Bempedoic Acid Lowers Low-Density Lipoprotein Cholesterol and Attenuates Atherosclerosis in Low-Density Lipoprotein Receptor–Deficient ( <i>LDLR</i> <sup>+/−</sup> and <i>LDLR</i> <sup>−/−</sup> ) Yucatan Miniature Pigs
Bibliographic record
Abstract
Objective— Bempedoic acid (BemA; ETC-1002) is a novel drug that targets hepatic ATP-citrate lyase to reduce cholesterol biosynthesis. In phase 2 studies, BemA lowers elevated low-density lipoprotein cholesterol (LDL-C) in hypercholesterolemic patients. In the present study, we tested the ability of BemA to decrease plasma cholesterol and LDL-C and attenuate atherosclerosis in a large animal model of familial hypercholesterolemia. Approach and Results— Gene targeting has been used to generate Yucatan miniature pigs heterozygous ( LDLR +/− ) or homozygous ( LDLR −/− ) for LDL receptor deficiency (ExeGen). LDLR +/− and LDLR −/− pigs were fed a high-fat, cholesterol-containing diet (34% kcal fat; 0.2% cholesterol) and orally administered placebo or BemA for 160 days. In LDLR +/− pigs, compared with placebo, BemA decreased plasma cholesterol and LDL-C up to 40% and 61%, respectively. In LDLR −/− pigs, in which plasma cholesterol and LDL-C were 5-fold higher than in LDLR +/− pigs, BemA decreased plasma cholesterol and LDL-C up to 27% and 29%, respectively. Plasma levels of triglycerides and high-density lipoprotein cholesterol, fasting glucose and insulin, and liver lipids were unaffected by treatment in either genotype. In the aorta of LDLR +/− pigs, BemA robustly attenuated en face raised lesion area (−58%) and left anterior descending coronary artery cross-sectional lesion area (−40%). In LDLR −/− pigs, in which lesions were substantially more advanced, BemA decreased aortic lesion area (−47%) and left anterior descending coronary artery lesion area (−48%). Conclusions— In a large animal model of LDLR deficiency and atherosclerosis, long-term treatment with BemA reduces LDL-C and attenuates the development of aortic and coronary atherosclerosis in both LDLR +/− and LDLR −/− miniature pigs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".