Cholesteryl ester transfer protein inhibitors for dyslipidemia: focus on dalcetrapib
Bibliographic record
Abstract
Alyse S Goldberg, Robert A HegeleDepartment of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, CanadaAbstract: Among the noteworthy recent stories in the management and prevention of atherosclerotic cardiovascular disease (CVD) is the saga of the development of pharmacological inhibitors of cholesteryl ester transfer protein (CETP). Inhibiting CETP significantly raises plasma concentrations of high-density lipoprotein cholesterol, which has long been considered a marker of reduced CVD risk. However, the first CETP inhibitor, torcetrapib, showed a surprising increase in CVD events, despite a dramatic increase in high-density lipoprotein cholesterol levels. This paradox was explained by putative off-target effects not related to CETP inhibition that were specific to torcetrapib. Subsequently, three newer CETP inhibitors, namely dalcetrapib, anacetrapib, and evacetrapib, were at various phases of clinical development in 2012. Each of these had encouraging biochemical efficacy and safety profiles. Dalcetrapib even had human arterial imaging results that tended to look favorable. However, the dalcetrapib development program was recently terminated, presumably because interim analysis of a large CVD outcome trial indicated no benefit. These events raise important questions regarding the validity of the mechanism of CETP inhibition and the broader issue of whether pharmacological raising of high-density lipoprotein cholesterol itself is a useful strategy for CVD risk reduction.Keywords: dalcetrapib, CETP inhbitor, HDL, cardiovascular disease
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 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 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".