Effects of 12 weeks of treatment with intravenously administered bococizumab, a humanized monoclonal antibody blocking proprotein convertase subtilisin/kexin type 9, in hypercholesterolemic subjects on high‐dose statin
Bibliographic record
Abstract
AIMS: Two multiple-dose phase II studies were conducted in subjects with primary hypercholesterolemia to evaluate the LDL-C lowering efficacy, safety, and tolerability of bococizumab, a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor. METHODS: The results from the two phase II, double-blinded, randomized, placebo-controlled, multicenter studies conducted in the USA and Canada were combined. In Study 1, 90 subjects with LDL-C ≥100 mg/dL received intravenous (IV) placebo or bococizumab 0.25, 1, 3, or 6 mg/kg. In Study 2, 45 subjects with LDL-C ≥80 mg/dL received IV placebo or bococizumab 1 or 3 mg/kg. Subjects were treated every 4 weeks for 12 weeks. Dosing was interrupted if LDL-C dipped to ≤25 mg/dL and resumed if LDL-C returned to ≥40 mg/dL. The primary endpoint was percent LDL-C reduction from baseline at Week 12. RESULTS: At Week 12, the reductions from baseline in LDL-C vs placebo in the bococizumab 0.25, 1, 3, and 6 mg/kg groups were 9.3%, 10.2%, 41.6%, and 52.0%, respectively (P < .001 vs placebo for all). LDL-C reductions were greater (69.9%) in subjects who received all three doses of bococizumab 6 mg/kg (P < .001 vs placebo). Pharmacogenomic analysis revealed that 15% of hyperlipidemic subjects carried polymorphisms associated with familial hypercholesterolemia, with maximal LDL-C reductions being similar between carriers and noncarriers. Adverse events were mild, unrelated to bococizumab, and resolved by Week 12. CONCLUSIONS: These studies demonstrated that bococizumab safely and effectively lowered LDL-C in hypercholesterolemic subjects on high doses of statin.
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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) | 0.002 | 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 | 0.002 |
| 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".