Liposome‐Polymer Nanoparticles Loaded with Copper Diethyldithiocarbamate and 6‐Bromo‐Indirubin‐3′‐Oxime Enable the Treatment of Refractive Melanoma
Bibliographic record
Abstract
Despite significant advances in cancer immunotherapy, many patients fail to respond to current treatments, outlining the need to develop novel therapeutic modalities. Therapeutic resistance in cancer cells is mediated by significant genomic instability due to their oncogenic transformation and evolutionary pressures inside the tumor microenvironment (TME). However, these cellular and molecular adaptations can result in a significant increase in the baseline endoplasmic reticulum (ER) stress in TME-resident cells. This can be taken advantage of as a therapeutic strategy by using the metal chelate copper diethyldithiocarbamate (CuET), a potent inhibitor of the p97-UFD1-NPL4 protein complex to induce cytotoxicity and exacerbate ER stress in cancer cells. Here, CuET is combined with the anti-inflammatory drug 6-bromo-indirubin-3'-oxime (BIO), a potent GSK3 inhibitor, to modulate the aberrant inflammatory response inside the TME. However, both CuET and BIO are highly hydrophobic and exhibit poor bioavailability, requiring the development of an appropriate carrier. Herein, it is demonstrated that CuET and BIO can be efficiently loaded into liposomes that are stabilized by poly(vinylpyrrolidone). The liposome-loaded drug combination resulted in a significant decrease of 47% and 76% in the tumor burden of syngeneic B16F10 and YUMM1.7 mouse models, respectively, without any major acute toxicity.
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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".