Nanomedicine in Pancreatic Cancer: Current Status and Future Opportunities for Overcoming Therapy Resistance
Bibliographic record
Abstract
The development of drug resistance remains one of the greatest clinical oncology challenges that can radically dampen the prospect of achieving complete and durable tumour control. Efforts to mitigate drug resistance are therefore of utmost importance, and nanotechnology is rapidly emerging for its potential to overcome such issues. Studies have showcased the ability of nanomedicines to bypass drug efflux pumps, counteract immune suppression, serve as radioenhancers, correct metabolic disturbances and elicit numerous other effects that collectively alleviate various mechanisms of tumour resistance. Much of this progress can be attributed to the remarkable benefits that nanoparticles offer as drug delivery vehicles, such as improvements in pharmacokinetics, protection against degradation and spatiotemporally controlled release kinetics. These attributes provide scope for precision targeting of drugs to tumours that can enhance sensitivity to treatment and have formed the basis for the successful clinical translation of multiple nanoformulations to date. In this review, we focus on the longstanding reputation of pancreatic cancer as one of the most difficult-to-treat malignancies where resistance plays a dominant role in therapy failure. We outline the mechanisms that contribute to the treatment-refractory nature of these tumours, and how they may be effectively addressed by harnessing the unique capabilities of nanomedicines. Moreover, we include a brief perspective on the likely future direction of nanotechnology in pancreatic cancer, discussing how efforts to develop multidrug formulations will guide the field further towards a therapeutic solution for these highly intractable tumours.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".