Targeting combinations of liposomal drugs to both tumor vasculature cells and tumor cells for the treatment of HER2-positive breast cancer
Bibliographic record
Abstract
PURPOSE: We used two ligand-modified liposomal drugs to selectively deliver two different chemotherapeutics to tumor cells (TC) and tumor vasculature endothelial (TV) cells, and examined the therapeutic effect of altering the order of treatment administration, and the effect of the temporal spacing of the treatments on the accumulation of a second dose of liposomes and therapeutic activity. METHODS: Studies were completed in an orthotopic mouse model of human epidermal growth factor receptor 2 (HER2)-positive breast cancer, utilizing liposomal doxorubicin, targeted to TC via αHER2 Fab' fragments, and liposomal vincristine, targeted to CD13 on TV cells via NGR peptides. RESULTS AND DISCUSSION: Combination treatment with TV-targeted plus TC-targeted therapies was therapeutically superior to either single agent; switching the order of administration of the combination did not alter treatment efficacy. The tumor accumulation of a second dose of liposomes was increased if administered at 4 days after pre-treatment with TV-targeted therapy. Using a treatment schedule exploiting this increase, the dose of simultaneously administered combination therapy was halved without compromising therapeutic effect. CONCLUSION: Proof-of-concept studies revealed the therapeutic potential of a dual-targeted two drug approach against HER2-positive breast cancer, and may be applicable to the treatment of other solid tumors.
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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".