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Enregistrement W2800268500 · doi:10.1097/qad.0000000000001826

Seroconversion on preexposure prophylaxis

2018· letter· en· W2800268500 sur OpenAlexaboutno aff
Giuliano Rizzardini, Dean L. Winslow

Notice bibliographique

RevueAIDS · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV/AIDS Research and Interventions
Établissements canadiensnon disponible
Organismes subventionnairesCenters for Disease Control and Prevention
Mots-clésMedicineSerodiscordantEmtricitabinePre-exposure prophylaxisMen who have sex with menCondomPopulationFamily medicineHuman immunodeficiency virus (HIV)Treatment as preventionEnvironmental healthAntiretroviral therapyViral loadSyphilis

Résumé

récupéré en direct d'OpenAlex

The success achieved in the last decade in HIV/AIDS treatment with combination antiretroviral therapy (cART) has not been paralleled by remarkable improvements in the effectiveness of HIV prevention strategies: still 1.8 million (1.6–2.1 million) new HIV-1 infections (all ages) were reported in 2016 worldwide [1]. For this reason, it is important to propose and implement a combined systematic prevention strategy able to reach the entire population at risk, in particular people considered at high risk for acquiring HIV infection. Recently, the landscape related to prevention strategies has been significantly modified, and a particular interest has emerged for the use of the oral tenofovir disoproxil/emtricitabine (TDF/FTC), for preexposure prophylaxis (PrEP) among high-risk persons without HIV, as an innovative strategy to decrease the HIV epidemic [2–6]. In the United States, TDF/FTC-based PrEP regimens were approved by the US Food and Drug Administration in 2012. In 2014, the Centers for Disease Control and Prevention, by means of federal guidelines, and on the basis of the drugs’ clinical effectiveness and safety, recommended the use of PrEP, in addition to condoms and needle and syringe exchange programs, for HIV-negative individuals with the following characteristics: serodiscordant sexual relationship; anyone who is not in a monogamous relationship with an HIV-negative person; MSM; sexual risk in general, including individuals who have had sex without using a condom; and IDUs [7]. By 2017, several other countries approved the use of PrEP for HIV/AIDS prevention, including France, Norway, Australia, Israel, Canada, Kenya, South Africa, and Taiwan. Although there have been a substantial numbers of studies suggesting the high potential efficacy of PrEP [8], its large-scale implementation has been limited by several issues, including cost, adherence and concern about selection of resistance. In particular, there is particular attention given to the potential emergence and spread of HIV drug resistance arising from PrEP rollout, particularly in resource-constrained settings, in which antiretroviral treatment options are limited. PrEP use also poses some challenges as TDF and FTC are part of the recommended first and second-line cART regimens to treat HIV-infected individuals in both the developed and developing world. Well documented cases of acquisition of infection due to antiretroviral-resistant HIV in individuals who acquired HIV while receiving PrEP have rarely been reported. In some reports, it is uncertain whether the cases of selected resistance developed shortly after infection with wild-type virus, cases of transmitted drug resistance, or cases of development of drug resistance in individuals with undetected infection at enrollment [9]. In this issue of AIDS, Thaden et al.[10] describe an interesting case of multidrug-resistant (MDR) HIV acquisition in a patient receiving PrEP, studied with an innovative assay utilizing segmental analysis of a hair sample to determine past adherence to PrEP over various time points. It is noteworthy that, in this patient, together with the nucleoside/nucleotide reverse transcriptase substitutions K65R and M184V (consistent with PrEP based on TDF/FTC), another reverse transcriptase mutation, K103N, was present. The latter is linked to resistance to first-generation nonnucleoside reverse transcriptase inhibitors such as efavirenz and nevirapine, whose utilization was not reported by the patient. This does not exclude the possibility that the HIV strain was acquired from someone treated with TDF, FTC and efavirenz, and was resistant to all three drugs. In this patient, PrEP failure appears to be driven by acquisition of an already resistant virus, and not by the lack of PrEP exposure or efficacy vs. wild-type virus at the time of risk behaviors. In this regard, the plasma and segmental hair analysis data suggest that the patient was highly adherent to PrEP over the months preceding seroconversion. According to Thaden et al. [10], this observation makes infection due to an MDR virus the most plausible scenario in this particular situation. Whether PrEP failures are driven primarily by infection with preexisting resistant viruses (data from both the developed and developing world show rates of resistance to nucleoside reverse transcriptase inhibitor and/or non-nucleoside reverse transcriptase inhibitor of around 10% of the total new diagnoses of HIV infection) or by limited efficacy of PrEP driven by inadequate adherence to treatment, remains to be elucidated. It seems likely that depending on the populations studied that the predominant reason for PrEP failure may vary. The new assay used by Thaden et al.[10], perhaps together with ultradeep sequence analysis of antiretroviral resistance through next-generation sequencing methodology, may help to clarify the mechanism of PrEP failure in future cases. Acknowledgements Conflicts of interest There are no conflicts of interest.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,432
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,006

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 Opus0,024
Tête enseignante GPT0,308
Écart entre enseignants0,284 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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

En bref

Citations2
Publié2018
Routes d'admission1
Résumé présentoui

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