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Enregistrement W2626976864 · doi:10.1093/infdis/jix137

Global HIV Antiretroviral Drug Resistance

2017· review· en· W2626976864 sur OpenAlexaff
Catherine Godfrey, Michael C. Thigpen, Keith W. Crawford, Patrick Jean–Phillippe, Deenan Pillay, Deborah Persaud, Daniel R. Kuritzkes, Mark A. Wainberg, Elliot Raizes, Joseph Fitzgibbon

Notice bibliographique

RevueThe Journal of Infectious Diseases · 2017
Typereview
Langueen
DomaineMedicine
ThématiqueHIV/AIDS Research and Interventions
Établissements canadiensMcGill University
Organismes subventionnairesNational Institute of Allergy and Infectious DiseasesCenters for Disease Control and PreventionNational Institutes of Health
Mots-clésAntiretroviral drugDrug resistanceHuman immunodeficiency virus (HIV)HIV drug resistanceVirologyDrugMedicineAntiretroviral therapyIntensive care medicineViral loadPharmacologyBiologyMicrobiology

Résumé

récupéré en direct d'OpenAlex

Antiretroviral therapy (ART) is recommended for all people infected with human immunodeficiency virus (HIV). The importance of wide population coverage and effective suppression of viremia is reflected by the 90-90-90 goals established by the Joint United Nations Programme on HIV/AIDS (UNAIDS). HIV prevention strategies increasingly have antiretrovirals, including preexposure prophylaxis as a core component. Acquired and transmitted HIV drug resistance may compromise effective control of HIV. High resistance rates in children and in communities with mature treatment programs, poorly documented rates in populations most at risk for both HIV infection and treatment failure, and delays in switching regimens after documented virological failure suggest that current strategies to minimize HIV drug resistance are suboptimal. Factors that might mitigate these trends include the adoption of regimens with lower barriers to resistance and technologies that could be deployed to identify treatment failures early. In May 2016, the Division of AIDS at the US National Institutes of Health (NIH) convened a consultation to identify research gaps and characterize ways in which the research community might support efforts to address global HIV drug resistance (Table 1). This article outlines the conclusions of the group and is the introduction to a supplement to this journal outlining some of the major challenges and research gaps associated with HIV drug resistance globally. Research Gaps and Opportunities Research Gaps and Opportunities HIV drug resistance is associated with suboptimal virological suppression, subsequent immunologic decline, and poor clinical outcomes [1]. Maintaining a failing regimen leads to accumulation of resistance mutations, compromising the efficacy of subsequent regimens [2]. Viral load (VL) monitoring has been embraced by treatment programs and by recommending authorities because early identification of virological failure may reduce acquired HIV drug resistance. Nevertheless, significant challenges with VL scale-up persist: Access to VL testing remains limited in many countries, results are returned slowly to clinicians, and action on these results is often lacking [3]. Insufficient training, together with the requirement in many countries for a confirmatory VL to obtain alternative regimens, compounds delays in treatment switch. For example, in 2015, the Kenyan Ministry of Health documented more than 110 000 VL measurements >1000 HIV RNA copies/mL; only 2.1% represented confirmatory VLs [4]. These data, when evaluated with national program and procurement data on protease inhibitor use, suggest a gap in the numbers of patients needing to switch therapy compared to the frequency of those who actually switch. Treatment programs struggle with diverse, often inadequate data sources to estimate ART use and future needs, leading to incorrect forecasting and interruptions of drug supply. Expansion of HIV treatment programs has been associated with an increased prevalence of pretreatment resistance, and very high rates of resistance-associated mutations have been documented in several countries and in specific populations. The rate of pretreatment resistance has also been shown to rise. For example, in East Africa, while the overall resistance level is 7.4%, 8 years after rollout, the rate of increase of any transmitted drug resistance mutation is estimated at 29% per year [5]. Models suggest that if pretreatment resistance exceeds 10%, the goal of 90% virological suppression will not be met [6]. HIV drug resistance may also compromise incidence goals, as individuals with high VLs are more likely to transmit HIV to their partners [7]. Some populations have extremely high drug resistance rates. Rates of drug resistance in perinatally infected children have been reported at 10%–24%, but increasing to 34%–56% in patients with prior exposure to drugs to prevent maternal-to-child transmission [8]. This, combined with higher VL and limited ART regimens, contributes to rates of virologic failure that are higher in HIV-infected children compared to adults, especially with nonnucleoside reverse transcriptase inhibitor-based regimens [9]. High rates of both acquired and pretreatment drug resistance have been observed in adult populations most at risk for HIV such as men who have sex with men, commercial sex workers, and injection drug users in Latin America and the Caribbean [10], in sub-Saharan Africa [11] and in Asia [12]. Although the exact dynamics of the spread of drug-resistant virus is unknown, phylogenetic approaches demonstrate that drug resistance can spread from ART-experienced and -naive individuals to others. If the resistance mutation transmitted is one that confers resistance to the agents used for preexposure prophylaxis, this may compromise HIV prevention efforts and incidence goals. The World Health Organization (WHO) has suggested several monitoring activities for HIV drug resistance assessment in low- and middle-income countries (LMICs) in an effort to obtain nationally representative data on HIV drug resistance. These include monitoring of clinic-level early warning indicators, surveys of pretreatment HIV drug resistance in populations initiating ART, surveys of acquired HIV drug resistance, and surveys in infants <18 months of age. Early warning indicators include