Emerging anti-inflammatory drugs for atherosclerosis
Bibliographic record
Abstract
INTRODUCTION: Cardiovascular disease is the most common cause of morbidity and mortality worldwide. Inflammation is responsible for initiation and progression of atherosclerosis, and leads to plaque vulnerability. Evidence-based therapies reduce the risk of initial and recurrent cardiovascular events but many patients experience recurrent events due to the failure of conventional therapies to adequately address inflammation. AREAS COVERED: Statins were originally developed for their LDL cholesterol-lowering effects, but are now thought to improve cardiovascular morbidity and mortality through anti-inflammatory effects as well. Drugs that inhibit the various inflammatory pathways responsible for atherosclerosis are the subject of current research. These include antioxidants, phospholipase A(2) inhibitors, leukotriene pathway inhibitors, CCL2-CCR2 pathway inhibitors, non-specific anti-inflammatory drugs (i.e., methotrexate), IL-1 inhibitors and p-selectin inhibitors. EXPERT OPINION: Currently, only three anti-inflammatory drugs (methotrexate, darapladib and canakinumab) are being investigated in Phase III clinical trials of atherosclerosis. The development of cardiovascular drugs requires long, expensive Phase III trials to demonstrate incremental improvement in cardiovascular events. Imaging end points and soluble biomarkers accelerate Phase II development, but further validation is needed before these can be used as surrogate end points in the large trials leading to drug approval. Improved access to currently available therapies like statins would decrease the burden of cardiovascular disease worldwide.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".