Tuning Peptide-Based Nanofibers for Achieving Selective Doxorubicin Delivery in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Introduction: The design of delivery tools that efficiently transport drugs into cells remains a major challenge in drug development for most pathological conditions. Triple-negative breast cancer (TNBC) is a very aggressive subtype of breast cancer with poor prognosis and limited effective therapeutic options. Purpose: In TNBC treatment, chemotherapy remains the milestone, and doxorubicin (Dox) represents the first-line systemic treatment; however, its non-selective distribution causes a cascade of side effects. To address these problems, we developed a delivery platform based on the self-assembly of amphiphilic peptides carrying several moieties on their surfaces, aimed at targeting, enhancing penetration, and therapy. Methods: Through a single-step self-assembly process, we used amphiphilic peptides to obtain nanofibers decorated on their surfaces with the selected moieties. The surface of the nanofiber was decorated with a cell-penetrating peptide (gH625), an EGFR-targeting peptide (P22), and Dox bound to the cleavage sequence selectively recognized and cleaved by MMP-9 to obtain on-demand drug release. Detailed physicochemical and cellular analyses were performed. Results: The obtained nanofiber (NF-Dox) had a length of 250 nm and a diameter of 10 nm, and it was stable under dilution, ionic strength, and different pH environments. The biological results showed that the presence of gH625 favored the complete internalization of NF-Dox after 1h in MDA-MB 231 cells, mainly through a translocation mechanism. Interestingly, we observed the absence of toxicity of the carrier (NF) on both healthy cells such as HaCaT and TNBC cancer lines, while a similar antiproliferative effect was observed on TNBC cells after the treatment with the free-Dox at 50 µM and NF-Dox carrying 7.5 µM of Dox. Discussion: We envision that this platform is extremely versatile and can be used to efficiently carry and deliver diverse moieties. The knowledge acquired from this study will provide important guidelines for applications in basic research and biomedicine.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".