Oral and Non-Oral Cholesterol-Lowering Drugs with PCSK9 and Other Biomolecules as Targets: Present Status and Future Prospects
Bibliographic record
Abstract
The accumulation of high levels of cholesterol associated with low-density lipoprotein (LDL) in the bloodstream is the key risk factor for stroke and cardiovascular diseases. Therefore, reducing the concentration of LDL-cholesterol in the blood and maintaining it at an optimum level are vital especially for hypercholesterolemic individuals and cardiovascular patients. Thus, the study of cholesterol management and regulation in the physiological system has drawn significant attention from researchers across the globe. This led to the discovery of several cholesterol-lowering drugs which have been approved for administration either via oral or non-oral routes. Owing to the high comfort level, reduced or no pain, and fewer side effects with oral administration, more focus has been directed towards the development of oral-based cholesterol-lowering drugs. The other modes of administration such as intravenous or intramuscular injections are complex and sometimes painful and less tolerable. Therefore, there was a significant interest to develop oral drugs targeting PCSK9. In fact, some progress has been accomplished in recent years. This review provides an overview of the existing cholesterol-lowering drugs, and the progress made so far with oral-based PCSK9 drugs for lowering LDL-cholesterol. The review is presented in several sections highlighting the molecular targets, the individual drugs, and the modes of administration, with a focus on the oral route.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".