Therapeutic Targets to Raise HDL in Patients at Risk or with Coronary Artery Disease
Bibliographic record
Abstract
The plasma levels of high-density lipoprotein (HDL) cholesterol are inversely related to cardiovascular risk. Traditional HDL-raising therapies, like fibrates, PPAR-γ agonists, and nicacin, among others, are associated with undesirable side effects, limited efficacy, or have not yet been shown to improve morbidity and mortality on top of statins in clinical outcome trials. A novel pharmacological target for raising circulating HDL-C levels is the cholesterol ester transfer protein (CETP), an enzyme that facilitates the transport of cholesteryl esters and triglycerides between the lipoproteins. Four pharmacological small-molecule inhibitors of CETP, i.e. torcetrapib (Pfizer), dalcetrapib (JTT-705; Roche), anacetrapib (Merck), and evacetrapib (Eli Lilly) have been developed. Notwithstanding a marked increase in HDL, torcetrapib was associated with an increase in all-cause mortality in the ILLUMINATE trial and raised safety concerns related to the off-target effects of CETP inhibition. Most recently, development of dalcetrapib was abruptly stopped due to a lack of clinically meaningful efficacy. Thus, it will be of utmost importance to demonstrate that the remaining CETP inhibitors in development not only increase HDL-C levels in plasma, but also improve HDL-function in patients with coronary disease or an acute coronary syndrome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".