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Enregistrement W7011881735

Novel Formulation of PGLA nanoparticles for targeted glioblastoma chemotherapy

2022· article· en· W7011881735 sur OpenAlexaboutno aff

Notice bibliographique

RevueSunderland Repository (University of Sunderland) · 2022
Typearticle
Langueen
DomaineMaterials Science
ThématiqueNanoparticle-Based Drug Delivery
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPimozideGlioblastomaDrug deliveryPLGATargeted drug deliveryDrugTransferrin receptorPaclitaxelChemotherapyTemozolomide
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Novel Formulation of PGLA nanoparticles for targeted glioblastoma chemotherapy Md Nazim Uddin1; Amal Elkordy2; Ahmed Faheem3 School of Pharmacy and Pharmaceutical Sciences, University of Sunderland, Sunderland, United Kingdom 1nazim.uddin@sunderland.ac.uk; 2amal.elkordy@sunderland.ac.uk; 3ahmed.faheem@sunderland.ac.uk INTRODUCTION Poly-lactide-co-glycolide (PLGA) is a biodegradable polymer that has been approved by U.S Food Drug Administration (FDA) and European Medicines Agency (EMA) for therapeutic use in human. Furthermore, it is being extensively studied as nanoparticulate drug delivery system. The most attractive feature of PLGA nanoparticles is that they could be tuned into functionalized nanoparticles having desired physicochemical properties. In particular, active targeting can be made possible with PLGA nanoparticle by conjugating targeting ligand within the PLGA. Pimozide is a first-generation antipsychotic drug used in schizophrenia and Tourette syndrome, and other psychotic disorder. Interestingly, it was reported as anticancer agent in several studies. However, no study has developed pimozide formulation aiming for cancer therapy. Glioblastoma is a deadly brain cancer that is not completely treatable with current clinical interventions. Studies suggest that antipsychotic agent pimozide inhabits glioblastoma cells. However, using free-pimozide for glioblastoma chemotherapy only would not be possible as it would induce severe side effects, along with its antipsychotic effect. Targeted delivery of pimozide by PLGA nanoparticles could be a potential therapeutic option for glioblastoma. However, blood-brain barrier (BBB) poses a threat to nanoparticles. In this case, PLGA nanoparticles functionalised with transferrin (TF) could selectively target transferrin receptor (TFR) proteins that are expressed by BBB lining capillary endothelial cells. PLGA-TF complex would be then transported across the BBB by clathrin-mediated endocytosis. Interestingly, glioblastoma cells overexpress TFR receptors, which would be again selectively targeted by PLGA-TF nanoparticles. Earlier, we developed PLGA-PEG nanoparticles with tuned physicochemical properties, such as sub 100 nm particle size and over 70% drug encapsulation efficiency. This study aims to develop targeted nanoparticles (PLGA-PEG-TF) and evaluate them on glioblastoma cell lines. OBJECTIVES The objectives of this study are- • To functionalise PLGA-PEG nanoparticles with transferrin. • To confirm the expression of TFR proteins on glioblastoma cell lines. • To evaluate the cytotoxicity of PGLA-PEG-TF nanoparticles on glioblastoma cell line. MATERIALS AND METHODS Nanoparticles were prepared by a staggered herringbone micromixing method, using a benchtop NanoAssemblr® (Precision NanoSystems™, Canada). Particle size and charge were analysed by dynamic light scattering (DLS), using Zetasizer ZSP instrument (Malvern Panalytical, UK). Drug encapsulation efficiency (EE) was analysed by a validated ultra-high-performance liquid chromatography (UHPLC). The morphology of the nanoparticles was analysed by transmission electron microscopy (TEM). Adsorbed transferrin (TF) on the nanoparticles was quantified by an indirect method using micro-BCA protein assay. For in vitro evaluation, patient-derived glioblastoma cell lines, namely E2, G7, R24, and