Network pharmacology and experimental verification reveal the mechanism of safranal against glioblastoma (GBM)

Yang, Xiaobing; Lu, Di; Sun, Yanfei; Wei, Tiandi; Man, Dulegeqi; Chen, Anbin; Luo, Tao; Zhao, Feihu; Liu, Xuemeng; Cheng, Bo; Wang, Xu; Zhao, Peng; Wang, Donghai; Li, Xingang

doi:10.3389/fonc.2023.1255164

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Mitchell and colleagues exploited a patient-derived GBM organoid model to identify the WDR5 gene as a targetable epigenetic regulator of cancer stem cell maintenance [178]. Finally, Yang et al tested the chemical compound safranal in both GBM-brain organoid cocultures and 3D spheroid tumor assays, showing that this agent is effective in inducing GBM cell apoptosis and cell cycle arrest and displays synergetic effects with TMZ [179].…”

Section: Gbm-brain Organotypic Models (Gboms)mentioning

confidence: 99%

Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues

Slika,

Karimov,

Alimonti

et al. 2023

IJMS

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Glioblastoma is the most common malignant primary central nervous system tumor and one of the most debilitating cancers. The prognosis of patients with glioblastoma remains poor, and the management of this tumor, both in its primary and recurrent forms, remains suboptimal. Despite the tremendous efforts that are being put forward by the research community to discover novel efficacious therapeutic agents and modalities, no major paradigm shifts have been established in the field in the last decade. However, this does not mirror the abundance of relevant findings and discoveries made in preclinical glioblastoma research. Hence, developing and utilizing appropriate preclinical models that faithfully recapitulate the characteristics and behavior of human glioblastoma is of utmost importance. Herein, we offer a holistic picture of the evolution of preclinical models of glioblastoma. We further elaborate on the commonly used in vitro and vivo models, delving into their development, favorable characteristics, shortcomings, and areas of potential improvement, which aids researchers in designing future experiments and utilizing the most suitable models. Additionally, this review explores progress in the fields of humanized and immunotolerant mouse models, genetically engineered animal models, 3D in vitro models, and microfluidics and highlights promising avenues for the future of preclinical glioblastoma research.

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Section: Gbm-brain Organotypic Models (Gboms)mentioning

confidence: 99%

Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues

Slika,

Karimov,

Alimonti

et al. 2023

IJMS

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Preparation and Optimization of Gemcitabine Loaded PLGA Nanoparticle Using Box-Behnken Design for Targeting to Brain: In Vitro Characterization, Cytotoxicity and Apoptosis Study

Alik Kumar,

Pattnaik,

Satapathy

et al. 2024

CNM

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Background: Treatment of glioma with conventional approaches remains a far-reaching target to provide the desired outcome. This study aimed to develop and optimize Gemcitabine hydrochloride- loaded PLGA nanoparticles (GNPs) using the Box-Behnken design methodology. The independent variables chosen for this study included the quantity of Polymer (PLGA) (X1), Tween 80 (X2), and Sonication time (X3), whereas the dependent variables were Particle size (Y1) EE % (Y2) and PDI (Y3). The optimized biodegradable nanoparticles are investigated for their anti-cancer effectiveness in U87MG human glioblastoma cells in vitro. Method: The formulation process involved two steps. Initially, emulsification was carried out by combining the organic polymer solution with the aqueous surfactant solution. Subsequently, in the second step, the organic solvent was evaporated, resulting in the precipitation of the polymer and the formation of nanoparticles. The quantity of PLGA, Tween 80, and PVA (at a constant concentration) was adjusted based on the experimental trial approach. Subsequently, the PLGA-based nanoparticles underwent characterization, wherein their particle size, encapsulation efficiency, polydispersity index (PDI), and cumulative release were assessed. The optimal formulation composition was determined as 200 mg of PLGA, 4 ml of Tween 80, and 2 mg of PVA. Further, the optimized GNPs were evaluated for their anti-cancer effectiveness on U87 MG cells by MTT and apoptosis assay. Results: The results demonstrated that the gemcitabine hydrochloride loaded PLGA nanoparticles in the optimal formulation exhibited an encapsulation efficiency of 81.66 %, a particle size of 140.1 nm, and a PDI of 0.37. The morphology of the Opt-GNP-PLGA-NP was observed to be spherical through transmission electron microscopy (TEM). Conclusion: The Apoptosis study further confirmed the observations of MTT assay as the Opt- GNP-PLGA-NP significantly enhanced the apoptosis in U-87 MG cells than the Standard marketed formulation. other: The in vitro cell line study was performed to ensure brain targeting

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Network pharmacology and experimental verification reveal the mechanism of safranal against glioblastoma (GBM)

Cited by 2 publications

References 41 publications

Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues

Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues

Preparation and Optimization of Gemcitabine Loaded PLGA Nanoparticle Using Box-Behnken Design for Targeting to Brain: In Vitro Characterization, Cytotoxicity and Apoptosis Study

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