Glucose-responsive mesoporous silica nanoparticles to generation of hydrogen peroxide for synergistic cancer starvation and chemistry therapy
Bibliographic record
Abstract
Xiao Du,1 Tian Zhang,1 Guanglan Ma,1 Xiaochen Gu,2 Guangji Wang,3 Juan Li11Department of Pharmaceutics, School of Pharmacy, China Pharmaceutical University, Nanjing, , People’s Republic of China; 2College of Pharmacy, University of Manitoba, Winnipeg, MB, Canada; 3Center for New Drug Safety Evaluation and Research, China Pharmaceutical University, Nanjing, People’s Republic of ChinaBackground: The combination of novel starving therapy with chemotherapy is one of the most promising strategies to achieve an effective antitumor activity.Methods: Herein, we developed a multifunctional mesoporous silica nanoparticle (MSNs-GOx/PLL/HA) coated with poly (L-lysine) (PLL) and hyaluronic acid (HA) for co-delivery of glucose oxidase (GOx) and anticancer drug paclitaxel (PTX) for cancer treatment for the first time. Compared to single chemotherapy, introduction of GOx would not only selectively trigger the consumption of intracellular glucose, leading to the interruption of energy supply, but also elevat the endogenous H2O2 level, inducing stronger therapeutic effects.Results: The novel drug delivery system possessed desirable particle diameter of 40 nm and exhibited a pH-sensitive drug release behavior. An in vitro cellular uptake study indicated that MSNs-GOx/PLL/HA nanoparticles effectively enhanced the cellular uptake of drug in an apparently CD44 receptor-dependent manner, and delivered more cargo into cytoplasm via endolysosomal escape effect in presence of PLL. The nanoplatform has also demonstrated amplified synergistic therapeutic effects for remarkable tumor inhibition in a xenograft animal tumor model.Conclusion: Consequently, the developed synergistic starving-like/chemotherapy may provide a potential platform for next generation cancer therapy.Keywords: combination therapy, glucose oxidase, hyaluronic acid, pH-sensitivity, nanomedicine
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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) | 0.000 | 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 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".