Abstract 13853: Biological Action of ABCA1 Ligand Peptide ATI-5261 and its Analogs With Improved Safety Features
Bibliographic record
Abstract
Recent studies have produced apolipoprotein (apo) mimetic peptides that reduce substantial atherosclerosis in animal models when given orally, subcutaneously or intravenously in lipid-free form and composed of L-amino acids. These findings suggest small peptides may represent a viable approach for the treatment of acute coronary syndrome and atherosclerosis in humans. Despite these favorable features, several adverse effects have been noted in preclinical studies, including elevations in blood triglycerides (TG) and cytotoxic responses (ALT, AST and creatine kinase) at high doses. To address these issues, we established a screening paradigm involving >200 analogs of ATI-5261, a novel apoE mimetic peptide and ABCA1 ligand that stimulates cellular cholesterol efflux with high potency. Safety profiles of peptides were evaluated in mice and rats, and anti-atherosclerosis effects in apoE deficient (apoE-/-) mice fed high-fat western-diet (HFWD) for 14 weeks. These studies revealed the TG elevating effects and cytotoxic responses of class A a-helical peptides were governed by a different set of molecular determinants apart from those of ABCA1 mediated cholesterol efflux. A lead peptide (CS6253) emerged with improved safety features that retained the favorable drugability properties (i.e. solubility), potent and selective cholesterol efflux activity via ABCA1 (km= 0.26±0.14 vs. 0.25±0.04 uM for ATI-5261, respectively), and mediated high affinity binding to ABCA1 as well as produced nascent HDL similar to full-length apoA-I. In vivo CS6253 stimulated macrophage specific RCT and reduced (32%) substantial atherosclerosis in apoE-/- mice when administered (SQ or IP) at a dose of 30 mg/kg over 6 weeks. These structure-activity studies from lead optimization of the ATI-5261 peptide series provide new and exciting insights for improving the therapeutic potential of apo mimetic peptides.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".