Limited penetration of anticancer drugs through tumor tissue: a potential cause of resistance of solid tumors to chemotherapy.
Bibliographic record
Abstract
PURPOSE: Potential causes of drug resistance in solid tumors include genetically determined factors expressed in individual cells and those related to the solid tumor environment. Important among the latter is the requirement for drugs to penetrate into tumor tissue and to achieve a lethal concentration in all of the tumor cells. The present study was designed to characterize further the multicellular layer (MCL) method for studying drug penetration through tumor tissue and to provide information about tissue penetration for drugs used commonly in the treatment of human cancer. EXPERIMENTAL DESIGN: EMT-6 mouse mammary and MGH-U1 human bladder cancer cells were grown on collagen-coated semiporous Teflon membranes to form MCLs approximately 200 microm thick. The properties of MCLs were compared with those of tumors grown in mice from the same cells. The penetration of drugs through the MCL was evaluated by using radiolabeled drugs or analytical methods. RESULTS: The MCL developed an extracellular matrix containing both laminin and collagen, although there were some differences in expression of extracellular matrix proteins. Electron microscopy showed rare desmosomes in both MCL and tumors. The penetration of cisplatin, etoposide, gemcitabine, paclitaxel, and vinblastine through tissue in the MCL was slow compared with penetration through the Teflon support membrane alone. CONCLUSIONS: Our results suggest limited ability of anticancer drugs to reach tumor cells that are distant from blood vessels. The limited penetration of anticancer drugs through tumor tissue may be an important cause of clinical resistance of solid tumors to chemotherapy.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".