Mechanisms of Drug Resistance Related to the Microenvironment of Solid Tumors and Possible Strategies to Inhibit Them
Bibliographic record
Abstract
Drug resistance can occur at the individual cellular level or as a result of properties of the tumor microenvironment. The convoluted vasculature within tumors results in robustly proliferating well-nourished cells located proximal to functional blood vessels and regions of slowly proliferating (often hypoxic) cells located distal to functional blood vessels. Irregular blood flow and large distances between functional blood vessels in solid tumors lead to poor drug distribution within them such that cells distal from functional blood vessels are exposed to ineffective concentrations of drug, resulting in therapeutic resistance. Strategies to improve or complement the distribution of anticancer drugs within tumors hold promise for increasing antitumor effects without corresponding increases in normal tissue toxicity. In particular, use of hypoxia-targeted agents and modulation of autophagy have shown promising results in enhancing the distribution of drug activity within solid tumors and hence antitumor efficacy. In this review, we describe causes of resistance to chemotherapy that relate to the microenvironment of solid tumors and the potential to improve antitumor effects by countering such mechanisms of resistance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".