Evaluating the efficacy of therapeutic HIV vaccines through analytical treatment interruptions
Bibliographic record
Abstract
INTRODUCTION: The development of an effective therapeutic HIV vaccine that induces immunologic control of viral replication, thereby eliminating or reducing the need for antiretroviral therapy (ART), would be of great value. Besides the obvious challenges of developing a therapeutic vaccine that would generate effective, sustained anti-HIV immunity in infected individuals is the issue of how to best assess the efficacy of vaccine candidates. DISCUSSION: This review discusses the various outcome measures assessed in therapeutic HIV vaccine clinical trials involving individuals receiving suppressive ART, with a particular focus on the role of analytical treatment interruption (ATI) as a way to assess the virologic control induced by an immunotherapy. This strategy is critical given that there are otherwise no readily available measures to determine the ability of a vaccine-induced immune response to effectively control HIV replication. The various outcome measures that have been used to assess vaccine efficacy in published therapeutic HIV vaccine clinical trials will also be discussed. Outcome measures have included the kinetics of viral rebound, the new viral set point and changes in the size of the viral reservoir. Clinically relevant outcomes such as the CD4 decline, the time to resume therapy or the time to meet the criterion to resume therapy, the proportion of participants who resume therapy and/or the development of clinical symptoms such as acute retroviral syndrome are also measures of vaccine efficacy. CONCLUSIONS: Given the lack of consistency between therapeutic HIV vaccine trials in how efficacy is assessed, comparing vaccines has been difficult. It would, therefore, be beneficial to determine the most clinically relevant measure for use in future studies. Other recommendations for future clinical trials also include studying compartments in addition to blood and replacing ATIs with single-copy assays in situations in which the use of an ATI is not ideal.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".