Numerical modeling of drug delivery in a dynamic solid tumor microvasculature
Bibliographic record
Abstract
The complicated capillary network induced by angiogenesis is one of the main reasons of unsuccessful cancer therapy. A multi-scale mathematical method which simulates drug transport to a solid tumor is used in this study to investigate how capillary network structure affects drug delivery. The mathematical method involves processes such as blood flow through vessels, solute and fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. The effect of heterogeneous dynamic network on interstitial fluid flow and drug delivery is investigated by this multi-scale method. The sprouting angiogenesis model is used for generating capillary network and then fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network and fluid flow in normal and tumor tissues. Finally, convection-diffusion equation is used to simulate drug delivery. Three approaches are used to simulate drug transport based on the developed mathematical method: without a vascular network, using a static vascular network, and a dynamic vascular network. The avascular approach predicts more uniform and higher drug concentration than vascular approaches since the simplified assumptions are implemented in this method. The dynamic network which uses more realistic assumptions predicts more irregular blood vessels, high interstitial pressure, and more heterogeneity in drug distribution than other two approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".