PCSK9 inhibition: drug development for low-density lipoprotein lowering
Bibliographic record
Abstract
Reduction of low-density lipoprotein (LDL)-cholesterol (LDL-C) with statin therapy has been demonstrated to lower the risk of cardiovascular diseases (CVD). There is a need for additional LDL-C-lowering therapies beyond statins as it remains a challenge among high-risk patients with elevated initial LDL-C levels, genetic hyperlipidemia syndromes and drug-related side effects to achieve target LDL-C levels on statin therapy alone. Targeted approaches to reduce LDL-C have been developed to address the residual risk of CVD. PCSK9, one of the serine proteases, is involved in control of the expression and function of the LDL receptor (LDLR). PCSK9 binds to LDL receptors, leading to their accelerated degradation and to increased LDL-C levels. PCSK9 inhibition is a novel therapeutic approach to reduce LDL-C levels. Monoclonal antibodies directed to PCSK9 have been demonstrated to decrease PCSK9 activity and reduce plasma LDL-C levels by 40–65% in Phase I and II trials. Current antibody-based PCSK9 therapies have a long half-life, requiring bimonthly or monthly dosing injection regimens. Preclinical studies testing of other PCSK9 inhibition approaches such as antisense oligonucleotides (ASOs), lipidoid nanoparticle (LNP)-formulated siRNA and small peptides to block the interaction between PCSK9 and the LDLR also show promise. Long-term safety, persistence in LDL-lowering effects, as well as efficacy of CVD event reduction of PCSK9 inhibition approaches beyond maximum tolerated statin treatment are required. PCSK9 inhibition shows promise to offer a novel therapeutic strategy for patients at high risk of CVD who are unable to meet target LDL-C levels on statin therapy alone.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".