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Record W4415033642 · doi:10.1016/j.jconrel.2025.114306

Polyphosphocholination of liposomic vehicles extends blood circulation, enhances cellular uptake, and lowers immunogenicity relative to PEGylation

2025· article· en· W4415033642 on OpenAlexfundno aff
Monika Kluzek, Weifeng Lin, Evgenia Mitsou, Ziv Porat, Yuri Kuznetsov, Yaara Oppenheimer‐Shaanan, Jacob Klein

Bibliographic record

VenueJournal of Controlled Release · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsnot available
FundersEuropean Research CouncilHorizon 2020 Framework ProgrammeNarodowe Centrum NaukiResearch and DevelopmentIsrael Science FoundationWeizmann Institute of ScienceEgg Farmers of Canada
KeywordsPEGylationImmunogenicityLiposomeDrug deliveryImmune systemDrugDrug carrierTargeted drug delivery

Abstract

fetched live from OpenAlex

Intravenous liposomal drug delivery holds great promise for pharmaceutical efficacy, but faces challenges such as rapid clearance and immune system degradation. PEG-based liposome surface functionalization (PEGylation), currently the gold-standard and most widely-used approach to address these issues, is prone to reduced cellular uptake and accelerated bloodstream clearance (ABC) effect upon repeated administration due to immune activation. We demonstrate a novel liposome surface functionalization using poly(2-methacryloyloxyethyl phosphorylcholine) (pMPC) that significantly overcomes these limitations while maintaining comparable colloidal stability. Such polyphosphocholinated liposomes exhibit tunable cellular uptake, and prolonged blood circulation times, both modulated by polymer length, alongside reduced immunogenicity (lower IgM antibody elicitation) and a diminished ABC effect compared to PEG-liposomes. These polymer-length-dependent properties offer flexibility in optimizing drug delivery systems, positioning pMPCylated liposomes as a compelling alternative to PEGylated formulations with clear advantages for liposomal drug delivery therapeutics.

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 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 score0.045
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.235
Teacher spread0.231 · 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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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