The BET inhibitor apabetalone decreases neuroendothelial proinflammatory activation<i>in vitro</i>and in a mouse model of systemic inflammation
Bibliographic record
Abstract
Abstract Brain vascular inflammation is characterized by endothelial activation and immune cell recruitment to the blood vessel wall, potentially causing a breach in the blood – brain barrier, brain parenchyma inflammation, and a decline of cognitive function. The clinical-stage small molecule, apabetalone, reduces circulating vascular endothelial inflammation markers and improves cognitive scores in elderly patients by targeting epigenetic regulators of gene transcription, bromodomain and extraterminal proteins. However, the effect of apabetalone on cytokine-activated brain vascular endothelial cells (BMVECs) is unknown. Here, we show that apabetalone treatment of BMVECs reduces hallmarks ofin vitroendothelial activation, including monocyte chemoattractant protein-1 (MCP-1) and RANTES chemokine secretion, cell surface expression of endothelial cell adhesion molecule VCAM-1, as well as endothelial capture of THP-1 monocytes in static and shear stress conditions. Apabetalone pretreatment of THP-1 downregulates cell surface expression of chemokine receptors CCR1, CCR2, and CCR5, and of the VCAM-1 cognate receptor, integrin α4. Consequently, apabetalone reduces THP-1 chemoattraction towards soluble CCR ligands MCP-1 and RANTES, and THP-1 adhesion to activated BMVECs. In a mouse model of brain inflammation, apabetalone counters lipopolysaccharide-induced transcription of endothelial and myeloid cell markers, consistent with decreased neuroendothelial inflammation. In conclusion, apabetalone decreases proinflammatory activation of brain endothelial cells and monocytesin vitroand in the mouse brain during systemic inflammation.
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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".