Donepezil/Curcumin-Loaded Nanoparticles-Embedded Microneedles for the Treatment of Alzheimer’s Disease: In Vitro Evaluation
Notice bibliographique
Résumé
Alzheimer’s disease (AD) is an insidious neurodegenerative disease that slowly deteriorates and seriously impairs the activity of daily living (ADL) of those who suffer from. Although the precise pathophysiology of AD hasn’t been identified yet; however, it is believed that the accumulation of intracellular neurofibrillary tangles and extracellular amyloid beta plaques are the biomarkers manifesting the occurrence of AD that are assumably linked with excessive neuroinflammation and oxidative stress [1]. The current treatment strategy is symptomatic relief with acetylcholinesterase inhibitors, primarily donepezil (DPZ), based on the cholinergic hypothesis. DPZ significantly advances the life quality of Alzheimer’s patients; however, its antisymptomatic effect should be empowered with another agent possessing preventive capacity [2]. Curcumin (Cur), Curcuma longa, has the ability to intervene and attenuate AD’s prognosis by suppressing neuroinflammation and, therefore, cease its evolution at Mild cognitive impairment (MCI) known as prodromal AD [3]. Orally administered DPZ as well as Cur withstand bold limitations affecting both patient compliance and treatment effectiveness [4]. As a consequence, a novel administration route is necessitated. While the transdermal application has been vastly considered, the bypassing of the rate-limiting layer, stratum corneum (SC), is mandatory. Microneedle arrays (MNs), set on a miniaturized patch, create micron size canals across the SC and enable direct drug passage into the systemic circulation extended beneath. Among numerous materials feasible for MNs fabrication, bio-compatible and -degradable polymers are sought due to their outstanding superiorities [5]. Hence, in this project, we combined the anti-Alzheimer effect of DPZ and Cur by producing DPZ/Cur loaded nanoparticles (NPs) made of muco-adhesive chitosan/tripolyphosphate (CS/TPP) via ionic gelation that were subsequently coated with polysorbate 80 to maximize transmembrane penetration. The NP optimization process was carried out based on CS:TPP mass and solution volume ratio plus acetic acid and polysorbate 80 volume percent; the optimized sample was chosen according to its zeta potential, mean particle size, and polydispersity index measured with Zetasizer Nano 2S which were 37.1 ± 0.56 mV, 42.63 ± 0.24 nm, and 0.344 ± 0.01, respectively. Produced NPs were then embedded in swellable MNs made of polyvinylpyrrolidone/polyvinyl alcohol (PVP/PVA). MN fabrication was accomplished with a novel device named pressurized gyration (PG) which is a first in the literature. In this step, production parameters, rotation duration, and speed, as well as ambient conditions, temperature, and humidity, were optimized to obtain MNs with ideal mechanical strength and penetration degree determined by a mechanical tester performed in compression mode. The morphological features of the biomaterials were investigated with scanning electron microscopy. Chemical, crystallo-graphical, and thermal analyses were concluded with Fourier-transform infrared spectroscopy, X-Ray powder diffraction, and differential scanning calorimetry, respectively. Encapsulation efficiency % and loading capacity % were measured as 70.12 and 34.78% for DPZ, 69.57 and 53.2% for Cur, respectively. Furthermore, drug release analysis demonstrated that both drugs were released based on Higuchi kinetic model via non-Fickian diffusion mechanism. Additionally, NP’s degradation and MN’s swelling patterns were thoroughly investigated. Finally, the bio-safety and -efficacy of synthesized materials were assessed on Aβ1−42-induced SH-SY5Y in vitro Alzheimer models.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».