Penetration of anticancer drugs through tumour tissue as a function of cellular packing density and interstitial fluid pressure and its modification by bortezomib
Bibliographic record
Abstract
BACKGROUND: Limited penetration of anticancer drugs in solid tumours is a probable cause of drug resistance. Our previous results indicate that drug penetration depends on cellular packing density and adhesion between cancer cells. METHODS: We used epithelioid and round cell variants of the HCT-8 human colon carcinoma cell lines to generate tightly and loosely packed xenografts in nude mice. We measured packing density and interstitial fluid pressure (IFP) and studied the penetration of anti-cancer drugs through multilayered cell cultures (MCC) derived from epithelioid HCT-8 variants, and the distribution of doxorubicin in xenografts with and without pre-treatment with bortezomib. RESULTS: We show lower packing density in xenografts established from round cell than epithelioid cell lines, with lower IFP in xenografts. There was better distribution of doxorubicin in xenografts grown from round cell variants, consistent with previous data in MCC. Bortezomib pre-treatment reduced cellular packing density, improved penetration, and enhanced cytotoxcity of several anticancer drugs in MCC derived from epithelioid cell lines. Pre-treatment of xenografts with bortezomib enhanced the distribution of doxorubicin within them. CONCLUSIONS: Our results provide a rationale for further investigation of agents that enhance the distribution of chemotherapeutic drugs in combination with conventional chemotherapy in solid tumours.
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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".