A Self‐Assembly Combined Nano‐Prodrug to Overcome Gemcitabine Chemo‐Resistance of Pancreatic Tumors
Bibliographic record
Abstract
Abstract Gemcitabine (GEM), as a first‐line chemotherapeutics for pancreatic ductal adenocarcinoma (PDAC) treatment, still faces several clinical challenges, restricted by instability in blood circulations, low tumor selectivity, and acquired nature characteristics of chemo‐resistance. To solve these challenges, the rational design of combination therapy with GEM and other therapy modalities is imperative. Herein, a small molecular self‐assembly nano‐prodrug is developed, which can achieve the co‐delivery of GEM, Ferrocene and nutlin‐3a on the achievement of GEM‐induced apoptosis with ferroptosis. In this nano‐prodrug, the disulfide linkage not only acts as a GSH‐responsive trigger but also plays an important role in self‐assembly behavior of nanoparticle that can load nutlin‐3a. Interestingly, nutlin‐3a plays an important role in both ferroptosis and apoptosis, one is effectively sensitized cells to ferroptosis by inhibiting cystine uptake, and the other is promoted apoptosis by elevating p53 expression. To further enhance the drug tumor accumulation and maintain stability in systemic circulations, this nano‐prodrug is then encapsulated into plectin1 receptor‐targeting phospholipid micelles (DSPE‐PEG‐PTP), which displays high selective tumor inhibition and good biosafety on different mice models, especially in orthotopic and patient‐derived xenograft (PDX) models. The findings provide new insights into the combination therapy of GEM with ferroptosis for reduced chemo‐resistance on PDAC treatment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".