The Penetration of Anticancer Drugs through Tumor Tissue as a Function of Cellular Adhesion and Packing Density of Tumor Cells
Bibliographic record
Abstract
To reach cancer cells in optimal quantities, therapeutic agents must be delivered to tumors through their imperfect blood vascular system, cross vessel walls into the interstitium, and penetrate multiple layers of tissue. Strategies to enhance drug penetration have potential to improve therapeutic outcome. The development of multicellular layers (MCLs), in which tumor cells are grown on a semipermeable Teflon support membrane, has facilitated quantification of drug penetration through solid tissue. The goals of the present study were to quantify the penetration of anticancer drugs as a function of cellular adhesion and packing density and to determine the effects of variable penetration on therapeutic efficacy in this model system. We compared the properties of MCLs grown from two epithelioid and round subclones of a colon carcinoma cell line. One pair of epithelioid and round sublines differed in expression of alpha-E-catenin, and both pairs generated MCLs with different packing density. The penetration of commonly used anticancer agents (paclitaxel, doxorubicin, methotrexate, and 5-fluorouracil) through MCLs derived from these cell lines was significantly greater through the round (loosely packed) than through the epithelioid (tightly packed) sublines. In MCLs treated with doxorubicin, we observed greater survival in the tightly packed cell lines than in the loosely packed cell lines. Impaired penetration of anticancer agents through MCLs derived from the tightly packed cell lines and relative resistance to killing of cells within them by doxorubicin treatment strengthen the role of tumor cell adhesion and packing density as contributing to drug resistance.
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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".