Pharmacodynamic Behavior of Liposomal Antisense Oligonucleotides Targeting Her-2/neu and Vascular Endothelial Growth Factor in an Ascitic MDA435/LCC6 Human Breast Cancer Model
Bibliographic record
Abstract
The nature of anti-cancer therapeutics is currently undergoing a paradigm change, with biologic agents slowly being introduced into the therapeutic armory, displacing or complimenting the traditionally used cytotoxic agents. These new agents include monoclonal antibodies, recombinant DNA, antisense oligonucleotides (ASO) and others. To assess the new therapeutics, new predictive models are required. Utilizing the MDA435/LCC6 human breast cancer xenograft model, the pharmacokinetic behavior of antisense oligonucleotides targeted against vascular endothelial growth factor and HER-2/neu was assessed. For pharmacodynamic analysis, ASO in buffer or encapsulated in a liposomal formulation were injected intravenously or intraperitoneally into MDA435/LCC6 ascites tumor-bearing mice. Plasma antisense elimination, tissue distribution, total peritoneal antisense and peritoneal cell associated antisense levels were determined. Liposomal encapsulation led to significant decreases in the plasma elimination rate, as evidenced by an approximate 10-fold increase in mean AUC over 24 hours, as well as enhanced peritoneal cell delivery in mice bearing ascites tumors. Tissue distribution studies of both free and liposome encapsulated ASO indicated that ASO distribution was dictated primarily by the liposomal carrier when administered in liposomal form.
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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".