The end of the road for CETP inhibitors after torcetrapib?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it