Synergistic Nanoparticulate Drug Combination Overcomes Multidrug Resistance, Increases Efficacy, and Reduces Cardiotoxicity in a Nonimmunocompromised Breast Tumor Model
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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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".