Peptide-Based Anti-PCSK9 Vaccines - An Approach for Long-Term LDLc Management
Bibliographic record
Abstract
BACKGROUND: Low Density Lipoprotein (LDL) hypercholesterolemia, and its associated cardiovascular diseases, are some of the leading causes of death worldwide. The ability of proprotein convertase subtilisin/kexin 9 (PCSK9) to modulate circulating LDL cholesterol (LDLc) concentrations made it a very attractive target for LDLc management. To date, the most advanced approaches for PCSK9 inhibition are monoclonal antibody (mAb) therapies. Although shown to lower LDLc significantly, mAbs face functional limitations because of their relatively short in vivo half-lives necessitating frequent administration. Here, we evaluated the long-term efficacy and safety of PCSK9-specific active vaccines in different preclinical models. METHODS AND FINDING: PCSK9 peptide-based vaccines were successfully selected by our proprietary technology. To test their efficacy, wild-type (wt) mice, Ldlr+/- mice, and rats were immunized with highly immunogenic vaccine candidates. Vaccines induced generation of high-affine PCSK9-specific antibodies in all species. Group mean total cholesterol (TC) concentration was reduced by up to 30%, and LDLc up to 50% in treated animals. Moreover, the PCSK9 vaccine-induced humoral immune response persisted for up to one year in mice, and reduced cholesterol levels significantly throughout the study. Finally, the vaccines were well tolerated in all species tested. CONCLUSIONS: Peptide-based anti-PCSK9 vaccines induce the generation of antibodies that are persistent, high-affine, and functional for up to one year. They are powerful and safe tools for long-term LDLc management, and thus may represent a novel therapeutic approach for the prevention and/or treatment of LDL hypercholesterolemia-related cardiovascular diseases in humans.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".