Targeting intracellular signaling as an antiviral strategy: aerosolized LASAG for the treatment of influenza in hospitalized patients
Bibliographic record
Abstract
Influenza has been a long-running health problem and novel antiviral drugs are urgently needed. In pre-clinical studies, we demonstrated broad antiviral activity of D, L-lysine-acetylsalicylate glycine (LASAG) against influenza virus (IV) in cell culture and protection against lethal challenge in mice. LASAG is a compound with a new antiviral mode of action. It inhibits the NF-κB signal transduction module that is essential for IV replication. Our goal was to determine whether aerosolized LASAG would also show a therapeutic benefit in hospitalized patients suffering from severe influenza. The primary endpoint was time to alleviation of clinical influenza symptoms. The primary analysis was based on the modified intention-to-treat (MITT) population. This included all patients with confirmed influenza virus infection who received at least one treatment. The per protocol (PP) analysis set included all subjects from the MITT population who underwent at least 13 inhalations. In the MITT group, 48 (41.7%) participants (29 LASAG; 19 placebo) had severe influenza. The mean time to symptom alleviation was 56.2 h in the placebo group and 43.0 h in the LASAG group. The PP set consisted of 41 patients (24 LASAG; 17 placebo). The mean time to symptom alleviation in the LASAG group (38.3 h; P = 0.0365) was lower than that in the placebo group (56.2 h). In conclusion, LASAG improved the time to alleviation of influenza symptoms in hospitalized patients. The present phase II proof-of-concept (PoC) study demonstrates that targeting an intra-cellular signaling pathway using aerosolized LASAG improves the time to symptom alleviation compared to standard treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".