Proprotein convertase subtilisin / kexin 9 (PCSK9) inhibitors and the future of dyslipidemia therapy: an updated patent review (2011-2015)
Bibliographic record
Abstract
INTRODUCTION: The identification by Abifadel et al. in 2003 of the first mutations of PCSK9 was the major breakthrough in the cholesterol field that led to a new therapeutic target. This discovery paved the way to new lipid lowering drugs reducing LDL-cholesterol levels through the inhibition of PCSK9. Two anti-PCSK9 monoclonal antibodies have received FDA and EMA approvals: Alirocumab and Evolocumab. Areas covered: This article reviews the different strategies that are pursued to modulate the functional activity of PCSK9 for lowering LDL-cholesterol levels. It also provides a brief overview of the patents related to PCSK9 from 2011 until the end of 2015. This review is addressed to researchers from academia and pharmaceutical companies who are engaged in PCSK9 research/cholesterol regulation. Readers will gain an up-to-date overview of the different strategies that have been investigated to reduce PCSK9, focusing on anti-PCSK9 monoclonal antibodies and the related clinical trials. Expert opinion: Anti-PCSK9 antibodies are a new class of lipid lowering drugs with promising results in reducing LDL-cholesterol. Long-term ongoing studies investigating on a large scale the efficacy and safety of the anti-PCSK9 antibodies and their cardiovascular outcomes are eagerly awaited.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".