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A proposal of innovative injectability assessment method for intravenous formulations - case study on PEGylated nanoemulsions

2022· article· en· W7133016376 sur OpenAlexaboutno aff
Jelena Đoković, Sanela Savić, Nebojša Cekić, Snežana Savić

Notice bibliographique

RevueFarFar - Pharmacy Repository · 2022
Typearticle
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiqueAdvanced Drug Delivery Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHomogenizerBiocompatibilityDrug deliveryCompoundingDosage formAqueous two-phase systemCritical quality attributes
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

1. INTRODUCTION Syringeability and injectability are recognised as fundamental performance parameters / critical quality attributes of any parenteral dosage form. Syringeability refers to the ability of an injectable preparation to transfer from a vial through a hypodermic needle prior an injection, while injectability is defined as the force, or pressure, required to inject the formulation from a syringe-needle system into the tissue [1]. When developing drug delivery systems, the priority is usually the release kinetics, biocompatibility or other factors that may come in conflict with the optimal parameters for the applicability of those systems [2]. The aim of this research was to develop a method that could be used for injectability assessment of the intravenous formulations and the application of this method on curcumin-loaded PEGylated nanoemulsions (NEs) in order to gage the impact of PEGylation on NEs injectability. 2. MATERIALS AND METHODS 2.1. Nanoemulsion preparation Nanoemulsions were prepared using high pressure homogenization method. The aqueous phase (glycerol, polysorbate 80, sodium oleate and highly purified water) was added into the oil phase (soybean oil, soybean lecithin, medium chain triglycerides, butylhydroxytoluene, benzyl alcohol, curcumin and PEGylated phospholipid – PEG2000-DSPE in 0.1 %, 0.3 % or 0.6 % concentrations) and mixed using rotor-stator homogenizer (IKA Ultra-Turrax® T25 digital, IKA®-Werke GmbH and Co. KG, Staufen, Germany), and further processed on high pressure homogenizer (EmulsiFlex-C3, Avestin Inc., Canada) at 800 bar for 10 discontinued cycles. The non PEGylated formulation was marked as CS, and the PEGylated ones were marked S1, S3 and S6, referring to the PEG2000-DSPE concentration. 2.2. Physicochemical characterization The NEs droplet size (Z-Ave) and droplet size distribution (PDI) were determined with Zetasizer Nano ZS90 (Malvern Instruments Ltd., Worcestershire). Rheological analysis was performed using MCR 302 air-bearing rheometer (Anton Paar, Graz, Austria) equipped with coaxial cylinders system (CC27 measuring bob with C-PTD 180/Air) with sheer rate range of 0.1-100 s-1 at 20°C. 2.3. Injectabilty assesment The injectability of the NEs was expressed as force (N) needed to extrude the NE in the function of the extruded volume (ml). About 10 ml of the NE was loaded into the 10 ml syringe and extruded through the 25 G scalp vein infusion set (Romed, Wilnis, Netherlands) into the blood mimicking solution, circulating through pump at 4 ml/min, in order to assess the NEs’ performance in the prospective intravenous administration. The NEs were extruded at 1 mm/s croshead speed of the loading cell of the texure analyzer (EZ-LX Compact Table-Top Testing Machine, Shimadzu, Japan) with the TrapeziumX software version 1.5 used for data collection and analysis 3. RESULTS AND DISCUSSION 3.1. Physicochemical characterization The NEs have average size of about 100 nm, with the PDI values below 0.20, indicating suitability for intravenous application. It could be observed from Fig. 1 that the addition of PEGylated phospholipids caused an increase in NE viscosity, as could be expected given that the polyethylene glycols are used in parenteral suspensions as stabilizing - rheology modifying agents [3]. 3.2. Injectability assessment The injectability assessment was performed with syringe-needle system used in our laboratory for intravenous administration in in vivo animal studies. As blood-mimicking solution, 36.6 %, v/v, glycerol solution was used [4]. It could be observed from Fig. 2 that the injectability of NEs depended on their viscosity, with the higher pressure needed to extrude the formulations with the higher PEG2000-DSPE concentration. Even though, to the best of our knowledge, there are no studies investigating the injectability of the intravenous preparations, based on some previous research on subcutaneous model [5], it is recommended the maximum force used to inject the formulations should be kept about 20 N, which would eliminate S3 and S6 from further investigation (Fig. 2). 4. CONCLUSION The injectability method used in this research proved as a useful tool in screening formulations adequate for prospective intravenous use. 5. REFERENCES 1. Cilurzo, F., et al. Injectability Evaluation: An Open Issue. AAPS PharmSciTech, 2011. 12(2): 604-609. 2. Sarmadi, M., et al. Modeling, design, and machine learning-based framework for optimal injectability of microparticle-based drug formulations. Science advances, 2020. 6: eabb6594. 3. Gullapalli, R. P., Mazzitelli, C. L. Polyethylene glycols in oral and parenteral formulations—A critical review. International Journal of Pharmaceutics, 2015. 496(2): 219-239. 4. Yousif, M. Y., et al.. Deriving a blood-mimicking fluid for particle image velocimetry in Sylgard-184 vascular models. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009 (pp. 1412-1415 5. Watt, R. P., et al. (2019). Injectability as a function of viscosity and dosing materials for subcutaneous administration. International Journal of Pharmaceutics, 2019:554, 376-386. ACKNOWLEDGMENT This research was funded by the MESDT, Republic of Serbia through Grant Agreement with University of Belgrade-Faculty of Pharmacy No: 451-03-68/2022-14/200161 and supported by the Science Fund of the Republic of Serbia, GRANT No 7749108, Neuroimmune aspects of mood, anxiety and cognitive effects of leads/drug candidates acting at GABAA and/or sigma-2 receptors: In vitro/in vivo delineation by nano- and hiPSC-based platform - NanoCellEmоCog.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
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 enseignants0,326
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,131
Tête enseignante GPT0,509
Écart entre enseignants0,378 · 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 tête enseignante, pas un consensus.

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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