Chemosensitization of carcinoma cells using epithelial cell adhesion molecule–targeted liposomal antisense against bcl-2/bcl-xL
Bibliographic record
Abstract
Nanoscale drug delivery systems, such as sterically stabilized immunoliposomes binding to internalizing tumor-associated antigens, can increase therapeutic efficacy and reduce toxicity to normal tissues compared with nontargeted liposomes. The epithelial cell adhesion molecule (EpCAM) is of interest as a ligand for targeted drug delivery because it is abundantly expressed in solid tumors but shows limited distribution in normal tissues. To generate EpCAM-specific immunoliposomes for targeted cancer therapy, the humanized single-chain Fv antibody fragment 4D5MOCB was covalently linked to the exterior of coated cationic liposomes. As anticancer agent, we encapsulated the previously described antisense oligonucleotide 4625 specific for both bcl-2 and bcl-xL. The EpCAM-targeted immunoliposomes (SIL25) showed specific binding to EpCAM-overexpressing tumor cells, with a 10- to 20-fold increase in binding compared with nontargeted control liposomes. No enhanced binding was observed on EpCAM-negative control cells. On cell binding, SIL25 was efficiently internalized by receptor-mediated endocytosis, ultimately leading to down-regulation of both bcl-2 and bcl-xL expression on both the mRNA and protein level, which resulted in enhanced tumor cell apoptosis. In combination experiments, the use of SIL25 led to a 2- to 5-fold sensitization of EpCAM-positive tumor cells of diverse origin to death induction by doxorubicin. Our data show the promise of EpCAM-specific drug delivery systems, such as antisense-loaded immunoliposomes, for targeted 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".