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Telodendrimers for Physical Encapsulation and Covalent Linking of Individual or Combined Therapeutics

2017· article· en· W2615756908 on OpenAlexafffund
Jason Choi, Alexandre Moquin, Enzo Bomal, Na Li, Dušica Maysinger, Ashok Kakkar

Bibliographic record

VenueMolecular Pharmaceutics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsEncapsulation (networking)ChemistryCovalent bondNanotechnologyComputational biologyBiophysicsComputer scienceMaterials scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

New therapeutics for glioblastoma multiforme and our ability to deliver them using efficient nanocarriers constitute topical areas of research. We report a comparative study of temozolomide and quercetin in the treatment of glioblastoma (GBM) in three-dimensions, and their incorporation into micelles obtained from synthetically articulated architectural copolymers, and a commercially available linear polymer poly(ethylene glycol)-poly(lactic-co-glycolic acid) (PEG-PLGA). A versatile synthetic methodology to telodendrimers, which can be easily adapted to the needs of other therapeutic interventions, is presented. These dendritic block copolymers self-assemble into micelles and offer a platform for single or combination drug therapy. Telodendrimer micelles loaded with quercetin did not exhibit superior cell killing effect over the free drug, but acetazolamide, an inhibitor carbonic anhydrase IX, significantly reduced GBM cell viability in 3D spheroids. Results from these studies show that high loading of drugs into telodendrimer micelles requires a physical fit between the biologically active agent and telodendrimer nanocarrier, and points toward new possibilities for incorporation of chemotherapeutic and other agents to enhance their effectiveness.

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 score0.011
Threshold uncertainty score0.541

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 head0.077
GPT teacher head0.375
Teacher spread0.298 · 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

Citations23
Published2017
Admission routes2
Has abstractyes

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