6E11, a highly selective inhibitor of Receptor-Interacting Protein Kinase 1, protects cells against cold hypoxia-reoxygenation injury
Bibliographic record
Abstract
Necroptosis is a programmed cell death pathway that has been shown to be of central pathophysiological relevance in multiple disorders (hepatitis, brain and cardiac ischemia, pancreatitis, viral infection and inflammatory diseases). Necroptosis is driven by two serine threonine kinases, RIPK1 (Receptor Interacting Protein Kinase 1) and RIPK3, and a pseudo-kinase MLKL (Mixed Lineage Kinase domain-Like) associated in a multi-protein complex called necrosome. In order to find new inhibitors for use in human therapy, a chemical library containing highly diverse chemical structures was screened using a cell-based assay. The compound 6E11, a natural product derivative, was characterized as a positive hit. Interestingly, this flavanone compound: inhibits necroptosis induced by death receptors ligands TNF-α (Tumor Necrosis Factor) or TRAIL (TNF-Related Apoptosis-Inducing Ligand); is an extremely selective inhibitor, among kinases, of human RIPK1 enzymatic activity with a nM Kd; has a non-ATP competitive mode of action and a novel putative binding site; is weakly cytotoxic towards human primary blood leukocytes or retinal pigment epithelial cells at effective concentrations; protects human aortic endothelial cells (HAEC) from cold hypoxia/reoxygenation injury more effectively than necrostatin-1 (Nec-1) and Nec-1s. Altogether, these data demonstrate that 6E11 is a novel potent small molecular inhibitor of RIPK1-driven necroptosis.
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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 | 0.000 |
| 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".