Enhanced anti-tumor activity of the Multi-Leu peptide PACE4 inhibitor transformed into an albumin-bound tumor-targeting prodrug
Bibliographic record
Abstract
Abstract The proprotein convertase PACE4 has been validated as a potential target to develop new therapeutic interventions in prostate cancer (PCa). So far, the most effective compound blocking the activity of this enzyme has been designed based on the structure of a small peptide Ac-LLLLRVKR-NH2 known as the Multi-Leu (ML) peptide. Optimization of this scaffold led to the synthesis of compound C23 (Ac-[DLeu]LLLRVK-amidinobenzylamide) with a potent in vivo inhibitory effect on the tumor growth. However, further developments of PACE4 inhibitors may require additional improvements to counter their rapid renal clearance and to increase their tumor targeting efficiency. Herein, we explored the transformation of the ML-peptide into an albumin-binding prodrug containing a tumor specific release mechanism based on the prostate-specific antigen. Our data confirms that intravenous treatment using the ML-peptide alone has little effect on tumor growth, whereas by using the ML-prodrug in LNCaP xenograft-bearing mice it was significantly reduced. Additionally, excellent in vivo stability and tumor-targeting efficiency was demonstrated using a radiolabelled version of this compound. Taken together, these results provide a solid foundation for further development of targeted PACE4 inhibition in PCa.
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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) | 0.001 | 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".