Dabrafenib, an inhibitor of RIP3 kinase-dependent necroptosis, reduces ischemic brain injury
Bibliographic record
Abstract
Ischemic brain injury triggers neuronal cell death by apoptosis via caspase activation and by necroptosis through activation of the receptor-interacting protein kinases (RIPK) associated with the tumor necrosis factor-alpha (TNF-α)/death receptor. Recent evidence shows RIPK inhibitors are neuroprotective and alleviate ischemic brain injury in a number of animal models, however, most have not yet undergone clinical trials and safety in humans remains in question. Dabrafenib, originally identified as a B-raf inhibitor that is currently used to treat melanoma, was later revealed to be a potent RIPK3 inhibitor at micromolar concentrations. Here, we investigated whether Dabrafenib would show a similar neuroprotective effect in mice subjected to ischemic brain injury by photothrombosis. Dabrafenib administered intraperitoneally at 10 mg/kg one hour after photothrombosis-induced focal ischemic injury significantly reduced infarct lesion size in C57BL6 mice the following day, accompanied by a markedly attenuated upregulation of TNF-α. However, subsequent lower doses (5 mg/kg/day) failed to sustain this neuroprotective effect after 4 days. Dabrafenib blocked lipopolysaccharides-induced activation of TNF-α in bone marrow-derived macrophages, suggesting that Dabrafenib may attenuate TNF-α-induced necroptotic pathway after ischemic brain injury. Since Dabrafenib is already in clinical use for the treatment of melanoma, it might be repurposed for stroke 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".