Liposomal delivery of a disulfiram metabolite drives copper-mediated tumor immunity
Bibliographic record
Abstract
Disulfiram, traditionally used as an alcohol-aversion drug, has demonstrated anticancer properties attributed to its metabolism into diethyldithiocarbamate (DDC), a potent copper-binding agent. Previously, we developed a liposomal formulation of copper diethyldithiocarbamate (Cu(DDC) 2 ) by incorporating DDC into copper-containing liposomes. In this study, we present an improved formulation achieved by directly mixing DDC and copper in a suspension of empty liposomes, leveraging DDC’s copper ionophore-like activity. This method minimizes Cu(DDC) 2 precipitation outside the liposomes and enables efficient drug loading at defined molar ratios. The formulation effectively slowed the growth of subcutaneous MDA-MB-231 tumors in mice and showed increased efficacy in 4T1 tumors in immunocompetent mice compared to immunocompromised counterparts. In vitro , Cu(DDC) 2 -treated cancer cells exhibited upregulation of damage-associated molecular patterns (DAMPs), including ATP release, HMGB1 secretion, and calreticulin exposure. Transcriptomic analysis revealed increased expression of immune activation and copper transport genes, further supporting the potential for immunogenic cell death (ICD) induction. In a prophylactic tumor vaccination model, inoculation with Cu(DDC) 2 -treated CT26 cells delayed tumor growth and conferred protection in a subset of animals, indicating the induction of an adaptive immune response consistent with ICD. These findings align with previous reports of disulfiram-induced ICD and provide functional validation linking this activity to DDC’s role as a copper ionophore. This formulation offers a robust and scalable platform for exploring the role of copper as an immune modulator and lays the groundwork for future optimization toward clinical application.
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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".