New groups of hypolipidemic medications based on inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9). Part 1
Bibliographic record
Abstract
Hypolipidemic therapy is one of the essential components for the management of patients with cardiovascular diseases (CVD). In this regard, the main task of modern research is to find new targets for creating additional effective groups of hypolipidemic medications. Canadian and French research groups led by N. Seidah and M. Abifadel discovered a new enzyme — proprotein convertase subtilisin-kexin type 9 (PCSK9) in 2003. It turned out to play an important role in lipid metabolism later. The main mechanism of action of PCSK9 is to regulate the density of low-density lipoprotein receptors (LDLR) in the cell membrane of hepatocytes. Increased activity of PCSK9 accelerates the degradation of LDL significantly, and leads to an increase in the concentration of atherogenic classes of lipoproteins — low-density lipoproteins (LDL). In contrast, reduced PCSK9 activity is accompanied by a decrease in LDL concentrations and a reduced risk of developing atherosclerosis and CVD. The second of the recently discovered and less studied mechanism of PCSK9 protearogenic action is an increase in inflammatory processes in the atherosclerotic plaque. Considering this adverse contribution of PCSK9 to the development and progression of atherosclerosis and CVD, the main task of the researchers was to develop medications that inhibit THIS enzyme. Several new groups of medications that target the stages of biosynthesis and the function of PCSK9 have been developed by now. In this article, we will focus on details discussing the mechanisms of action and effectiveness of the following groups of hypolipidemic medications: anti-PCSK9 monoclonal antibodies (alirocumab, evolocumab), small interfering ribonucleic acids (incliciran), and antisense nucleotides.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".