Experimental and clinical studies show that the probucol derivative AGI-1067 prevents vascular growth
Bibliographic record
Abstract
AGI-1067 is a derivative of probucol that is a promising new development for the treatment of restenosis and possibly atherosclerosis. In monkeys fed a high-fat diet for 1 year, AGI-1067 prevented the development of atherosclerosis. In these monkeys, AGI-1067 lowered plasma levels of low-density lipoprotein (LDL)-cholesterol and, in contrast to probucol, was capable of increasing high-density lipoprotein (HDL)-cholesterol levels. Although AGI1067 did not have marked lipid-lowering effects in two transgenic mouse models (the LDL-receptor-deficient and apolipoprotein-E-deficient models) fed a high-fat chow, it decreased the atherosclerotic lesion area in the aorta. In a mouse model of acute inflammation, the mRNA for the pro-inflammatory vascular cell adhesion molecule-1 and monocyte chemoattractant protein-1 was upregulated and this was inhibited by AGI-1067. AGI-1067 inhibited the TNF-alpha induction of redox-sensitive inflammatory proteins, vascular cell adhesion molecule-1, monocyte chemoattractant protein-1 and Eselectin, in cell culture. In addition, AGI-1067 is an antioxidant. In the Canadian Antioxidant Restenosis Trial (CART-1) of AGI-1067 in percutaneous coronary interventions, AGI-1067 had no effect on LDL-cholesterol but lowered HDL-cholesterol. At 6 months follow up, the lumen area of the percutaneous coronary interventions segments was greater in patients treated with AGI1067 than in untreated patients. Restenosis rates were 37.5% in the placebo group and 26% in the AGI-1067 group. The lumen area of reference segments was reduced in the placebo group but increased with the higher doses of AGI1067. Unlike probucol, AGI-1067 did not alter QTc interval.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".