Microvesicle-Mediated Delivery of Minicircle DNA Results in Effective Gene-Directed Enzyme Prodrug Cancer Therapy
Bibliographic record
Abstract
Abstract An emerging approach for cancer treatment employs the use of extracellular vesicles, specifically exosomes and microvesicles, as delivery vehicles. We previously demonstrated that microvesicles can functionally deliver plasmid DNA to cells and showed that plasmid size and sequence, in part, determine the delivery efficiency. In this study, delivery vehicles comprised of microvesicles loaded with engineered minicircle (MC) DNA that encodes prodrug converting enzymes developed as a cancer therapy in mammary carcinoma models. We demonstrated that MCs can be loaded into shed microvesicles with greater efficiency than their parental plasmid counterparts and that microvesicle-mediated MC delivery led to significantly higher and more prolonged transgene expression in recipient cells than microvesicles loaded with the parental plasmid. Microvesicles loaded with MCs encoding a thymidine kinase (TK)/nitroreductase (NTR) fusion protein produced prolonged TK-NTR expression in mammary carcinoma cells. In vivo delivery of TK-NTR and administration of prodrugs led to the effective killing of both targeted cells and surrounding tumor cells via TK-NTR–mediated conversion of codelivered prodrugs into active cytotoxic agents. In vivo evaluation of the bystander effect in mouse models demonstrated that for effective therapy, at least 1% of tumor cells need to be delivered with TK-NTR–encoding MCs. These results suggest that MC delivery via microvesicles can mediate gene transfer to an extent that enables effective prodrug conversion and tumor cell death such that it comprises a promising approach to cancer therapy.
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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".