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Record W4415047617 · doi:10.1021/acs.biomac.5c00955

Unimer Exchange as a Tool for Programming Enzymatic Degradation through Micellar Dynamics

2025· article· en· W4415047617 on OpenAlexaff
Shahar Tevet, Michael Brodsky, Roey J. Amir

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsNovelis (Canada)
FundersFacultad de Ciencias Exactas, Universidad Nacional de La PlataIsrael Science Foundation
KeywordsMicelleAmphiphileDegradation (telecommunications)NanocarriersKineticsEnzyme catalysisFörster resonance energy transferCopolymer

Abstract

fetched live from OpenAlex

A key challenge in designing enzyme-responsive micellar nanocarriers lies in balancing their stability and enzymatic degradation. While it has been widely assumed that the micelle-unimer exchange governs enzyme accessibility to the hydrophobic blocks, this relationship had not been directly demonstrated. Here, to uncover this long-assumed mechanistic link, we synthesized a set of triblock amphiphiles that convert by an in situ transition to diblock amphiphiles via reductive cleavage of a central disulfide bond. In parallel, hydrophobicity was independently tuned by modifying the aliphatic end-groups. Enzymatic degradation studies and Förster resonance energy transfer (FRET)-based exchange assays showed two consistent trends across all systems: increasing hydrophobicity led to slower micelle-unimer exchange and reduced enzymatic degradation rates, while transition to diblock consistently enhanced both. These results provide direct evidence that exchange kinetics govern enzymatic degradation and lay the mechanistic foundation for overcoming the stability-degradability barrier for enzyme-responsive micelles by applying architectural transitions as a molecular programming tool.

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 score0.472
Threshold uncertainty score0.762

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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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