A nanomedicine approach to targeting cuproplasia in cancer: Labile copper sequestration using polydopamine nanoparticles blocks tumor growth in vivo through altering metabolism and redox homeostasis.
Bibliographic record
Abstract
Copper plays critical roles as a metal active site cofactor and metalloallosteric signal for enzymes involved in cell proliferation and metabolism, making it an attractive target for cancer therapy. In this study, we investigated the efficacy of polydopamine nanoparticles (PDA NPs), classically applied for metal removal from water, as a therapeutic strategy for depleting intracellular labile copper pools in triple negative breast cancer models through the metal-chelating groups present on PDA surface. By using the activity-based sensing probe FCP-1, we could track the PDA-induced labile copper depletion while leaving total copper levels unchanged, and link it to the selective MDA-MB-231 cell death. Further mechanistic investigations revealed that PDA NPs increased reactive oxygen species (ROS) levels, potentially through the inactivation of superoxide dismutase 1 (SOD1), a copper-dependent antioxidant enzyme. Additionally, PDA NPs were found to interact with the mitochondrial membrane, resulting in an increase in mitochondrial membrane potential, which may contribute to enhanced ROS production. We employed an in vivo tumor model to validate the therapeutic efficacy of PDA NPs. Remarkably, in the absence of any additional treatment, the presence of PDA NPs alone led to a significant reduction in tumor volume by a factor of 1.66 after 22 days of tumor growth. Our findings highlight the potential of PDA NPs as a promising therapeutic approach for selectively targeting cancer by modulating copper levels and inducing oxidative stress, leading to tumor growth inhibition as shown in these triple negative breast cancer models.
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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 | 0.001 | 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 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".