Pan-caspase inhibitor F573 mitigates liver ischemia reperfusion injury in a murine model
Bibliographic record
Abstract
BACKGROUND: Liver ischemia reperfusion injury (IRI) remains a challenge in liver transplantation. A number of compounds have previously demonstrated efficacy in mitigating IRI. Herein, we applied three specific additive strategies to a mouse IRI screening model to determine their relative potencies in reducing such injury, with a view to future testing in a large animal and clinical ex situ normothermic perfusion setting: 1) F573, a pan-caspase inhibitor, 2) anti-inflammatory anakinra and etanrecept and 3) BMX-001, a mimetic of superoxide dismutase. METHODS: A non-lethal liver ischemia model in mice was used. Additives in the treatment groups were given at fixed time points before induction of injury, compared to a vehicle group that received no therapeutic treatment. Mice were recovered for 6 hours following the ischemic insult, at which point blood and tissue samples were obtained. Plasma was processed for transaminase levels. Whole liver tissue samples were processed for histology, markers of apoptosis, oxidative stress, and cytokine levels. RESULTS: In an in vivo murine IRI model, the F573 treatment group demonstrated statistically lower alanine aminotransferase (ALT) levels (p = 0.01), less evidence of apoptosis (p = 0.03), and lower cytokine levels compared to vehicle. The etanercept with anakinra treatment group demonstrated significantly lower cytokine levels. The BMX-001 group demonstrated significantly decreased apoptosis (p = 0.01) evident on TUNEL staining. CONCLUSIONS: The administration of pan-caspase inhibitor F573 in a murine in vivo model likely mitigates liver IRI based on decreased markers of cellular injury, decreased evidence of apoptosis, and improved cytokine profiles. Anakinra with etanercept, and BMX-001 did not demonstrate convincing efficacy at reducing IRI in this model, and likely need further optimization. The positive findings set rational groundwork for future translational studies of applying F573 during normothermic ex situ liver perfusion, with the aim of improving the quality of marginal grafts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".