborative study. The time to virological failure VF: one measurement of viral load (VL) ≥ 1000 copies/mL or two ≥ 50 copies/ml or one VL measurement ≥ 50 copies/mL followed by treatment change and to INSTI discontinuation were evaluated.
Results
Of 13 560 treatments analysed, 4284 were from INSTI‐naïve, non‐viraemic (IN‐NV) individuals, 1465 were from INSTI‐naïve, viraemic (IN‐V) individuals, 6016 were from INSTI‐experienced, non‐viraemic (IE‐NV) individuals and 1795 were from INSTI‐experienced, viraemic (IE‐V) individuals. Major INSTI drug resistance mutations (DRMs) were previously detected in 4/519 (0.8%) IN‐NV, 3/394 (0.8%) IN‐V, 7/1510 (0.5%) IE‐NV and 25/935 (2.7%) IE‐V individuals. The 1‐year estimated probabilities of VF were 3.1% 95% confidence interval (CI): 2.5–3.8 in IN‐NV, 18.4% (95% CI: 15.8–21.2) in IN‐V, 4.2% (95% CI: 3.6–4.9) in IE‐NV and 23.9% (95% CI: 20.9–26.9) in IE‐V subjects. The 1‐year estimated probabilities of INSTI discontinuation were 12.1% (95% CI: 11.1–13.0) in IN‐NV, 19.6% (95% CI: 17.5–21.6) in IN‐V, 10.8% (95% CI: 10.0–11.6) in IE‐NV and 21.7% (95% CI: 19.7–23.5) in IE‐V subjects.
Conclusions
Both VF and INSTI discontinuation occur at substantial rates in viraemic subjects. Detection of DRMs in a proportion of INSTI‐experienced individuals makes INSTI resistance testing mandatory after failure.
Objectives
Nucleoside reverse transcriptase inhibitor (NRTI) transmitted drug resistance mutations (TDRMs) could increase the risk of virological failure (VF) of first‐line integrase strand transfer ...inhibitor (InSTI)‐based regimens.
Methods
Patients starting two NRTIs (lamivudine/emtricitabine plus abacavir/tenofovir) plus raltegravir or dolutegravir were selected from the EuResist cohort. The role of NRTI genotypic susceptibility score and of specific TDRMs in VF (i.e. two consecutive viral loads > 50 HIV‐1 RNA copies/mL or a single viral load ≥ 200 copies/mL after 3 months from antiretroviral therapy start) was evaluated in the overall population and according to the InSTI employed.
Results
From 2008 to 2017, 1095 patients were eligible for the analysis (55.5% men, median age 39 years). In all, 207 VFs occurred over 1023 patient‐years of follow‐up. The genotypic susceptibility score (GSS) had no effect on the risk of VF in the overall population. However, the presence of M184V/I independently predicted VF of raltegravir‐ but not dolutegravir‐based therapy when compared with a fully‐active backbone adjusted hazard ratio (aHR) = 3.09, P = 0.035, particularly when associated with other non‐thymidine analogue mutations (aHR = 27.62, P = 0.004). Higher‐zenith HIV‐RNA and lower nadir CD4 counts independently predicted VF.
Conclusions
NRTI backbone TDRMs increased the risk of VF with raltegravir‐based but not dolutegravir‐based regimens.
In this Phase I trial, patients' peripheral blood dendritic cells were pulsed with peptides eluted from the surface of autologous glioma cells. Three biweekly intradermal vaccinations of ...peptide-pulsed dendritic cells were administered to seven patients with glioblastoma multiforme and two patients with anaplastic astrocytoma. Dendritic cell vaccination elicited systemic cytotoxicity in four of seven tested patients. Robust intratumoral cytotoxic and memory T-cell infiltration was detected in two of four patients who underwent reoperation after vaccination. This Phase I study demonstrated the feasibility, safety, and bioactivity of an autologous peptide-pulsed dendritic cell vaccine for patients with malignant glioma.
