Peptide Modified Polymeric Micelles Specific for Breast Cancer Cells
Bibliographic record
Abstract
The specific targeting ability of novel breast cancer targeting peptides as ligands coupled to polymeric micelles was evaluated in the present study. In this context, engineered breast cancer cell targeting peptides, denoted as peptide 11 (RGDPAYQGRFL) and peptide 18 (WXEAAYQRFL), were compared with the lead 12-mer p160 peptide and cyclic RGDfK peptide. All four peptides were conjugated individually to poly(ethylene oxide)-b-poly(caprolactone) (PEO-b-PCL) diblock polymeric micelles to obtain targeted carrier systems PM11, PM18, PM 160, and PM c-RGD. Physical blending of the peptides 11 and 18 with PEO-b-PCL was also done to yield combination micelles, comPM11 and comPM18. The structural confirmation of polymer was carried out using (1)H NMR and MALDI-TOF, and the size distribution and zeta potential of the micelles were determined using dynamic light scattering. Lipophilic cyanine fluorescent probe DiI was physically incorporated in the polymeric micelles to imitate the hydrophobic drug loaded in the micellar core. The cellular uptake of DiI-loaded peptide-containing polymeric micelles by MDA-MB-435, MDA-MB-231, and MCF7 breast cancer cell lines, as well as HUVEC and MCF10A noncancerous cells, were analyzed using flow cytometry and confocal microscopy techniques. Modification of polymeric micelles with peptide 11 or 18 led to an increase in micellar uptake specifically in breast cancer cells compared to p160, c-RGD modified, or naked micelles. The peptide-micelle combinations (comPM11 and comPM18) displayed better uptake by the cells compared to the covalently conjugated PM11 and PM18 micelles; however, the combinations were less selective toward cancer cells. The results point to a potential for peptides 11- and 18-micelle conjugates as attractive platforms for improved performance of a wide range of chemotherapeutic drugs and/or imaging agents in cancer therapy and diagnosis.
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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 | 0.000 | 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) | 0.002 | 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".