Abstract 2899: Doxorubicin-mitomycin co-loaded polymer lipid nanoparticle enhanced efficacy against breast tumor and decreased toxicity as compared to Doxil
Bibliographic record
Abstract
Abstract Background: Multidrug resistance acquired by cancer cells and the dose-limiting toxicity of anti-cancer drugs are contributing factors to the failure of cancer chemotherapy. Doxorubicin (Dox) -Mitomycin C (MMC) co-loaded stealth polymer lipid hybrid nanoparticles (DMsPLN) was developed to overcome these problems and demonstrated anti-cancer synergy in vitro. The purpose of the study is to evaluate in vivo efficacy and safety of DMsPLN in a breast cancer model. Method: The efficacy and systemic toxicity of DMsPLN were evaluated against clinically used Doxil in mice bearing murine or human mammary carcinomas. DMsPLN were administered intravenously at 50 mg/m2 doxorubicin single dose or once every 4 days for 4 cycles. Tumor size was measured as function of time to determine therapeutic efficacy of the treatment and systemic toxicity was monitored by repeated measurement of body weight. Results: Significant difference in the efficacy of DMsPLN relative to Doxil was observed in both sensitive and resistant tumor models. A clear enhancement of tumor growth delay (TLD) was evident with both single and 4 times doses of DMs PLN. In single dose and 4x dose DMsPLN groups, the TGD were significantly improved to 100% and 300% in the sensitive tumor model. In the resistant tumor model, the TGD ranged from 30%-130% for the single dose and the 4x dose DMsPLN, respectively. 10% of the mice showed complete tumor disappearance in DMs PLN and 25% complete tumor disappearance was observed in DMs PLN 4x group in the sensitive tumor model. 11% of the mice showed complete tumor disappearance in both DMs PLN and in DMs PLN 4x group in the resistant tumor model. In addition to enhanced efficacy, none of the toxicity associated with Doxil treatment were observed in single or 4x DMsPLN treatment. Conclusions: DMsPLN demonstrated enhanced efficacy and reduced toxicity over Doxil in aggressive mouse models of sensitive and resistant breast cancers. Therefore, DMsPLN may provide clinically relevant, more aggressive anti-cancer interventions. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2899. doi:1538-7445.AM2012-2899
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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) | 0.002 | 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".