Landscape review of current HIV ‘kick and kill’ cure research - some kicking, not enough killing
Bibliographic record
Abstract
BACKGROUND: Current antiretroviral therapy (ART) used to treat human immunodeficiency virus (HIV) patients is life-long because it only suppresses de novo infections. Recent efforts to eliminate HIV have tested the ability of a number of agents to reactivate ('Kick') the well-known latent reservoir. This approach is rooted in the assumption that once these cells are reactivated the host's immune system itself will eliminate ('Kill') the virus. While many agents have been shown to reactivate large quantities of the latent reservoir, the impact on the size of the latent reservoir has been negligible. This suggests that the immune system is not sufficient to eliminate reactivated reservoirs. Thus, there is a need for more emphasis on 'kill' strategies in HIV cure research, and how these might work in combination with current or future kick strategies. METHODS: We conducted a landscape review of HIV 'cure' clinical trials using 'kick and kill' approaches. We identified and reviewed current available clinical trial results in human participants as well as ongoing and planned clinical trials. We dichotomized trials by whether they did not include or include a 'kill' agent. We extracted potential reasons why the 'kill' is missing from current 'kick and kill' strategies. We subsequently summarized and reviewed current 'kill' strategies have entered the phase of clinical trial testing in human participants and highlighted those with the greatest promise. RESULTS: The identified 'kick' trials only showed promise on surrogate measures activating latent T-cells, but did not show any positive effects on clinical 'cure' measures. Of the 'kill' agents currently being tested in clinical trials, early results have shown small but meaningful proportions of participants remaining off ART for several months with broadly neutralizing antibodies, as well as agents for regulating immune cell responses. A similar result was also recently observed in a trial combining a conventional 'kick' with a vaccine immune booster ('kill'). CONCLUSION: While an understanding of the efficacy of each individual component is crucial, no single 'kick' or 'kill' agent is likely to be a fully effective cure. Rather, the solution is likely found in a combination of multiple 'kick and kill' interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".