Binding and internalization of NGR-peptide-targeted liposomal doxorubicin (TVT-DOX) in CD13-expressing cells and its antitumor effects
Bibliographic record
Abstract
In an effort to develop new agents and molecular targets for the treatment of cancer, aspargine-glycine-arginine (NGR)-targeted liposomal doxorubicin (TVT-DOX) is being studied. The NGR peptide on the surface of liposomal doxorubicin (DOX) targets an aminopeptidase N (CD13) isoform, specific to the tumor neovasculature, making it a promising strategy. To further understand the molecular mechanisms of action, we investigated cell binding, kinetics of internalization as well as cytotoxicity of TVT-DOX in vitro. We demonstrate the specific binding of TVT-DOX to CD13-expressing endothelial [human umbilical vein endothelial cells (HUVEC) and Kaposi sarcoma-derived endothelial cells (SLK)] and tumor (fibrosarcoma, HT-1080) cells in vitro. Following binding, the drug was shown to internalize through the endosomal pathway, eventually leading to the localization of doxorubicin in cell nuclei. TVT-DOX showed selective toxicity toward CD13-expressing HUVEC, sparing the CD13-negative colon-cancer cells, HT-29. Additionally, the nontargeted counterpart of TVT-DOX, Caelyx, was less cytotoxic to the CD13-positive HUVECs demonstrating the advantages of NGR targeting in vitro. The antitumor activity of TVT-DOX was tested in nude mice bearing human prostate-cancer xenografts (PC3). A significant growth inhibition (up to 60%) of PC3 tumors in vivo was observed. Reduction of tumor vasculature following treatment with TVT-DOX was also apparent. We further compared the efficacies of TVT-DOX and free doxorubicin in the DOX-resistant colon-cancer model, HCT-116, and observed the more pronounced antitumor effects of the TVT-DOX formulation over free DOX. The potential utility of TVT-DOX in a variety of vascularized solid tumors is promising.
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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) | 0.000 | 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 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".