Apolipoprotein B Antisense Inhibition -Update on Mipomersen
Bibliographic record
Abstract
Dyslipidemia is one of the main risk factors leading to cardiovascular disease (CVD). The standard of therapy, administration of statins, in conjunction with lifestyle and habit changes, can improve high cholesterol levels in the majority of patients. However, some patients with familial hypercholesterolemia (FH) need low-density-lipoprotein cholesterol (LDL-C) apheresis, as the available medications fail to reduce LDL-C levels sufficiently even at maximum doses. Intense research on cholesterol reducing agents and rapid progress in drug design have yielded many approaches that reduce cholesterol absorption or inhibit its synthesis. Antisense oligonucleotides (ASOs) targeting the production of apolipoprotein B-100 (apoB-100), inhibitors of proprotein convertase subtilisin/kexin type 9, microsomal triglyceride transfer protein inhibitors, squalene synthase inhibitors, peroxisome proliferator-activated receptor agonists, and thyroid hormone receptor agonists are some of the evolving approaches for lipid-lowering therapies. We provide an overview of the apoB ASO approach and its potential role in the management of dyslipidemia. Mipomersen (ISIS-301012, KYNAMRO™) is a synthetic ASO targeting the mRNA of apoB-100, which is an essential component of LDL particles and related atherogenic lipoproteins. ASOs bind to target mRNAs and induce their degradation thereby resulting in reduced levels of the corresponding protein levels. Mipomersen has been investigated in different indications including homozygous and heterozygous FH, as well as in high-risk hypercholesterolemic patients. Recent phase II and III clinical studies have shown a 25-47% reduction in LDL-C levels in mipomersen-treated patients. If future studies continue to show such promising results, mipomersen would likely be a viable additional lipid-lowering therapy for high-risk populations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".