Enhanced nanoparticle delivery exploiting tumour-responsive formulations
Bibliographic record
Abstract
Nanoparticles can be used as drug carriers, contrast agents and radiosensitisers for the treatment of cancer. Nanoparticles can either passively accumulate within tumour sites, or be conjugated with targeting ligands to actively enable tumour deposition. With respect to passive accumulation, particles < 150 nm accumulate with higher efficiency within the tumour microenvironment, a consequence of the enhanced permeability and retention effect. Despite these favourable properties, clinical translation of nano-therapeutics is inhibited due to poor in vivo stability, biodistribution and target cell internalisation. Nano-therapeutics can be modified to exploit features of the tumour microenvironment such as elevated hypoxia, increased pH and a compromised extracellular matrix. This is in contrast to cytotoxic chemotherapies which generally do not exploit the characteristic pathological features of the tumour microenvironment, and as such are prone to debilitating systemic toxicities. This review examines strategies for tumour microenvironment targeting to improve nanoparticle delivery, with particular focus on the delivery of nucleic acids and gold nanoparticles. Evidence for key research areas and future technologies are presented and critically evaluated. Among the most promising technologies are the development of next-generation cell penetrating peptides and the incorporation of micro-environment responsive stealth molecules.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".