Neutrophil elastase and acute lung injury: Prospects for sivelestat and other neutrophil elastase inhibitors as therapeutics
Bibliographic record
Abstract
OBJECTIVES: To review the evidence and rationale that suggest that neutrophil elastase (NE) may contribute to the development of acute lung injury (ALI) and the acute respiratory distress syndrome. To review selected preliminary data regarding the effectiveness of NE inhibition in animals, in in vitro models, and in patients with ALI. DATA SOURCES: The published literature and observations provided by Ono Pharmaceutical and Eli Lilly investigators and their colleagues. DATA SUMMARY: Taken en toto, the data suggest that NE could contribute to ALI and endothelial cell injury that is relevant to ALI. Moreover, the toxic effects of NE are greatly enhanced by increased oxidative stress, which commonly occurs in patients with ALI. In addition to neutrophils, xanthine oxidase, a constituent of endothelial cells, is a potential source of oxidative stress in ALI; xanthine oxidase-derived oxidants enhance NE toxicity in in vivo, isolated lung, and in vitro endothelial cell test systems. Not surprisingly, endogenous nonoxidatively sensitive NE inhibitors (e.g., eglin C) are more effective in combating the detrimental effects of NE than oxidatively sensitive NE inhibitors (e.g., alpha-1-proteinase inhibitor). In addition, a synthetic NE inhibitor, sivelestat (ONO-5046 and LY544349), is effective in reducing measures of inflammation and injury in multiple animal models of ALI. In a trial of ALI patients with systemic inflammatory response syndrome, conducted in Japan by Ono Pharmaceutical scientists, sivelestat treatment improved the investigator assessment of global improvement and the percentages of patients who were removed from ventilators and transferred out of the intensive care unit. CONCLUSIONS: Further study of the role of NE inhibition as a treatment for ALI is warranted. Additional clinical and preclinical studies with sivelestat and various other NE inhibitors should not only clarify the clinical potential of this intervention strategy, but also better define the activities of NE in inflammatory disorders such as ALI and multiple organ failure.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".