BET protein inhibitor apabetalone (RVX-208) suppresses pro-inflammatory hyper-activation of monocytes from patients with cardiovascular disease and type 2 diabetes
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Bibliographic record
Abstract
BACKGROUND: Patients with cardiovascular disease (CVD) and type 2 diabetes (DM2) have a high residual risk for experiencing a major adverse cardiac event. Dysregulation of epigenetic mechanisms of gene transcription in innate immune cells contributes to CVD development but is currently not targeted by therapies. Apabetalone (RVX-208) is a small molecule inhibitor of bromodomain and extra-terminal (BET) proteins-histone acetylation readers that drive pro-inflammatory and pro-atherosclerotic gene transcription. Here, we assess the impact of apabetalone on ex vivo inflammatory responses of monocytes from DM2 + CVD patients. RESULTS: Monocytes isolated from DM2 + CVD patients and matched controls were treated ex vivo with apabetalone, interferon γ (IFNγ), IFNγ + apabetalone or vehicle and phenotyped for gene expression and protein secretion. Unstimulated DM2 + CVD monocytes had higher baseline IL-1α, IL-1β and IL-8 cytokine gene expression and Toll-like receptor (TLR) 2 surface abundance than control monocytes, indicating pro-inflammatory activation. Further, DM2 + CVD monocytes were hyper-responsive to stimulation with IFNγ, upregulating genes within cytokine and NF-κB pathways > 30% more than control monocytes (p < 0.05). Ex vivo apabetalone treatment countered cytokine secretion by DM2 + CVD monocytes at baseline (GROα and IL-8) and during IFNγ stimulation (IL-1β and TNFα). Apabetalone abolished pro-inflammatory hyper-activation by reducing TLR and cytokine gene signatures more robustly in DM2 + CVD versus control monocytes. CONCLUSIONS: Monocytes isolated from DM2 + CVD patients receiving standard of care therapies are in a hyper-inflammatory state and hyperactive upon IFNγ stimulation. Apabetalone treatment diminishes this pro-inflammatory phenotype, providing mechanistic insight into how BET protein inhibition may reduce CVD risk in DM2 patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it