PEGylated liposomal Gemcitabine: insights into a potential breast cancer therapeutic
Bibliographic record
Abstract
PURPOSE: Nanoencapsulation of chemotherapeutics is an established method to target breast tumors and has been shown to enhance the efficacy of therapy in various animal models. During the past two decades, the nucleoside analog Gemcitabine has been under investigation to treat both recalcitrant and localized breast cancer, often in combination with other chemotherapeutics. In this study, we investigated the chemotherapeutic efficacy of a novel Gemcitabine-encapsulated liposome previously formulated by our group, GemPo, on both sensitive (4T1) and recalcitrant (MDA-MB-231) breast cancer cell lines. METHODS: Gemcitabine free drug and liposomal Gemcitabine were compared both in vitro and in vivo using breast cancer models. RESULTS: We demonstrated that GemPo differently hindered the growth, survival and migration of breast cancer cells, according to their drug sensitivities. Specifically, whereas GemPo was a more potent cytotoxic and apoptotic agent in sensitive breast cancer cells, it more potently inhibited cell migration in the resistant cell line. However, GemPo still acted as a more potent inhibitor of migration, in comparison with free Gemcitabine, irrespective of cell sensitivity. Administration of GemPo in a 4T1-bearing mouse model inhibited tumor growth while increasing mice survival, as compared with free Gemcitabine and a vehicle control. Interestingly, the inclusion of a mitotic inhibitor, Paclitaxel, synergized only with free Gemcitabine in this model, yet was as effective as GemPo alone. However, inclusion of Paclitaxel with GemPo significantly improved mouse survival. CONCLUSIONS: Our study is the first to demonstrate the pleiotropic effects of Gemcitabine and Gemcitabine-loaded nanoparticles in breast cancer, and opens the door for a novel treatment for breast cancer patients.
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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 | 0.001 |
| 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".