Abstract 17091: ARO-APOC3, an Investigational RNAi Therapeutic, Silences APOC3 and Reduces Atherosclerosis-Associated Lipoproteins in Patients With Mixed Dyslipidemia: MUIR Study Results
Bibliographic record
Abstract
Background: Despite the availability of potent LDL-C-lowering therapies, patients with mixed dyslipidemia (MD) remain at high risk of cardiovascular (CV) disease due to residual risk from triglyceride (TG)-rich atherogenic lipoproteins (TRLs), ie, remnant cholesterol and/or VLDL-C. Inhibition of APOC3 has emerged as a promising therapeutic strategy for reducing this residual CV risk. Aim: We report interim results through Week 24 from MUIR, an ongoing, randomized, placebo-controlled, Phase 2b study (NCT04998201) evaluating the effects of ARO-APOC3 in patients with MD (fasting TGs 150 to 499 mg/dL and either LDL-C ≥70 mg/dL or non-HDL-C ≥100 mg/dL). Methods: Eligible subjects (n=353) were randomized 3:1 to receive either subcutaneous injections of 10, 25, or 50 mg ARO-APOC3 or matched placebo on Day 1 and at Week 12 or 50 mg ARO-APOC3 on Day 1 and Week 24. Subjects were on a stable diet and optimal lipid-lowering therapies. The primary endpoint was the percent change from baseline in fasting TGs at Week 24. Statistically significant differences were determined using mixed model repeat measures analysis. Results: At Week 24, when dosed on Day 1 and Week 12, ARO-APOC3 significantly decreased APOC3 in a dose-dependent manner up to 80% (p<0.0001). Least squares (LS) mean TGs were significantly reduced by 52 to 64% (p<0.0001). Least squares (LS) mean atherogenic lipoproteins were reduced by up to 27% for non-HDL-C, 19% for apolipoprotein B, and 55% for remnant cholesterol. LS mean HDL-C was increased by up to 51%. The most frequent adverse events were COVID-19 infection, worsening of glycemic control, and upper respiratory infection. Conclusions: By silencing APOC3 expression, ARO-APOC3 significantly reduced circulating TGs and atherogenic TRLs in patients with MD. The effect of ARO-APOC3 on CV relative risk reduction will be evaluated in an upcoming outcomes 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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".