Overview of Methods for Overcoming Hindrance to Drug Delivery to Tumors, with Special Attention to Tumor Interstitial Fluid
Why this work is in the frame
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Bibliographic record
Abstract
Every drug used to treat cancer (chemotherapeutics, immunological, monoclonal antibodies, nanoparticles, radionuclides) must reach the targeted cells through the tumor environment at adequate concentrations, in order to exert their cell-killing effects. For any of these agents to reach the goal cells, they must overcome a number of impediments created by the tumor microenvironment (TME), beginning with tumor interstitial fluid pressure (TIFP), and a multifactorial increase in composition of the extracellular matrix (ECM). A primary modifier of TME is hypoxia, which increases the production of growth factors, such as vascular endothelial growth factor and platelet-derived growth factor. These growth factors released by both tumor cells and bone marrow recruited myeloid cells form abnormal vasculature characterized by vessels that are tortuous and more permeable. Increased leakiness combined with increased inflammatory byproducts accumulates fluid within the tumor mass (tumor interstitial fluid), ultimately creating an increased pressure (TIFP). Fibroblasts are also up-regulated by the TME, and deposit fibers that further augment the density of the ECM, thus, further worsening the TIFP. Increased TIFP with the ECM are the major obstacles to adequate drug delivery. By decreasing TIFP and ECM density, we can expect an associated rise in drug concentration within the tumor itself. In this overview, we will describe all the methods (drugs, nutraceuticals, and physical methods of treatment) able to lower TIFP and to modify ECM used for increasing drug concentration within the tumor tissue.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it