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Record W7132956291

Structural analysis of PEGylated nanoemulsions using EPR spectroscopy – the impact of an active compound incorporated in stabilizing layer

2023· article· en· W7132956291 on OpenAlexaboutno aff
Jelena Đoković, Sotiria Demisli, Vassiliki Papadimitriou, Snežana Savić

Bibliographic record

VenueFarFar - Pharmacy Repository · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHomogenizerBioavailabilityDrug deliveryPolyvinyl alcoholPhospholipidAqueous solutionAqueous two-phase systemPEG ratio
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Nanoemulsions (NEs) offer a flexible platform for drug delivery via several administration routes. Rapid plasma clearance brought on by interactions with plasma proteins and the activation of the mononuclear phagocytic system is the greatest challenge NEs face after parenteral administration. PEGylation, or adding PEGylated phospholipids to the stabilizing layer of the NEs, is one method for ensuring that droplets circulate for a longer period of time. It is crucial to select the optimum concentration of the PEGylated in order to maintain the necessary physicochemical properties of NEs while providing appropriate surface coverage with the PEG chains. Curcumin is a model active that has been found to be localized in the stabilizing layer of NEs (1-3) and offers a wide range of potential health benefits, but due to its short plasma half-life, new strategies for enhancing bioavailability are required. The purpose of this study is to investigate the effects of various PEGylated phospholipid (PEG2000-DSPE) concentrations on the structural properties of NEs with an active placed in the stabilizing layer. PREPARATION OF NANOEMULSIONS All the NEs were prepared using the high pressure homogenization technique. The aqueous phase (glycerol, polysorbate 80, sodium oleate and highly purified water) was added to the oil phase (soybean oil, medium chain triglycerides, soybean lecithin, buthylhydroxytoluene, curcumin and 0.1%/0.3%/0.6% PEG2000-DSPE) and mixed at 11000rpm for 1 min on rotor stator homogenizer (IKA Ultra-Turrax T25 digital, IKA-Werke GmbH & Co. KG, Staufen, Germany), and then further processed at 800 bar for 10 discontinued cycles (EmulsiFlex-C3, Avestin Inc., Ottawa,ON, Canada) to obtain CS21, CS23 and CS26 formulations. NANOEMULSION DROPLET SIZE The droplet size was assessed through the dynamic light scattering method and presented as mean droplet size (Z-ave) and polydispersity index (PDI), after diluting the NEs 1:500 (v/v) in highly purified water. ELECTRON PARAMAGNETIC RESONANCE (EPR) SPECTROSCOPY For this study three amphiphilic fatty acid derivatives labeled at different positions of the aliphatic chain (5-DSA, 12-DSA and 16-DSA) were used to probe the dynamics of the membrane at different depths. Stock solutions of the spin probes were prepared in absolute ethanol at 1mM concentration. Subsequently, 15 µl of the stock solutions were evaporated and then incubated with 1 ml of the NE sample in final concentration of 0.015 mM. The resulting spectra was analyzed in terms of rotational correlation time (τR), order parameter (S) and isotropic hyperfine constant (αN). RESULTS AND DISCUSION All of the formulations had average droplet sizes between 95 and 103 nm and PDI values under 0.25, which indicated that they were suitable for parenteral administration. The results of the EPR investigation showed that the stabilizing layer changed as the amount of PEGylated phospholipids increased, indicating that the PEGylation threshold has not yet been reached in the stabilizing layer. The EPR research also showed that the 5-DSA spin probe's spectra were significantly affected by the addition of various PEGylated phospholipid concentrations (Figure 1). This indicates that the portion of the stabilizing layer nearest to the aqueous phase was the one most affected by the increase in the PEGylated phospholipid concentration. Table 1 provides the calculated values for the spectrum parameters. The mobility of the spin-probe and the time it takes for the spin-probe to make a full rotation is reflected in the τR parameter, which was changed the most, compared to the other parameters, by the variations in the PEGylated phospholipid content. A formulation with the most rigid stabilizing layer had the largest τR values, which, in this instance, was the formulation with 0.1% PEG2000-DSPE. It's interesting to note that the addition of PEGylated phospholipid had the opposite effect of strengthening the stabilizing layer. Further PEG2000-DSPE addition appeared to result in nanoemulsions with a less rigid stabilizing layer, possibly indicating that larger concentrations of the PEGylating agent lead to interface destabilization. The interactions between the curcumin, a symmetrical molecule with two aromatic ring systems and a bent conformation located in the stabilizing layer and the extra stabilizer are likely responsible for this. The other two spin probes (12-DSA and 16-DSA) provide information about the stabilizing layer located closer to the oil core. Based on the data provided in Table 1 it can be inferred that the PEGylation mostly affects the stabilizing layer's areas closest to the aqueous interface, leaving the parts closer to the oil core largely not impacted. CONCLUSION This study demonstrates that one of the key elements in assessing how PEGylation affects the NE system is the active's localization. To pick the concentration of the PEGylated phospholipid that will offer the best surface coverage without compromising the integrity of the interface, additional considerations must be addressed in the event of an active situated in the stabilizing layer. In this instance, it may be hypothesized that the lowest PEG2000-DSPE concentration of 0.1%, CS21, will produce NEs that can slow down curcumin release the most compared to the other two formulations. Additionally, given that further addition of the PEGylated phospholipid causes the formation of less rigid stabilizing layer, further inquiries should be made to see the impact of these changes on the interactions with plasma proteins and biological fate of the droplets upon administration.

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.372
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.097
GPT teacher head0.368
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations0
Published2023
Admission routes1
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