Lipopeptide Hydrogel Possesses Adjuvant‐Like Properties for the Delivery of the GPC‐3 Peptide‐derived Antigen
Bibliographic record
Abstract
Abstract Peptide‐based cancer vaccines are attractive as they can elicit antigen‐specific cytotoxic T lymphocytes (CTLs) with minimal side effects. However, low immunogenic responses have delayed their translation to the clinic. Adjuvants, which are required in vaccines to boost immunogenicity, currently lack the ability to simultaneously induce specific cell‐mediated effects and act as a delivery system. These concerns are addressed by engineering a novel class of adjuvants; lipopeptide hydrogels (LPHs). Myristic acid conjugated to diphenylalanine (Myr‐FF), myristic acid diphenylalanine‐tyrosine (Myr‐FFY), and combination gel (Cogel) are synthesized and tested for immune modulation. They are loaded with a glypican‐3 (GPC‐3)‐derived peptide designed for targeting hepatocellular carcinoma (HCC). The LPHs enable the sustained release of the GPC‐3 peptide over two weeks, followed by antigen uptake by antigen‐presenting cells (APCs). These LPHs are shown to act as a toll‐like receptor (TLR)‐2 agonist, cause an upregulation of costimulatory molecules (CD80, CD83, CD86), and the secretion of cytokines (IL‐6, TNF‐α, etc). In vivo studies showed that LPHs cause an increase in leukocyte infiltration within LNs with no visible toxicity. These LPHs exhibit adjuvant‐like characteristics, thus providing a promising platform for future investigation in enhancing peptide‐based vaccine efficacy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".