Proteolytically Stable Cancer Targeting Peptides with High Affinity for Breast Cancer Cells
Bibliographic record
Abstract
Cancer cell targeting peptides have emerged as a highly efficient approach for selective delivery of chemotherapeutics and diagnostics to different cancer cells. However, the use of α-peptides in pharmaceutical applications is hindered by their enzymatic degradation and low bioavailability. Starting with a 10-mer α-peptide 18 that we developed previously, here we report three novel analogues of 18 that are proteolytically stable and display better (up to 3.5-fold) affinity profiles for breast cancer cells compared to 18. The design strategy involved replacement of two or three amino acids in the sequence of 18 with d-residues or β(3)-amino acids. Such replacement maintained the specificity for cancer cells (MDA-MB-435, MDA-MB-231, and MCF-7) with low affinity for control noncancerous cells (MCF-10A and HUVEC), showed an increase in secondary structure, and rendered the analogues completely stable to human serum and liver homogenate from mice. The three analogues are potentially safe with minimal cellular toxicity and are efficient targeting moieties for specific drug delivery to breast cancer cells. The strategy used here may be adapted to develop peptide analogues that will target other cancer cell types.
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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".