Abstract 17499: ETC-1002 Incrementally Lowers Low Density Lipoprotein-cholesterol in Patients With Hypercholesterolemia Receiving Stable Statin Therapy
Bibliographic record
Abstract
Background: ETC-1002 is an oral investigational drug that modulates hepatic adenosine triphosphate-citrate lyase to reduce cholesterol biosynthesis and may be beneficial in treating patients with elevated LDL-C despite using stable statin therapy. Methods: A double-blind, parallel group, placebo-controlled multicenter trial evaluated patients (n=134) with baseline LDL-C of 115-220 mg/dL while taking atorvastatin ≤20 mg, simvastatin ≤20 mg, rosuvastatin ≤10mg or pravastatin ≤40 mg randomized to ETC-1002 120 mg, ETC-1002 180 mg, or placebo once daily for 12 weeks. Results: ETC-1002 120 mg (p=0.0055) and 180 mg (p<0.0001) lowered LDL-C significantly more than placebo (primary endpoint) as an add-on to statin therapy. ETC-1002 also lowered (p<0.05) apolipoprotein B, non-high density lipoprotein-cholesterol, total cholesterol and LDL particle number more than placebo. C-reactive protein was also reduced with ETC-1002 120 mg (22%, p=0.26) and 180 mg (30%, p=0.08) versus 0% with placebo. Adverse events (AEs), muscle-related AEs, discontinuations due to AEs and levels of clinical safety labs were generally similar compared with placebo. Conclusion: In patients with elevated LDL-C despite stable statin therapy, ETC-1002 produces incremental LDL-C lowering and is well-tolerated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".