Antitumor activity of an epithelial cell adhesion molecule–targeted nanovesicular drug delivery system
Bibliographic record
Abstract
Site-specific delivery of anticancer agents to tumors represents a promising therapeutic strategy because it increases efficacy and reduces toxicity to normal tissues compared with untargeted drugs. Sterically stabilized immunoliposomes (SIL), guided by antibodies that specifically bind to well internalizing antigens on the tumor cell surface, are effective nanoscale delivery systems capable of accumulating large quantities of anticancer agents at the tumor site. The epithelial cell adhesion molecule (EpCAM) holds major promise as a target for antibody-based cancer therapy due to its abundant expression in many solid tumors and its limited distribution in normal tissues. We generated EpCAM-directed immunoliposomes by covalently coupling the humanized single-chain Fv antibody fragment 4D5MOCB to the surface of sterically stabilized liposomes loaded with the anticancer agent doxorubicin. In vitro, the doxorubicin-loaded immunoliposomes (SIL-Dox) showed efficient cell binding and internalization and were significantly more cytotoxic against EpCAM-positive tumor cells than nontargeted liposomes (SL-Dox). In athymic mice bearing established human tumor xenografts, pharmacokinetic and biodistribution analysis of SIL-Dox revealed long circulation times in the blood with a half-life of 11 h and effective time-dependent tumor localization, resulting in up to 15% injected dose per gram tissue. These favorable pharmacokinetic properties translated into potent antitumor activity, which resulted in significant growth inhibition (compared with control mice), and was more pronounced than that of doxorubicin alone and nontargeted SL-Dox at low, nontoxic doses. Our data show the promise of EpCAM-directed nanovesicular drug delivery for targeted therapy of solid tumors.
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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".