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Record W2739063252

EFFECT OF PEG COATING ON NANOPARTICLE DIFFUSION THROUGH TUMOUR EXTRACELLULAR MATRIX

2015· article· en· W2739063252 on OpenAlexaffvenue
Jacob Kennard, Kaisha Terefe, Wayne Ngo, Hagar I. Labouta, Trinh Nguyen, Christopher D. Sarsons, David T. Cramb, Kristina D. Rinker

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLiposomePEG ratioBiophysicsEthylene glycolNanoparticleChemistryChemical engineeringDrug deliveryVesicleBilayerLipid bilayerAqueous solutionMaterials scienceChromatographyNanotechnologyMembraneBiochemistryOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Nanoparticle drug delivery systems have the potential to improve current cancer treatments through encapsulating cytotoxic agents and delivering them to specific sites in the body. One such class of particle, liposomes, has already found some commercial success [1]. Liposomes are vesicles composed of a lipid bi-layer surrounding an aqueous solution. Poly(ethylene) glycol (PEG) surface coating is commonly used to improve the hydrophilicity of liposomes, thereby increasing their stability in aqueous solutions. Furthermore, PEG limits the binding of blood antigens, which minimizes opsonisation and phagocytosis, extending circulation time in the blood stream. When applied to the surface of liposomes at lower molecular weights and surface densities, PEG adopts a “mushroom” conformation, in which adjacent chains of PEG do not interact laterally, therefore portions of the bi-layer remain exposed [2]. However, at higher molecular weights and surface densities, the “brush” conformation is adopted; where lateral interactions occur between neighbouring PEG strands and provide complete coverage of the lipid bi-layer [2]. This study will investigate the effect of varying PEG molecular weight and surface density on liposome transport through tumour extracellular matrix. METHODS Seven different formulations of liposomes were synthesized using a modification of the lipid extrusion method described in [1]. Molecular weight and surface density values were chosen to include both PEG conformations. The Type I collagen hydrogel was prepared with a collagen concentration of 2.5mg/mL. Confocal Microscopy was used to track the liposome transport into the gels via the bilayer incorporated Rhodamine dye. While simple collagen hydrogels may not capture all of the complexity of native tumour ECM, they allow for more carefully controlled conditions than in vivo models. Images were taken every 30 minutes until the 900 minute mark. RESULTS As shown in Figure 1, the liposomes with a lower PEG loading (DOPC, 5, 10% PEG 1000, 5, 10% PEG 2000), all accumulated at the interface of the hydrogel, and had identical diffusion coefficients. The 5% and 10% PEG 5000 however, accumulated significantly less and therefore had a much greater diffusion coefficient. DISCUSSION AND CONCLUSIONS The liposomes with low PEG surface density, and DOPC control liposomes shown in Figure 1, are all within the “mushroom” conformation of PEG [2] and therefore would all have exposed bilayer which is not shielded by the PEG strands. The formulations that penetrated deeply were notably only higher PEG surface densities (5 and 10% PEG 5000) which literature suggests would have been in the “brush” conformation [2]. This suggests that the high PEG surface densities sterically shielded the liposomes, and reduced the electrostatic interactions between the hydrogels and the liposomes, allowing increased diffusion.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.054
GPT teacher head0.365
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes2
Has abstractyes

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