Apabetalone Mediated Epigenetic Modulation is Associated with Favorable Kidney Function and Alkaline Phosphatase Profile in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: The association between serum alkaline phosphatase (ALP) with adverse cardiovascular outcomes, in Chronic Kidney Disease (CKD) patients has previously been reported and may be a result of increased vascular calcification and inflammation. Here we report, for the first time, the effects of pharmacologic epigenetic modulation on levels of ALP and kidney function via a novel oral small molecule BET inhibitor, apabetalone, in CKD patients. METHODS: A post-hoc analysis evaluated patients with estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2, who participated in the apabetalone phase 2 randomized controlled trials (SUSTAIN and ASSURE). 48 CKD subjects with a history of cardiovascular disease (CVD) were treated with 100mg twice-daily of 24 and 26 weeks of apabetalone or placebo. ALP and eGFR were measured prior to randomization and at final visits. RESULTS: Patients who received apabetalone (n=35) versus placebo (n=13) over 6 months showed significantly (p=0.02) lowered serum ALP -14.0% (p<0.0001 versus baseline) versus -6.3% (p=0.9 versus baseline). The eGFR in the apabetalone group increased by 3.4% (1.7 mL/min/1.73 m2) (p=0.04 versus baseline) and decreased by 5.8% (2.9 mL/min/1.73 m2) (p=0.6 versus baseline) in the placebo group. Apabetalone was well tolerated. CONCLUSION: A post-hoc analysis of CKD subjects from the SUSTAIN and ASSURE randomized controlled trials demonstrated favorable effects of apabetalone on ALP and eGFR, and generated the hypothesis that epigenetic modulation by BET inhibition may potentially offer a novel therapeutic strategy to treat CVD and progressive kidney function loss in CKD patients. This is being examined in the phase III trial BETonMACE.
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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 | 0.000 |
| 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".