Leronlimab Treatment for Multidrug-Resistant HIV-1 (OPTIMIZE): A Randomized, Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Leronlimab is a humanized κ-IgG4 monoclonal antibody that blocks C-C chemokine receptor type 5. We investigated leronlimab as a treatment option for people living with multidrug-resistant HIV-1. SETTING AND METHODS: In a phase 2b/3, multicenter, randomized, double-blind, placebo-controlled study conducted in 21 hospital centers in the United States, treatment-experienced people living with HIV with documented drug resistance were randomly assigned once weekly leronlimab (350 mg subcutaneously) or matching placebo for 1 week overlapping existing failing antiretroviral therapy, followed by a 24-week single-arm extension with weekly leronlimab combined with a new optimized background treatment. The primary end point was achieving ≥0.5 log 10 reduction in plasma HIV-1 RNA from baseline at the end of the 1-week double-blinded treatment period. RESULTS: Fifty-two participants were enrolled (25 leronlimab and 27 placebo). After the 1-week randomized phase, by the intent-to-treat analysis, 64.0% (16/25) receiving leronlimab achieved ≥0.5 log 10 reduction in plasma HIV-1 RNA versus 23.1% (6/26) receiving placebo ( P = 0.0032), whereas by per protocol analysis, 72.7% (16/22) receiving leronlimab achieved ≥0.5 log 10 reduction in plasma HIV-1 RNA versus 24.0% (6/25) receiving placebo ( P = 0.0008). Leronlimab was generally well tolerated with no drug-related serious adverse events reported. Overall, 175 adverse events were reported by 34/52 participants, with 120 (68.6%) adverse events categorized as mild. CONCLUSIONS: Leronlimab resulted in significantly reduced plasma HIV-1 within 1 week after addition to failing antiretroviral therapy. After 24 weeks combined with an optimized background treatment, most participants had plasma HIV-1 RNA levels <50 copies per milliliter plasma, suggesting utility of leronlimab as a component of salvage therapy.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".