Synergistic Nanoparticulate Drug Combination Overcomes Multidrug Resistance, Increases Efficacy, and Reduces Cardiotoxicity in a Nonimmunocompromised Breast Tumor Model
Why this work is in the frame
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Bibliographic record
Abstract
Anthracyclines, commonly employed for cancer chemotherapy, suffer from dose-limiting cardiotoxicity and poor efficacy due to multidrug resistance (MDR). We previously demonstrated that simultaneous delivery of the synergistic drugs doxorubicin (DOX) and mitomycin C (MMC) by polymer-lipid hybrid nanoparticles (PLN) circumvented MDR, increased efficacy, and reduced cardiotoxicity in immuncompromised mice superior to poly(ethylene glycol)-coated (PEGylated) lipososmal DOX (PLD). Herein it is shown that the DOX-MMC combination was also synergistic in MDR EMT6/AR1 murine breast cancer cells and that their nanoparticle formulations were able to overcome the MDR phenotype. In contrast PLD exhibited little or no effect on the MDR cells. For the first time, these differences in in vitro efficacy are shown to be strongly correlated with cellular uptake and intracellular distribution of DOX brought about by DOX formulations (e.g., free solution, PLN vs PLD). To take into consideration the role of an intact immune system and tumor stroma in the response of host and tumor to chemotherapy, use was made of nonimmunocomprised mouse models to study the dose tolerance, cardiotoxicity, and efficacy of DOX-MMC coloaded PLN (DMsPLN) compared to PLD. DMsPLN treatment at 50 mg/m(2) DOX and 17 mg/m(2) of MMC singly or once every 4 days for 4 cycles were well tolerated by the mice without elevated systemic toxicity blood markers or myocardial damage. In contrast, PLD was limited to a single treatment due to significant total weight loss. The DMsPLN treatment delayed tumor growth up to 312% and 28% in EMT6/WT and EMT6/AR1 models, respectively. This work supports the translational value of DMsPLN for the aggressive management of either naïve or anthracycline-resistant tumors.
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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 | 0.000 |
| 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 it