#2999 APABETALONE REDUCES CARDIAC EVENTS IN CKD PATIENTS BY DOWNREGULATING FIBROTIC AND INFLAMMATORY PROCESSES
Bibliographic record
Abstract
Abstract Background and Aims Major adverse cardiac events (MACE) are a leading cause of mortality in patients with chronic kidney disease (CKD). Apabetalone is an orally available inhibitor of bromodomain & extraterminal (BET) proteins – epigenetic readers that modulate gene expression involved in fibrosis & inflammation. In the phase 3 BETonMACE trial in patients with CVD & diabetes mellitus (NCT02586155), apabetalone reduced MACE by 50% in the subpopulation with CKD (eGFR < 60 mL/min/1.73 m2; HR 0.50 95% CI 0.26,0.96 p = 0.04) indicating favorable response along the kidney-heart axis. This study examines effects of apabetalone in human renal mesangial cells (HRMCs) on fibrotic & inflammatory pathways. In the clinic, we assess plasma levels of fibrotic factors in CKD patients & matched controls after receiving apabetalone. Method TGF-β1 is a pro-fibrotic cytokine that activates HRMCs to over-produce extracellular matrix (ECM). HRMCs were stimulated with TGF-β1 or LPS ± 1-25 µM apabetalone, 0.15-0.5 µM JQ1 or 0.1 µM MZ1 - BET inhibitors (BETi) with chemical scaffolds different than apabetalone. Gene expression was measured by RNA-seq & real-time PCR. RNA-seq was evaluated by Gene Ontology (GO) & Ingenuity Pathway Analysis (IPA). Proteins were measured by immunofluorescence microscopy or ELISA. Collagen gel contraction was assessed in 3D culture. In human subjects, proteomic analysis of plasma was performed via SOMAscan 1.3k 12 hours after 100 mg of apabetalone in CKD patients (n = 8 eGFR < 30 stage 4 or 5) and matched controls (n = 8 eGFR > 60). Results GO analysis of RNA-seq from TGF-β1 stimulated HRMCs showed multiple gene sets associated with ECM production & remodeling in the top 20 affected by BETi. IPA predicted NfkB-RelA and NFkB (complex) were inhibited by apabetalone, consistent with suppressed inflammation. IPA also predicted apabetalone activated pathways of glucose utilization & tolerance of ROS production, including Oxidative Phosphorylation (z-score 5.7, p = 2.95 × 10−3 at 25 µM) & NRF2-Mediated Oxidative Stress Response (z score 2.3, p = 7.9 × 10−12 at 25 µM; z-score 1.6, p = 1.5 × 10−5 at 5 µM). Predicted changes may allow tolerance of elevated glucose. Mechanistically, apabetalone suppressed TGF-β1 induced α-smooth muscle actin (α-SMA) gene expression, a marker of activation, up to 89% (p = 2.8 × 10−6 at 25 µM), and abolished α-SMA filament formation. Consequently, apabetalone opposed TGF-β1 induced collagen gel contraction up to 24% (p = 0.031 at 25 µM). Further, apabetalone countered TGF-β1 induced expression & protein production of key drivers of fibrosis including (a) thrombospondin, an activator of latent TGF-β1 (b) fibronectin, a key ECM component (c) periostin, a promoter of ECM production (d) osteonectin, a regulator of TGF-β1 expression and (e) IL6, a pro-inflammatory cytokine (Table 1). In addition, apabetalone dose dependently opposed LPS stimulated expression of inflammatory genes: IL6 up to 94%, IL1B up to 95% and PTGS2 up to 94% (p<0.001 for each). In all studies, JQ1 or MZ1 had similar activity as apabetalone, confirming on-target BETi effects. In humans, plasma levels of profibrotic and inflammatory markers were reduced 12 hours after receiving apabetalone specifically in those with CKD. Only periostin was reduced in subjects without renal impairment (Table 1). Conclusion Through epigenetic regulation of transcription in HRMCs, apabetalone reduces production of fibrotic & inflammatory factors known to signal along the kidney-heart axis & exacerbate kidney dysfunction. Predicted changes in energy metabolism suggest apabetalone facilitates adaptation to high glucose in kidneys. In a clinical trial, plasma levels of fibrotic & inflammatory factors were reduced specifically in CKD patients receiving apabetalone. Our results provide mechanistic insights into reduced MACE in CKD patients receiving apabetalone in the phase 3 BETonMACE trial & will be further evaluated in an upcoming 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) | 0.001 | 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) | 0.013 | 0.002 |
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".