Dual-targeted hybrid nanoparticles of synergistic drugs for treating lung metastases of triple negative breast cancer in mice
Bibliographic record
Abstract
Lung metastasis is the major cause of death in patients with triple negative breast cancer (TNBC), an aggressive subtype of breast cancer with no effective therapy at present. It has been proposed that dual-targeted therapy, ie, targeting chemotherapeutic agents to both tumor vasculature and cancer cells, may offer some advantages. The present work was aimed to develop a dual-targeted synergistic drug combination nanomedicine for the treatment of lung metastases of TNBC. Thus, Arg-Gly-Asp peptide (RGD)-conjugated, doxorubicin (DOX) and mitomycin C (MMC) co-loaded polymer-lipid hybrid nanoparticles (RGD-DMPLN) were prepared and characterized. The synergism between DOX and MMC and the effect of RGD-DMPLN on cell morphology and cell viability were evaluated in human MDA-MB-231 cells in vitro. The optimal RGD density on nanoparticles (NPs) was identified based on the biodistribution and tumor accumulation of the NPs in a murine lung metastatic model of MDA-MB-231 cells. The microscopic distribution of RGD-conjugated NPs in lung metastases was examined using confocal microscopy. The anticancer efficacy of RGD-DMPLN was investigated in the lung metastatic model. A synergistic ratio of DOX and MMC was found in the MDA-MB-231 human TNBC cells. RGD-DMPLN induced morphological changes and enhanced cytotoxicity in vitro. NPs with a median RGD density showed the highest accumulation in lung metastases by targeting both tumor vasculature and cancer cells. Compared to free drugs, RGD-DMPLN exhibited significantly low toxicity to the host, liver and heart. Compared to non-targeted DMPLN or free drugs, administration of RGD-DMPLN (10 mg/kg, iv) resulted in a 4.7-fold and 31-fold reduction in the burden of lung metastases measured by bioluminescence imaging, a 2.4-fold and 4.0-fold reduction in the lung metastasis area index, and a 35% and 57% longer median survival time, respectively. Dual-targeted RGD-DMPLN, with optimal RGD density, significantly inhibited the progression of lung metastasis and extended host survival.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".