GLG were cultured on advanced Dulbecco’s modified Eagle medium (DMEM). Transferrin receptors (TFR) expression on these four cell lines was investigated by Western blot analysis. Finally, the cytotoxicity of targeted nanoparticles was evaluated on the cell line that mostly expressed TFR, performing cell proliferation assay using a live-cell analysis system IncuCyte® ZOOM (Essen BioScience, UK). RESULTS AND DISCUSSIONS Increased particle size (observed by DLS) confirmed TF adsorption on PLGA (Table 1). Further, TEM and BCA protein assay confirmed the transferrin adsorption (data not shown). Western blot analysis showed that two types of TFR receptors (TFR1 and TFR1) were expressed on three cell lines out of four tested in this study (Figure 1). It can be observed that TFR1 was expressed most on E2 cell line. Accordingly, cell proliferation assay of E2 cell line showed that PLGA-PEG-TF nanoparticles effectively inhibited the growth of glioblastoma cells (Figure 2). This finding was further supported by the phase contrast images that were taken after treatment of nanoparticles (Figure 3). NPs Particle size (nm) Zeta potential (mV) Encapsulation efficiency %(EE) PLGA-PEG 61±1 -18 ± 2 73±4 PLGA-PEG-TF 74±6 -10 ± 2 47±3 Table 1. Physicochemical characterisation of nanoparticles Figure 1: Western blot analysis showing TFR expression on glioblastoma cell lines. Figure 2: Cytotoxicity of targeted nanoparticles (PLGA-PEG-TF) on glioblastoma cell line E2. Figure 3: Phase contrast images of glioblastoma cells at day 3 after treated with targeted and non-targeted PLGA nanoparticles. CONCLUSIONS This study aimed to target glioblastoma cells with pimozide-encapsulated PLGA-PEG-TF nanoparticles that would, when treated, selectively bind with TFR on the cell surface, and get internalized by clathrin-mediated endocytosis. Thus, pimozide would only kill glioblastoma cells if delivered with targeted nanoparticles. Our results, with sub 100 nm particle size even after TF adsorption, ~50% drug encapsulation efficiency, positive TFR expression on glioblastoma cells, and effective inhibition of glioblastoma cell growth support the hypothesis. However, in vivo studies are required for its development into nanomedicines. REFERENCES 1. Bidkar, A. P., Sanpui, P., & Ghosh, S. S. (2020). Transferrin-Conjugated Red Blood Cell Membrane-Coated Poly(lactic-co-glycolic acid) Nanoparticles for the Delivery of Doxorubicin and Methylene Blue. ACS Applied Nano Materials, 3(4), 3807–3819. https://doi.org/10.1021/acsanm.0c00502 2. Calzolari, A., Larocca, L. M., Deaglio, S., Finisguerra, V., Boe, A., Raggi, C., Ricci-Vitani, L., Pierconti, F., Malavasi, F., De Maria, R., Testa, U., & Pallini, R. (2010). Transferrin Receptor 2 Is Frequently and Highly Expressed in Glioblastomas. Translational Oncology, 3(2), 123–134. https://doi.org/10.1593/tlo.09274 3. Chang, J., Jallouli, Y., Kroubi, M., Yuan, X., Feng, W., Kang, C., Pu, P., & Betbeder, D. (2009). Characterization of endocytosis of transferrin-coated PLGA nanoparticles by the blood–brain barrier. International Journal of Pharmaceutics, 379(2), 285–292. https://doi.org/10.1016/j.ijpharm.2009.04.035 4. Lee, J. K., Nam, D.-H., & Lee, J. (2016). Repurposing antipsychotics as glioblastoma therapeutics: Potentials and challenges (Review). Oncology Letters, 11(2), 1281–1286. 5. Rath, B. H., Camphausen, K., & Tofilon, P. J. (2016). Glioblastoma radiosensitization by pimozide. Translational Cancer Research, 5(6), S1029–S1032. https://doi.org/10.21037/10584 6. Svenja, Z., N, M., M, M., K, A.-E.-A., F, R., Sjl, van W., D, K., & S, F. (2018, September 24). Loperamide, Pimozide, and STF-62247 Trigger Autophagy-Dependent Cell Death in Glioblastoma Cells. Cell Death & Disease; Cell Death Dis. https://doi.org/10.1038/s41419-018-1003-1.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,201
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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