Montelukast Drug May Improve COVID-19 Prognosis: A Review of Evidence
Bibliographic record
Abstract
With the lack of effective therapy, chemoprevention and vaccination, focusing on the immediate repurposing of existing drugs gives hope of curbing the pandemic. Interestingly, montelukast, a drug usually used in asthma, may be proposed as a potential adjuvant therapy in COVID-19. The aim of the present article was to review the properties of montelukast that could be beneficial in COVID-19. Ten experimentally supported properties were retrieved, either related to SARS-CoV-2 (antiviral properties, prevention of endotheliitis and of neurological disorders linked to SARS-CoV-2), and/or related to the host (improvement of atherogenic vascular inflammation, limitation of the ischemia/reperfusion phenomenon, improvement of respiratory symptoms), and/or related to serious COVID-19 outcomes (limitation of the cytokine storm, mitigation of acute respiratory distress syndrome), and/or related to tissue sequelae (antioxidant properties, anti-fibrosis effects). Based on gathered theoretical evidence, we argue that montelukast should be further tested to prevent and treat COVID-19 outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".