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Record W2626976864 · doi:10.1093/infdis/jix137

Global HIV Antiretroviral Drug Resistance

2017· review· en· W2626976864 on 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

Bibliographic record

VenueThe Journal of Infectious Diseases · 2017
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesCenters for Disease Control and PreventionNational Institutes of Health
KeywordsAntiretroviral drugDrug resistanceHuman immunodeficiency virus (HIV)HIV drug resistanceVirologyDrugMedicineAntiretroviral therapyIntensive care medicineViral loadPharmacologyBiologyMicrobiology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.401
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations28
Published2017
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