Limited penetration of anticancer drugs to cells in solid tumors: A neglected and modifiable cause of drug resistance
Bibliographic record
Abstract
9563 Background: Regardless of the drug sensitivity of tumor cells, treatment of solid tumors will only be effective if drugs delivered in the blood penetrate tissue to achieve adequate concentration in the tumor microenvironment. Here we report studies of the penetration of anticancer drugs through tumor tissue in a model system and in solid tumors of mice. Methods: Tumor cells were grown on collagen-coated porous teflon membranes to form multicellular layers (MCL) up to 200μm thick, that resemble tissue in solid tumors. We studied the time-dependent penetration through MCL of several anticancer drugs. Immunohistochemistry was used to study distribution of fluorescent doxorubicin in murine tumors and human tumor xenografts in relation to blood vessels (stained with anti CD-31) and hypoxic regions (identified by uptake of EF-5). Results: Penetration of drugs through MCL is slow compared with that through the teflon membrane, especially for the weak bases doxorubicin and mitoxantrone (<10% penetration relative to the teflon membrane alone). Weak bases are sequestered in acidic endosomes, and strategies to raise endosomal pH leads to less drug uptake into cells without loss of cytotoxicity and improved penetration of tissue. Modification of extracellular matrix also influences penetration in MCL. Doxorubicin has very poor penetration in solid tumors of mice, with a decrease in mean concentration to ∼50% of that in blood over about 3 cell layers. The concentration of doxorubicin remains at background in large areas of viable tumor tissue following a single i.v. injection into mice. Conclusions: Limited distribution of drugs from blood vessels in solid tumors is an important and neglected cause of drug resistance. Several strategies might be used to improve drug penetration and hence therapeutic index. Supported by a grant from CIHR. No significant financial relationships to disclose.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".