Novel strategies to target proprotein convertase subtilisin kexin 9: beyond monoclonal antibodies
Bibliographic record
Abstract
Since the discovery of the role of proprotein convertase subtilisin kexin 9 (PCSK9) in the regulation of low-density lipoprotein cholesterol (LDL-C) in 2003, a paradigm shift in the treatment of hypercholesterolaemia has occurred. The PCSK9 secreted into the circulation is a major downregulator of the low-density lipoprotein receptor (LDLR) protein, as it chaperones it to endosomes/lysosomes for degradation. Humans with loss-of-function of PCSK9 exhibit exceedingly low levels of LDL-C and are protected from atherosclerosis. As a consequence, innovative strategies to modulate the levels of PCSK9 have been developed. Since 2015 inhibitory monoclonal antibodies (evolocumab and alirocumab) are commercially available. When subcutaneously injected every 2-4 weeks, they trigger a ∼60% LDL-C lowering and a 15% reduction in the risk of cardiovascular events. Another promising approach consists of a liver-targetable specific PCSK9 siRNA which results in ∼50-60% LDL-C lowering that lasts up to 6 months (Phases II-III clinical trials). Other strategies under consideration include: (i) antibodies targeting the C-terminal domain of PCSK9, thereby inhibiting the trafficking of PCSK9-LDLR to lysosomes; (ii) small molecules that either prevent PCSK9 binding to the LDLR, its trafficking to lysosomes or its secretion from cells; (iii) complete silencing of PCSK9 by CRISPR-Cas9 strategies; (iv) PCSK9 vaccines that inhibit the activity of circulating PCSK9. Time will tell whether other strategies can be as potent and safe as monoclonal antibodies to lower LDL-C levels.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".