Improving accessibility of EPR-insensitive tumor phenotypes using EPR-adaptive strategies: Designing a new perspective in nanomedicine delivery
Bibliographic record
Abstract
The enhanced permeability and retention (EPR) effect has underlain the predominant nanomedicine design philosophy for the past three decades. However, growing evidence suggests that it is over-represented in preclinical models, and agents designed solely using its principle of passive accumulation can only be applied to a narrow subset of clinical tumors. For this reason, strategies that can improve upon the EPR effect to facilitate nanomedicine delivery to otherwise non-responsive tumors are required for broad clinical translation. EPR-adaptive nanomedicine delivery comprises a class of chemical and physical techniques that modify tumor accessibility in an effort to increase agent delivery and therapeutic effect. In the present review, we overview the primary benefits and limitations of radiation, ultrasound, hyperthermia, and photodynamic therapy as physical strategies for EPR-adaptive delivery to EPR-insensitive tumor phenotypes, and we reflect upon changes in the preclinical research pathway that should be implemented in order to optimally validate and develop these delivery strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".