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Record W1891032060 · doi:10.1039/c5cc04282h

Innovative use of the taxol binding peptide overcomes key challenges of stable and high drug loading in polymeric nanomicelles

2015· article· en· W1891032060 on OpenAlexafffund
Jennifer Logie, Christopher K. McLaughlin, Roger Y. Tam, Molly S. Shoichet

Bibliographic record

VenueChemical Communications · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsDrugKey (lock)PeptideCombinatorial chemistryChemistryNanotechnologyBiophysicsComputer scienceMaterials sciencePharmacologyBiochemistryMedicineBiologyComputer security

Abstract

fetched live from OpenAlex

Despite widespread clinical use, delivery of taxane chemotherapeutics remains a challenge due to poor solubility and lack of selectively. Polymeric nanomicelle strategies have been pursued to overcome these issues; however current formulations are often limited by low drug loading and poor serum stability. To achieve a drug delivery system that addresses these issues, poly(D,L-lactide-co-2-methyl-2-carboxytrimethylene carbonate)-g-poly(ethylene glycol) was covalently modified with the taxol binding peptide – a peptide from the β-tubulin-taxane binding site. This modification resulted in drug loadings five times higher than unmodified polymers, which is significantly higher than typical hydrophobic modifications, including with benzyl and docetaxel functionalization. Unlike many formulations with high drug loading, these nanomicelles were stable in serum for up to 24 h and maintained docetaxel cytotoxicity. By incorporating the taxane binding peptide into the polymer chemistry, a new twist was applied to an old problem, which is broadly applicable to other polymeric micelle systems and drug-peptide combinations in general.

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 score0.006
Threshold uncertainty score0.304

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 science0.0010.001
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 head0.069
GPT teacher head0.267
Teacher spread0.198 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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