Objectives
The EuResist expert system is a novel data‐driven online system for computing the probability of 8‐week success for any given pair of HIV‐1 genotype and combination antiretroviral therapy ...regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment.
Methods
The EuResist system was compared with 10 HIV‐1 drug resistance experts for the ability to predict 8‐week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success.
Results
There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6–13 for the human experts mean±standard deviation (SD) 9.1±1.9. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter‐rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%).
Conclusions
With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment‐decision support tool in clinical practice.
Objectives
This observational study in antiretroviral treatment‐experienced, HIV‐1‐infected adults explored the efficacy of etravirine plus darunavir/ritonavir (DRV group; n = 999) vs. etravirine ...plus an alternative boosted protease inhibitor (other PI group; n = 116) using pooled European cohort data.
Methods
Two international (EuroSIDA; EUResist Network) and five national (France, Italy, Spain, Switzerland and UK) cohorts provided data (collected in 2007–2012). Stratum‐adjusted (for confounding factors) Mantel–Haenszel differences in virological responses (viral load < 50 HIV‐1 RNA copies/mL) and odds ratios (ORs) with 95% confidence intervals (CIs) were derived.
Results
Baseline characteristics were balanced between groups except for previous use of antiretrovirals (≥ 10: 63% in the DRV group vs. 49% in the other PI group), including previous use of at least three PIs (64% vs. 53%, respectively) and mean number of PI resistance mutations (2.3 vs. 1.9, respectively). Week 24 responses were 73% vs. 75% (observed) and 49% vs. 43% (missing = failure), respectively. Week 48 responses were 75% vs. 73% and 32% vs. 30%, respectively. All 95% CIs around unadjusted and adjusted differences encompassed 0 (difference in responses) or 1 (ORs). While ORs by cohort indicated heterogeneity in response, for pooled data the difference between unadjusted and adjusted for cohort ORs was small.
Conclusions
These data do not indicate a difference in response between the DRV and other PI groups, although caution should be applied given the small size of the other PI group and the lack of randomization. This suggests that the efficacy and virology results from DUET can be extrapolated to a regimen of etravirine with a boosted PI other than darunavir/ritonavir.
Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), can lead to hospitalisation, particularly in elderly, immunocompromised, ...and non-vaccinated or partially vaccinated individuals. Although vaccination provides protection, the duration of this protection wanes over time. Additional doses can restore immunity, but the influence of viral variants, specific sequences, and vaccine-induced immune responses on disease severity remains unclear. Moreover, the efficacy of therapeutic interventions during hospitalisation requires further investigation. The study aims to analyse the clinical course of COVID-19 in hospitalised patients, taking into account SARS-CoV-2 variants, viral sequences, and the impact of different vaccines. The primary outcome is all-cause in-hospital mortality, while secondary outcomes include admission to intensive care unit and length of stay, duration of hospitalisation, and the level of respiratory support required. This ongoing multicentre study observes hospitalised adult patients with confirmed SARS-CoV-2 infection, utilising a combination of retrospective and prospective data collection. It aims to gather clinical and laboratory variables from around 35,000 patients, with potential for a larger sample size. Data analysis will involve biostatistical and machine-learning techniques. Selected patients will provide biological material. The study started on October 14, 2021 and is scheduled to end on October 13, 2026. The analysis of a large sample of retrospective and prospective data about the acute phase of SARS CoV-2 infection in hospitalised patients, viral variants and vaccination in several European and non-European countries will help us to better understand risk factors for disease severity and the interplay between SARS CoV-2 variants, immune responses and vaccine efficacy. The main strengths of this study are the large sample size, the long study duration covering different waves of COVID-19 and the collection of biological samples that allows future research.
