Mechanisms of cell penetration and cytotoxicity of ultrasmall Au nanoparticles conjugated to doxorubicin and/or targeting peptides
Bibliographic record
Abstract
The goals of this work were to determine whether conjugation of any of four selected peptides to Au nanoparticles improved their delivery to B16 melanoma in vitro and in vivo. In in vitro cytotoxicity assays, peptides and conjugates were endocytosed but did not escape from endosomes. None of the peptides showed any cytotoxicity, with or without conjugation to the nanoparticles. The combination of peptides and doxorubicin did not improve upon the cytotoxicity of gold-doxorubicin alone. We then tested targeting in vivo using inductively coupled plasma mass spectrometry to quantify the concentration of Au in the organs of B16 tumor-bearing mice 4, 24, and 72 h after intravenous Au nanoparticle injection. These experiments showed that in some cases, peptide conjugation improved upon the enhanced permeability and retention (EPR) effect. A peptide based upon the myxoma virus and the cyclic RGD peptide were both effective at tumor targeting; myxoma was more effective with un-PEGylated particles, and cRGD with PEGylated particles. The FREG and melanocyte stimulating hormone (MSH) peptides did not improve targeting. These results suggest that these peptides may improve delivery of Au particles to tumors, but also may prevent entry of particles into cell nuclei.
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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".