Specific Targeting of Recombinant Human Pancreatic Ribonuclease 1 using Gonadotropin-Releasing Hormone Targeting Peptide toward Gonadotropin-Releasing Hormone Receptor-Positive Cancer Cells.
Bibliographic record
Abstract
Background: Targeted drug delivery is a novel method to specifically deliver anticancer therapeutics to tumor sites. Gonadotropin-releasing hormone (GnRH) is a decapeptide, and its target binding property has attracted attention as a means of targeted drug delivery. Human pancreatic ribonuclease 1 (hpRNase1) has been shown to exert anticancer properties, when fused to a targeting moiety. The goal of the present study was to add a GnRH targeting peptide to the N-terminus of hpRNase1 to specifically target GnRH receptor (GnRH-R) expressing cells. Methods: tests were performed to determine the significant differences between the study groups. Results: GnRH-hpRNase1 chimeric protein specifically inhibited the proliferation of PC-3 (P=0.021), LNCaP (P=0.034), and AD-Gn (P=0.041) cells, while the growth of negative cells (AD-293) was not significantly affected (P=0.081). GnRH-hpRNase1 decreased the IC50 values more than non-fused hpRNase1, by approximately 26.5-fold (P=0.036) for PC-3 cells, and exerted its growth inhibitory effects through apoptosis induction. Conclusion: Fusion of GnRH to hpRNase1 structure produced an enzyme, which could specifically target tumor cells. This approach can be used to eliminate tumors that harbor GnRH-R.
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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".