variables such as on-time pill pickup, retention on ART at 12 months, drug supply shortages, VL testing, and VL suppression with the latter 3 also being President’s Emergency Plan for AIDS Relief (PEPFAR) indicators; countries receiving PEPFAR funds are required to report these data [13]. WHO recommends yearly monitoring of early warning indicators followed by representative national surveys every 3 years [14]. To date, no country has fully implemented this recommendation. During the NIH consultation, 4 broad themes with specific knowledge gaps were identified. Surveillance needs are significant. Sampling methods for current national surveys may not capture pockets of resistance in key populations and geographic areas. Innovative approaches that simplify collection of data on community VL and community drug resistance will improve national and international surveillance efforts. Advanced technologies such as multiplex next-generation sequencing methods could yield robust surveillance tools to simplify surveillance activities if they can be implemented for surveillance in LMICs. Beyond surveillance, there is the opportunity to develop platforms that combine these data with clinically useful patient-level data. These new technologies could also generate phylogenetic/phylodynamic data that could be used to target prevention interventions in specific populations [15, 16]. Electronic capture of data and cloud-based harmonization of various data streams may help capture these data in usable forms [17]. Research is needed to further understand the effects of currently used antiretroviral agents in individuals with non-B subtypes, especially the effects of HIV drug resistance mutations when combined with previously acquired mutations. The introduction of dolutegravir and tenofovir alafenamide may have dramatic effects on reducing the development of HIV drug resistance in LMICs; some of these effects may be related to viral subtype. Research has demonstrated that resistance mutations present at low levels within the HIV quasispecies (minor variants) may predict treatment failure [18, 19]. When sensitive assays are used that detect minor variants, rates of transmitted resistance are significantly increased in LMICs [20, 21] Better methods to detect and use this information for clinical care are needed. Some studies have suggested that nucleoside reverse transcriptase inhibitors (NRTIs) retain partial activity even in the presence of resistance-associated mutations [22]. Improved genotype–phenotype correlations are needed to better understand when NRTIs need to be switched. Simplified regimens of <3 drugs may also be possible with newer agents (eg, dolutegravir + lamivudine), but the effect on HIV drug resistance needs to be investigated. Long-acting regimens may prove useful in populations where adherence is problematic, but the HIV drug resistance implications require study. Most LMICs use a VL cutoff of 1000 HIV copies/mL as the definition of virologic failure, but studies are needed to determine if viral load and drug resistance assays on diverse specimens such as dried blood spots using lower cutoffs are feasible and would help prevent HIV drug resistance in LMICs. Studies need to be conducted not only in adults, but also in children. In the medium-term, individualized or stratified regimen modifications are needed for specific populations and communities. Simplified testing that combines VL with HIV drug resistance testing in different specimen types will allow for rapid identification of virological failure and adjustment of therapy, if required. Point-of-care or near-point-of-care diagnostics may improve the clinical management of individuals at risk for HIV drug resistance at initiation of therapy or at failure. Studies are needed to determine how best to deploy these approaches. Programmatic and other nonresearch data sources should be harmonized and improved to allow for the evaluation of interventions in service delivery and models of care on the development of HIV drug resistance. Simplified, electronic data collection methods will allow for rapid evaluation of interventions and the effect of specific HIV drug resistance trends on long-term outcomes. If proxies for specific HIV drug resistance data such as community VL are used, these measurements should be tested and validated. Modeling and cost-effectiveness evaluations will be needed to understand the economic impact of newer agents. Research initiatives are focused on collecting the best evidence from a variety of sources to drive policy decisions for the prevention of HIV drug resistance. Newer antiretroviral agents with a higher barrier to resistance will become available in the near future, but caution should be exercised when assuming that these agents will solve the HIV drug resistance problem. As test-and-start strategies are considered, data to support optimal first-line therapy will be required. In the near future, patient-level HIV drug resistance assessment will be needed in specific populations, especially those requiring third-line regimens. Finally, the contribution of preexposure prophylaxis to the overall resistance burden is unknown, but is concerning given that the failure of preexposure prophylaxis in the setting of HIV drug resistance has been described [23]. Attention to HIV drug resistance, as guided by the WHO, will be important in achieving the milestones that will end the HIV epidemic. Disclaimer. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the National Institutes of Health. Financial support. This work was supported by National Institute of Allergy and Infectious Diseases (grant numbers UM1AI068636, R01 AI098558 to D. R. K.). Supplement sponsorship. This work is part of a supplement sponsored by the National Institute of Allergy and Infectious Disease, NIH, and the Centers for Disease Control and Prevention. Potential conflicts of interest. All authors: No potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0120,001

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 Opus0,040
Tête enseignante GPT0,401
Écart entre enseignants0,361 · 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'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations28
Publié2017
Routes d'admission1
Résumé présentnon

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