Targeting Liposomal Chemotherapy via Both Tumor Cell–Specific and Tumor Vasculature–Specific Ligands Potentiates Therapeutic Efficacy
Bibliographic record
Abstract
Neuroblastoma, the most common solid tumor of infancy derived from the sympathetic nervous system, continues to present a formidable clinical challenge. Sterically stabilized immunoliposomes (SIL) have been shown to enhance the selective localization of entrapped drugs to solid tumors, with improvements in therapeutic indices. We showed that SIL loaded with doxorubicin (DXR) and targeted to the disialoganglioside receptor GD(2) [aGD(2)-SIL(DXR)] led to a selective inhibition of the metastatic growth of experimental models of human neuroblastoma. By coupling NGR peptides that target the angiogenic endothelial cell marker aminopeptidase N to the surface of DXR-loaded liposomes [NGR-SL(DXR)], we obtained tumor regression, pronounced destruction of the tumor vasculature, and prolonged survival of orthotopic neuroblastoma xenografts. Here, we showed good liposome stability, long circulation times, and enhanced time-dependent tumor accumulation of both the carrier and the drug. Antivascular effects against animal models of lung and ovarian cancer were shown for formulations of NGR-SL(DXR). In the chick embryo chorioallantoic assay, NGR-SL(DXR) substantially reduced the angiogenic potential of various neuroblastoma xenografts, with synergistic inhibition observed for the combination of NGR-SL(DXR) with aGD(2)-SIL(DXR). A significant improvement in antitumor effects was seen in neuroblastoma-bearing animal models when treated with the combined formulations compared with control mice or mice treated with either tumor- or vascular-targeted liposomal formulations, administered separately. The combined treatment resulted in a dramatic inhibition of tumor endothelial cell density. Long-term survivors were obtained only in animals treated with the combined tumor- and vascular-targeted formulations, confirming the pivotal role of combination therapies in treating aggressive metastatic neuroblastoma.
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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".