One-Step Synthesis of Nanoliposomal Copper Diethyldithiocarbamate and Its Assessment for Cancer Therapy
Bibliographic record
Abstract
The metal complex copper diethyldithiocarbamate (CuET) induces cancer cell death by inhibiting protein degradation and induces proteotoxic stress, making CuET a promising cancer therapeutic. However, no clinical formulation of CuET exists to date as the drug is insoluble in water and exhibits poor bioavailability. To develop a scalable formulation, nanoliposomal (LP) CuET was synthesized using ethanol injection as a facile one-step method that is suitable for large-scale manufacturing. The nanoparticles are monodispersed, colloidally stable, and approximately 100 nm in diameter with an encapsulation efficiency of over 80%. LP-CuET demonstrates excellent stability in plasma, minimal size change, and little drug release after six-month storage at various temperatures. Additionally, melanoma cell lines exhibit significant sensitivity to LP-CuET and cellular uptake occurs predominantly through endocytosis in YUMM 1.7 cancer cells. Intracellular drug delivery is mediated by vesicle acidification with more nanoparticles being internalized by melanoma cells compared with RAW 264.7 macrophages. Additionally, the nanoparticles preferentially accumulate in YUMM 1.7 tumors where they induce cancer cell death in vivo. The development and characterization of a stable and scalable CuET formulation illustrated in this study fulfils the requirements needed for a potent clinical grade formulation.
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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) | 0.000 | 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) | 0.001 | 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 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".