Abstract 257: Apabetalone (RVX-208) Reduces Pathologic Cell-Cell Adhesion and Expression of Key Vascular Inflammation Markers in Monocytes, Endothelial Cells and Mouse Aorta
Bibliographic record
Abstract
Apabetalone (RVX-208) is a bromodomain & extraterminal (BET) protein inhibitor, an epigenetic modifier of gene expression, currently in a phase 3 major adverse cardiac events outcomes trial in post-acute coronary syndrome patients with type 2 diabetes mellitus (DM) (BETonMACE). CVD patients enrolled in phase 2b trials (ASSERT, SUSTAIN and ASSURE) demonstrated a 44% relative risk reduction in cardiovascular disease (CVD) events (Nicholls et al. 2017). In CVD and DM, elevated circulating cytokines potentiate vascular inflammation (VI) through recruitment of leukocytes to the vascular endothelium, which contributes to atherosclerosis and plaque rupture. Previous studies demonstrated that apabetalone has potent anti-inflammatory effects on human aortic endothelial cells (HAEC) and macrophage-like U937 cells. Here we show that TNFα stimulation induced significant adhesion of THP-1 monocytes to inflamed endothelial cells, an outcome reversed by apabetalone treatment under both static (human umbilical vein endothelial cells - HUVEC) and flow (HAEC) conditions. Mechanistically, apabetalone suppressed the TNFα and IL-1β-induced expression of mRNAs of multiple endothelial cell adhesion molecules (CD44, E-selectin, VCAM-1 and MCP-1) and inflammatory cytokines (IL-6, IL-8, IL-1β, and CSF2). Monocytes also respond to TNFα stimulation with an upregulation of inflammatory and adhesion marker expression. In THP-1 cells, apabetalone treatment significantly reduced mRNA expression of CCR1, CCR2, IL-1β, MCP-1, MYD88, TLR4, TNFα, and VLA-4. In the diet-induced obesity (DIO) mouse model that mimics metabolic syndrome, treatment with apabetalone, administered at 150 mg/kg BID for the last 16 weeks of a 22 week study, downregulated aortic adhesion markers (E-selectin and ICAM) and markers of infiltrating immune cells (CCR2 and CD11b). In summary, treatment with apabetalone causes transcriptional changes in monocytes and endothelial cells that translate into a reduction in adhesion under inflammatory conditions. We hypothesize that downregulation of VI by apabetalone may contribute to the reduction in CVD events observed in phase 2 studies. This hypothesis is currently being tested in the ongoing BETonMACE phase 3 trial.
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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) | 0.004 | 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".