Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".