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Record W4307630691 · doi:10.3390/pharmaceutics14112300

Synthesis & Evaluation of Novel Mannosylated Neoglycolipids for Liposomal Delivery System Applications

2022· article· en· W4307630691 on OpenAlexafffund
Leila Mousavifar, Jordan D. Lewicky, Alexis Taponard, Rahul S. Bagul, Madleen Rivat, Shuay Abdullayev, Alexandrine L. Martel, Nya L. Fraleigh, Arnaldo Nakamura, Frédéric J. Veyrier, Hoang‐Thanh Le, René Roy

Bibliographic record

VenuePharmaceutics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsNOSM UniversityInstitut National de la Recherche ScientifiqueHealth Sciences NorthUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsLiposomeDelivery systemChemistryPharmaceutical technologyChromatographyBiochemistryPharmacologyBiology

Abstract

fetched live from OpenAlex

Glycosylated NPs, including liposomes, are known to target various receptors involved in cellular carbohydrate transport, of which the mannoside binding receptors are attracting particular attention for their expression on various immune cells, cancers, and cells involved in maintaining central nervous system (CNS) integrity. As part of our interest in NP drug delivery, mannosylated glycoliposomal delivery systems formed from the self-assembly of amphiphilic neoglycolipids were developed, with a C12-alkyl mannopyranoside (ML-C12) being identified as a lead compoundcapable of entrapping, protecting, and improving the delivery of structurally diverse payloads. However, ML-C12 was not without limitations in both the synthesis of the glycolipids, and the physicochemical properties of the resulting glycoliposomes. Herein, the chemical syntheses of a novel series of mannosylated neoglycolipids are reported with the goal of further improving on the previous ML-C12 glyconanoparticles. The current work aimed to use a self-contingent strategy which overcomes previous synthetic limitations to produce neoglycolipids that have one exposed mannose residue, an aromatic scaffold, and two lipid tails with varied alkyl chains. The azido-ending carbohydrates and the carboxylic acid-ending lipid tails were ligated using a new one-pot modified Staudinger chemistry that differed advantageously to previous syntheses. The formation of stable neoglycoliposomes of controllable and ideal sizes (≈100–400 nm) was confirmed via dynamic light scattering (DLS) experiments and transmission electron microscopy (TEM). Beyond chemical advantages, the present study further aimed to establish potential improvements in the biological activity of the neoglycoliposomes. Concanavalin A (Con A) agglutination studies demonstrated efficient and stable cross-linking abilities dependent on the length of the linkers and lipid tails. The efficacy of the glycoliposomes in improving cytosolic uptake was investigated using Nile Red as probe in immune and cancer cell lines. Preliminary ex vivo safety assessments showed that the mannosylated glycoliposomes are hemocompatible, and non-immunogenic. Finally, using a model peptide therapeutic, the relative entrapment capacity and plasma stability of the optimal glycoliposome delivery system was evaluated and compared to the previous neoglycoliposomes. Overall, the new lead glycoliposome showed improved biological activity over ML-C12, in addition to having several chemical benefits including the lack of stereocenters, a longer linker allowing better sugar availability, and ease of synthesis using novel one-pot modified Staudinger chemistry.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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 head0.091
GPT teacher head0.357
Teacher spread0.266 · 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".

Quick stats

Citations13
Published2022
Admission routes2
Has abstractyes

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