Antiretroviral therapy (ART) has been successfully introduced in low-middle income countries. However an increasing rate of ART failure with resistant virus is reported. We therefore described the ...pattern of drug resistance mutations at antiretroviral treatment (ART) failure in a real-life Tanzanian setting using the remote genotyping procedure and thereafter predicted future treatment options using rule-based algorithm and the EuResist bioinformatics predictive engine. According to national guidelines, the default first-line regimen is tenofovir + lamivudine + efavirenz, but variations including nevirapine, stavudine or emtricitabine can be considered. If failure on first-line ART occurs, a combination of two nucleoside reverse transcriptase inhibitors (NRTIs) and boosted lopinavir or atazanavir is recommended.
Plasma was obtained from subjects with first (n = 174) or second-line (n = 99) treatment failure, as defined by clinical or immunological criteria, as well as from a control group of ART naïve subjects (n = 17) in Dar es Salaam, Tanzania. Amplification of the pol region was performed locally and the amplified DNA fragment was sent to Sweden for sequencing (split genotyping procedure). The therapeutic options after failure were assessed by the genotypic sensitivity score and the EuResist predictive engine. Viral load was quantified in a subset of subjects with second-line failure (n = 52).
The HIV-1 pol region was successfully amplified from 55/174 (32%) and 28/99 (28%) subjects with first- or second-line failure, respectively, and 14/17 (82%) ART-naïve individuals. HIV-1 pol sequence was obtained in 82 of these 97 cases (84.5%). Undetectable or very low (<2.6 log10 copies/10-3 L) viral load explained 19 out of 25 (76%) amplification failures in subjects at second-line ART failure. At first and second line failure, extensive accumulation of NRTI (88% and 73%, respectively) and NNRTI (93% and 73%, respectively) DRMs but a limited number of PI DRMs (11% at second line failure) was observed. First line failure subjects displayed a high degree of cross-resistance to second-generation NNRTIs etravirine (ETR; 51% intermediate and 9% resistant) and rilpivirine (RPV; 12% intermediate and 58% resistant), and to abacavir (ABC; 49% resistant) which is reserved for second line therapy in Tanzania. The predicted probability of success with the best salvage regimen at second-line failure decreased from 93.9% to 78.7% when restricting access to the NRTIs, NNRTIs and PIs currently available in Tanzania compared to when including all approved drugs.
The split genotyping procedure is potential tool to analyse drug resistance in Tanzania but the sensitivity should be evaluated further. The lack of viral load monitoring likely results in a high false positive rate of treatment failures, unnecessary therapy switches and massive accumulation of NRTI and NNRTI mutations. The introduction of regular virological monitoring should be prioritized and integrated with drug resistance studies in resource limited settings.
Human immunodeficiency virus (HIV) can develop resistance to all antiretroviral drugs. Multidrug resistance, however, is a rare event in modern HIV treatment, but can be life‐threatening, particular ...in patients with very long therapy histories and in areas with limited access to novel drugs. To understand the evolution of multidrug resistance, we analyzed the EuResist database to uncover the accumulation of mutations over time. We hypothesize that the accumulation of resistance mutations is not acquired simultaneously and randomly across viral genotypes but rather tends to follow a predetermined order. The knowledge of this order might help to elucidate potential mechanisms of multidrug resistance. Our evolutionary model shows an almost monotonic increase of resistance with each acquired mutation, including less well‐known nucleoside reverse transcriptase (RT) inhibitor‐related mutations like K223Q, L228H, and Q242H. Mutations within the integrase (IN) (T97A, E138A/K G140S, Q148H, N155H) indicate high probability of multidrug resistance. Hence, these IN mutations also tend to be observed together with mutations in the protease (PR) and RT. We followed up with an analysis of the mutation‐specific error rates of our model given the data. We identified several mutations with unusual rates (PR: M41L, L33F, IN: G140S). This could imply the existence of previously unknown virus variants in the viral quasispecies. In conclusion, our bioinformatics model supports the analysis and understanding of multidrug resistance.
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