Novel strategies to target proprotein convertase subtilisin kexin 9: beyond monoclonal antibodies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it