Novel Insights into Structure–Activity Relationships of N‐Terminally Modified PACE4 Inhibitors
Bibliographic record
Abstract
PACE4 plays important roles in prostate cancer cell proliferation. The inhibition of this enzyme has been shown to slow prostate cancer progression and is emerging as a promising therapeutic strategy. In previous work, we developed a highly potent and selective PACE4 inhibitor, the multi-Leu (ML) peptide, an octapeptide with the sequence Ac-LLLLRVKR-NH2 . Here, with the objective of developing a useful compound for in vivo administration, we investigate the effect of N-terminal modifications. The inhibitory activity, toxicity, stability, and cell penetration properties of the resulting analogues were studied and compared to the unmodified inhibitor. Our results show that the incorporation of a polyethylene glycol (PEG) moiety leads to a loss of antiproliferative activity, whereas the attachment of a lipid chain preserves or improves it. However, the lipidated peptides are significantly more toxic when compared with their unmodified counterparts. Therefore, the best results were achieved not by the N-terminal extension but by the protection of both ends with the d-Leu residue and 4-amidinobenzylamide, which yielded the most stable inhibitor, with an excellent activity and toxicity profile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".