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Record W7133016376

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

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

Bibliographic record

VenueFarFar - Pharmacy Repository · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHomogenizerBiocompatibilityDrug deliveryCompoundingDosage formAqueous two-phase systemCritical quality attributes
DOInot available

Abstract

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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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.509
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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