The biology of PCSK9 from the endoplasmic reticulum to lysosomes: new and emerging therapeutics to control low-density lipoprotein cholesterol
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) directly binds to the epidermal growth factor-like repeat A domain of low-density lipoprotein receptor and induces its degradation, thereby controlling circulating low-density lipoprotein cholesterol (LDL-C) concentration. Heterozygous loss-of-function mutations in PCSK9 can decrease the incidence of coronary heart disease by up to 88%, owing to lifelong reduction of LDL-C. Moreover, two subjects with PCSK9 loss-of-function mutations on both alleles, resulting in a total absence of functional PCSK9, were found to have extremely low circulating LDL-C levels without other apparent abnormalities. Accordingly, PCSK9 could represent a safe and effective pharmacological target to increase clearance of LDL-C and to reduce the risk of coronary heart disease. Recent clinical trials using anti-PCSK9 monoclonal antibodies that block the PCSK9:low-density lipoprotein receptor interaction were shown to considerably reduce LDL-C levels by up to 65% when given alone and by up to 72% in patients already receiving statin therapy. In this review, we will discuss how major scientific breakthroughs in PCSK9 cell biology have led to the development of new and forthcoming LDL-C-lowering pharmacological agents.
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.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".