Apabetalone Downregulates Fibrotic, Inflammatory and Calcific Processes in Renal Mesangial Cells and Patients with Renal Impairment
Bibliographic record
Abstract
Epigenetic mechanisms are implicated in transcriptional programs driving chronic kidney disease (CKD). Apabetalone is an orally available inhibitor of bromodomain and extraterminal (BET) proteins, which are epigenetic readers that modulate gene expression. In the phase 3 BETonMACE trial, apabetalone reduced risk of major adverse cardiac events (MACE) by 50% in the CKD subpopulation, indicating favorable effects along the kidney–heart axis. Activation of human renal mesangial cells (HRMCs) to a contractile phenotype that overproduces extracellular matrix (ECM) and inflammatory cytokines, and promotes calcification, frequently accompanies CKD to drive pathology. Here, we show apabetalone downregulated HRMC activation with TGF-β1 stimulation by suppressing TGF-β1-induced α-smooth muscle actin (α-SMA) expression, α-SMA assembly into stress fibers, enhanced contraction, collagen overproduction, and expression of key drivers of fibrosis, inflammation, or calcification including thrombospondin, fibronectin, periostin, SPARC, interleukin 6, and alkaline phosphatase. Lipopolysaccharide-stimulated expression of inflammatory genes IL6, IL1B, and PTGS2 was also suppressed. Transcriptomics confirmed apabetalone affected gene sets of ECM remodeling and integrins. Clinical translation of in vitro results was indicated in CKD patients where a single dose of apabetalone reduced plasma levels of key pro-fibrotic and inflammatory markers, and indicated inhibition of TGF-β1 signaling. While plasma proteins cannot be traced to the kidney alone, anti-fibrotic and anti-inflammatory effects of apabetalone identified in this study are consistent with the observed decrease in cardiovascular risk in CKD patients.
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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".