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Enregistrement W4362636282 · doi:10.1101/2023.04.05.23288183

HIV-1 drug resistance in people on dolutegravir-based ART: Collaborative analysis of cohort studies

2023· preprint· en· W4362636282 sur OpenAlexaffabout
Tom Loosli, Stefanie Hossmann, Suzanne M Ingle, Hajra Okhai, Katharina Kusejko, Johannes P. Mouton, Pantxika Bellecave, Ard van Sighem, Melanie Stecher, Antonella d’Arminio Monforte, M. John Gill, Caroline Sabin, Gary Maartens, Huldrych F. Günthard, Jonathan A C Sterne, Richard Lessells, Matthias Egger, Roger D. Kouyos

Notice bibliographique

RevuemedRxiv · 2023
Typepreprint
Langueen
DomaineMedicine
ThématiqueHIV/AIDS drug development and treatment
Établissements canadiensAlberta Hip and Knee ClinicUniversity of Calgary
Organismes subventionnairesMedical Research CouncilNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
Mots-clésDolutegravirHuman immunodeficiency virus (HIV)HIV drug resistanceDrug resistanceCohortMedicineVirologyDrugAntiretroviral therapyPharmacologyViral loadInternal medicineBiologyGenetics

Résumé

récupéré en direct d'OpenAlex

Summary Background The widespread use of the integrase strand transfer inhibitor (INSTI) dolutegravir (DTG) in first- and second-line antiretroviral therapy (ART) may facilitate emerging resistance. We combined data from HIV cohorts to examine patterns of drug resistance mutations (DRMs) and identify risk factors for DTG resistance. Methods Eight cohorts from Canada, Europe, and South Africa contributed data on individuals with genotypic resistance testing on DTG-based ART. Resistance levels were categorised using the Stanford algorithm. We identified risk factors for resistance using mixed-effects ordinal logistic regression models. Results We included 750 people with genotypic resistance testing on DTG-based ART between 2013 and 2022. Most had HIV subtype B (N=444, 59·2%) and were treatment-experienced; 134 (17.9%) were on DTG dual and 19 (2.5%) on DTG monotherapy. INSTI DRMs were detected in 100 (13·3%) individuals; 21 (2·8%) had more than one mutation. Most (N=713, 95·1%) were susceptible to DTG, 8 (1·1%) had potential-low, 5 (0·7%) low, 18 (2·4%) intermediate and 6 (0·8%) high-level DTG resistance. The risk of DTG resistance was higher on DTG monotherapy (adjusted odds ratio (aOR) 37·25, 95% CI 11·17 to 124·2) and DTG lamivudine dual therapy (aOR 6·59, 95% CI 1·70 to 25·55) compared to combination ART, and higher in the presence of potential-low/low (aOR 4.62, 95% CI 1.24 to 17.2) or intermediate/high-level (aOR 7·01, 95% CI 2·52 to 19·48) nucleoside reverse transcriptase inhibitors (NRTI) resistance. Viral load on DTG showed a trend towards increased DTG resistance (aOR 1·42, 95% CI 0·92 to 2·19 per standard deviation of log 10 area under the viral load curve). Interpretation Among people experiencing virological failure on DTG-based ART, INSTI DRMs were uncommon, and DTG resistance was rare. DTG monotherapy and NRTI resistance substantially increased the risk for DTG resistance, which is of concern, notably in resource-limited settings. Funding US National Institutes of Health, Swiss National Science Foundation. Research in context Evidence before this study We searched SCOPUS on 20 March 2023 for all publications from inception using the terms “dolutegravir” or “DTG”, “resistant” or “resistance”, and “HIV”. The available evidence on resistance evolution in people living with HIV (PLHIV) with virological failure on DTG-based ART is limited. Most studies assessed the efficacy of DTG-based regimens in clinical studies and reported drug resistance in individuals experiencing virological failure as a secondary objective or reported single or multiple cases of patients developing resistance on DTG-based ART. Clinical trials such as the NADIA trial showed a high degree of viral suppression even in people with NRTI resistance. Consequently, previous analyses included only a small number of people experiencing failure on DTG; the SINGLE trial with 39 people with virologic failure on DTG was the largest. The highest number of individuals with DTG resistance was nine study participants in the NADIA trial. There is evidence that DTG resistance in PLHIV on a DTG monotherapy may be more likely. Other studies suggest that HIV subtype and mutations acquired during a first-generation INSTI-based regimen might affect the risk of DTG resistance. Added value of this study To our knowledge, this is the first study systematically investigating resistance in PLHIV experiencing virologic failure on DTG-based ART using a multi-cohort collaboration design reflecting real-world routine care. We collected genotypic resistance tests and clinical data from eight observational HIV cohorts. This resulted in a large dataset of PLHIV experiencing virologic failure on a DTG regimen (over 700 individuals). It allowed a robust assessment of drug resistance mutations and risk factors for DTG resistance. Cross-resistance of first-generation INSTIs does not appear to explain the mutation patterns in HIV-infected individuals who experience virological failure on DTG-based ART regimens. PLHIV who received DTG monotherapy or DTG lamivudine dual therapy and those infected with non-B subtypes were more likely to develop resistance. Resistance to NRTIs was a major risk factor for DTG resistance, indicating that PLHIV receiving functional monotherapy are more likely to develop DTG resistance. Implications of all the available evidence HIV drug resistance is a significant threat to the sustainability of current and future antiretroviral therapy for combating the ongoing HIV pandemic. Our collaborative analysis shows that cases of DTG resistance are so far rare but not negligible. Given the global DTG roll-out, this might lead to increased frequencies and transmission of DTG resistance, particularly in PLHIV with resistance to NRTIs. While the evidence regarding subtype differences is tentative, it indicates that non-B subtypes, which are most relevant for the global roll-out of DTG, might be associated with an increased risk of resistance.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,031
score de la tête « metaresearch » (Gemma)0,044
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,164

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0310,044
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,006
Bibliométrie0,0050,005
Études des sciences et des technologies0,0010,001
Communication savante0,0030,001
Science ouverte0,0020,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,323
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission2
Résumé présentoui

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