Platelet extracellular vesicles are efficient delivery vehicles of doxorubicin, an anti-cancer drug: preparation and in vitro characterization
Bibliographic record
Abstract
Platelet extracellular vesicles (PEVs) are an emerging delivery vehi for anticancer drugs due to their ability to target and remain in the tumor microenvironment. However, there is still a lack of understanding regarding yields, safety, drug loading efficiencies, and efficacy of PEVs. In this study, various methods were compared to generate PEVs from clinical-grade platelets, and their properties were examined as vehicles for doxorubicin (DOX). Sonication and extrusion produced the most PEVs, with means of 496 and 493 PEVs per platelet (PLT), respectively, compared to 145 and 33 by freeze/thaw and incubation, respectively. The PEVs were loaded with DOX through incubation and purified by chromatography. The size and concentration of the PEVs and PEV-DOX were analyzed using dynamic light scattering and nanoparticle tracking analysis. The results showed that the population sizes and concentrations of PEVs and PEV-DOX were in the ranges of 120–150 nm and 1.2–6.2 × 1011 particles/mL for all preparations. The loading of DOX determined using fluorospectrometry was found to be 2.1 × 106, 1.7 × 106, and 0.9 × 106 molecules/EV using freeze/thaw, extrusion, and sonication, respectively. The internalization of PEVs was determined to occur through clathrin-mediated endocytosis. PEV-DOX were more efficiently taken up by MDA-MB-231 breast cancer cells compared to MCF7/ADR breast cancer cells and NIH/3T3 cells. DOX-PEVs showed higher anticancer activity against MDA-MB-231 cells than against MCF7/ADR or NIH/3T3 cells and better than acommercial liposomal DOX formulation. In conclusion, this study demonstrates that PEVs generated by PLTs using extrusion, freeze/thaw, or sonication can efficiently load DOX and kill breast cancer cells, providing a promising strategy for further evaluation in preclinical animal models. The study findings suggest that sonication and extrusion are the most efficient methods to generate PEVs and that PEVs loaded with DOX exhibit significant anticancer activity against MDA-MB-231 breast cancer cells.
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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) | 0.000 | 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".