The Off-Treatment Effects of Olpasiran on Lipoprotein(a) Lowering
Bibliographic record
Abstract
BACKGROUND: Olpasiran, a small interfering RNA (siRNA), blocks lipoprotein(a) (Lp(a)) production by preventing translation of apolipoprotein(a) mRNA. In phase 2, higher doses of olpasiran every 12 weeks (Q12W) reduced circulating Lp(a) by >95%. OBJECTIVES: This study sought to assess the timing of return of Lp(a) to baseline after discontinuation of olpasiran, as well as longer-term safety. METHODS: OCEAN(a)-DOSE (Olpasiran Trials of Cardiovascular Events And LipoproteiN[a] Reduction-DOSE Finding Study) was a phase 2, dose-finding trial that enrolled 281 participants with atherosclerotic cardiovascular disease and Lp(a) >150 nmol/L to 1 of 4 active doses of olpasiran vs placebo (10 mg, 75 mg, 225 mg Q12W, or an exploratory dose of 225 mg Q24W given subcutaneously). The last dose of olpasiran was administered at week 36; after week 48, there was an extended off-treatment follow-up period for a minimum of 24 weeks. RESULTS: A total of 276 (98.2%) participants entered the off-treatment follow-up period. The median study exposure (treatment combined with off-treatment phases) was 86 weeks (Q1-Q3: 79-99 weeks). For the 75 mg Q12W dose, the off-treatment placebo-adjusted mean percent change from baseline in Lp(a) was -76.2%, -53.0%, -44.0%, and -27.9% at 60, 72, 84, and 96 weeks, respectively (all P < 0.001). The respective off-treatment changes in Lp(a) for the 225 mg Q12W dose were -84.4%, -61.6%, -52.2%, and -36.4% (all P < 0.001). During the extension follow-up phase, no new safety concerns were identified. CONCLUSIONS: Olpasiran is a potent siRNA with prolonged effects on Lp(a) lowering. Participants receiving doses ≥75 mg Q12W sustained a ∼40% to 50% reduction in Lp(a) levels close to 1 year after the last dose. (Olpasiran Trials of Cardiovascular Events And LipoproteiN[a] Reduction-DOSE Finding Study [OCEAN(a)-DOSE]; NCT04270760).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".