Design and Implementation of a Randomized Crossover Study of Valproic Acid and Antiretroviral Therapy to Reduce the HIV Reservoir
Bibliographic record
Abstract
BACKGROUND: HIV reservoirs represent the major obstacles for eradication and are defined as a cell type that allows persistence of replication-competent HIV in patients on optimal long-term antiretroviral therapy (HAART). Several pilot clinical trials have been implemented to assess the value of experimental therapy to reduce reservoir size or eradicate HIV. In order to eradicate HIV, valproic acid was used as a new strategy to increase viral gene expression in the nucleus of infected cells with the expectation of generating a direct cell death or destruction by nearby cytotoxic cells. Previous pilot studies using VPA have showed conflicting results on the ability of VPA to reduce the size of HIV reservoirs. PURPOSE: As the role of VPA on HIV reservoirs remains unclear, we conducted a multicenter clinical trial with a specific study design to obtain optimal information on reservoir changes while exposing the smallest number of individuals to the experimental medication. METHOD: To this aim, a randomized, crossover design with 2 different treatment durations was implemented. By doubling the therapeutic period in one study arm, we were in a position to assess the impact of an extended duration of VPA on the size of the HIV reservoir and to evaluate the duration of treatment effects upon VPA withdrawal in the other arm. However, limitations for this type of study design included the logistical complexity of 2 uneven study arms and longer study duration. CONCLUSION: Despite the absence of demonstrable impact of VPA on reservoir size, such crossover study design should be considered in the early stage testing of novel HIV therapeutics targeted to reduce reservoir size or eradicate HIV.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".