P6483Apabetalone (RVX-208) impacts key biomarkers and pathways associated with cardiovascular disease in patients with severe renal impairment
Bibliographic record
Abstract
Introduction: Apabetalone is a first-in-class orally active bromodomain and extraterminal (BET) inhibitor associated with a reduction in major adverse cardiac events (MACE) in patients with cardiovascular disease (CVD) from phase 2 clinical trials. Apabetalone has previously been shown to downregulate markers of atherosclerosis and vascular inflammation, which may explain its effects on MACE. Chronic kidney disease (CKD) is associated with a progressive loss of renal function and a high risk of CVD. Purpose: To determine the effect of apabetalone on levels of circulating proteins and pathways that contribute to cardiovascular complications in CKD, in a phase 1, open-label, parallel group study of patients with impaired kidney function. Methods: Eight subjects with stage 4 CKD not on dialysis (mean eGFR=20 ml/min/1.73m2) and eight age-, gender-, and BMI-matched subjects (mean eGFR=78.5 ml/min/1.73m2) received a single 100 mg oral dose of apabetalone. Plasma samples were collected at multiple time points over a period of 48 hours for pharmacokinetic (PK) analysis and at 12 hours post dose for proteomic analysis using the SOMAscan® 1.3K platform. Proteomics data were analysed with Ingenuity® Pathway Analysis (IPA) software to identify pathways dysregulated in CKD patients compared to matched controls, and the effect of apabetalone treatment on those pathways.
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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) | 0.002 | 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".