The end of the road for CETP inhibitors after torcetrapib?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Because high-density lipoprotein cholesterol (HDL-C) levels are inversely related to cardiovascular disease (CVD), raising HDL-C levels would seem intuitively valuable. However, the recent failure of the cholesteryl ester transfer protein (CETP) inhibitor torcetrapib to decrease CVD has raised doubts regarding HDL-C raising in general and CETP inhibition in particular for CVD prevention. We briefly discuss the complexity of HDL metabolism, caveats of CETP inhibition, possible mechanisms for torcetrapib's failure, and the potential utility of other CETP inhibitors. RECENT FINDINGS: Torcetrapib likely failed because of off-target effects, since other CETP inhibitors, such as dalcetrapib (JTT-705/R1658) or anacetrapib (MK-0859), do not increase blood pressure, a specific pressor effect of tocetrapib that appears to be CETP-independent. In small human trials of short duration, anacetrapib and dalcetrapib appear to improve the lipoprotein profile without obvious adverse effects, so far. SUMMARY: The relationship between HDL metabolism, pharmacologic CETP inhibition, and atherosclerosis requires further elucidation. There seems to be sufficient evidence that evaluation of CETP inhibitors such as dalcetrapib and anacetrapib should proceed, if cautiously, since it remains uncertain whether the increased CVD risk with torcetrapib was related to agent-specific off-target effects or more generally to CETP inhibition as a mechanism to raise HDL.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".