Endothelial Regulation of Drug Transport in a 3D Vascularized Tumor Model
Bibliographic record
Abstract
Abstract Drug discovery and efficacy in cancer treatments are limited by the inability of pre‐clinical models to predict successful outcomes in humans. Limitations remain partly due to their lack of a physiologic tumor microenvironment (TME), which plays a considerable role in drug delivery and tumor response to therapy. Chemotherapeutics and immunotherapies rely on transport through the vasculature, via the smallest capillaries and stroma to the tumor, where passive and active transport processes are at play. Here, a 3D vascularized tumor on‐chip is used to examine drug delivery in a relevant TME within a large bed of perfusable vasculature. This system demonstrates highly localized pathophysiological effects of two tumor spheroids (Skov3 and A549), which cause significant changes in vessel density and barrier function. Paclitaxel (Taxol) uptake is examined through diffusivity measurements, functional efflux assays, and accumulation of the fluorescent‐conjugated drug within the TME. Due to vascular and stromal contributions, differences in the response of vascularized tumors to Taxol (shrinkage and CD44 expression) are apparent compared with simpler models. This model specifically allows for examination of spatially resolved tumor‐associated endothelial dysfunction, likely improving the representation of in vivo drug distribution, and has potential for development into a more predictable model of drug delivery.
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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) | 0.001 | 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".