Combining doxorubicin and liposomal anti‐HER‐2/NEU antisense oligodeoxynucleotides to treat HER‐2/NEU‐expressing MDA‐MB‐435 breast tumor model
Bibliographic record
Abstract
This study assessed the in vivo therapeutic activity of an antisense molecule targeted against HER-2/neu expressing mRNA. Antisense activity was evaluated in female SCID/Rag2m mice bearing subcutaneous tumors derived from HER-2/neu-transfected MDA-MB-435 (MDA-MB-435(HER2)) cells, a transfected line derived from the human breast cancer MDA-MB-435 cell line. Animals were treated with free or liposome-encapsulated antisense. The area under the curve (AUC(0-24h)) of the liposomal formulated antisense was demonstrated to be more than 30-fold greater than that of free antisense following intravenous administration. Efficacy was determined by assessing changes in tumor growth rate as well as by an immunohistological end-point evaluating HER-2/neu expression. HER-2/neu protein expression was reduced in mice bearing HER-2/neu-transfected MDA-MB-435 tumors when treated with liposomal antisense. However, tumors in these mice grew at a faster rate than the control, a result that was interpreted to be a consequence of selection of a more rapidly proliferating HER-2/neu-negative subpopulation of cells. Effective control of the MDA-MB-435(HER2) tumors was achieved when antisense treatment was combined with doxorubicin. Tumors derived from animals treated with the combination of doxorubicin and the liposomal antisense against HER-2/neu exhibited no detectable levels of HER-2/neu expression. Antisense targeted against HER-2/neu mRNA was effective in reducing or eliminating HER-2/neu protein expression, and when combined wtih doxorubicin treatment was efficacious in the treatment of mice bearing HER-2/neu-overexpressing human xenograft 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) | 0.000 | 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".