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Record W4295931451 · doi:10.1021/acsapm.2c01220

Mechanistic Influence of Polymer Species, Molecular Weight, and Functionalization on Mucin–Polymer Binding Interactions

2022· article· en· W4295931451 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Polymer Materials · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Federation of University Women
KeywordsMucoadhesionCarboxymethyl cellulosePolymerPolymer chemistryMethacrylateDrug deliveryMaterials scienceSurface modificationHydroxypropyl celluloseChemistryChemical engineeringDrug carrierOrganic chemistryCopolymerNanotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

Improving both systemic and localized delivery of pharmaceuticals by optimizing the delivery vehicle properties is a major area of ongoing research. One such engineered property is mucoadhesion, wherein polymers are tailored to optimize polymer–mucin interactions to increase drug residence times and uptake efficiency. There are many examples of mucoadhesion as a drug delivery modality in the literature, yet the underlying mechanisms remain poorly understood, and its clinical translation is limited. Within this mechanistic lens, we found that nuclear magnetic resonance (NMR) is a powerful tool for uncovering polymer–mucin interactions. We investigated the influence of several polymer design parameters including molecular weight and repeating unit functionalization on mucoadhesion and uncovered several key findings in terms of the influence of these design parameters on polymer–protein intermacromolecular interactions. First, we probed individual polymer species including hydroxypropyl cellulose (HPC), hydroxypropyl methyl cellulose (HPMC), and carboxymethyl cellulose (CMC) and identified that molecular-weight-driven changes in the polymer–mucin interaction fingerprint result from perturbations in the polymer-specific binding complex. Specifically, we found that CMC and HPC complexes spatially reorient with increasing molecular weight, whereas HPMC complexes do not. We expanded this comparison to include functional group changes to the polymer repeating unit, where we showed that the addition of methyl groups to cellulose derivatives induces a unique binding fingerprint. Next, we expanded the mucoadhesive polymer series to include CMC, HPC, HPMC, poly(acrylic acid) (PAA), and poly((2-dimethylamino)ethyl methacrylate) (PDMAEMA) and observed a negative correlation between molecular weight and mucoadhesive interaction intimacy, highlighting the differences across polymer species. Finally, we explored polymers that lack mucin interactions to illustrate the limitations of polymer composition and molecular weight as predictors of mucoadhesive fate. Altogether, these experiments were used to report on mucoadhesive interactions at the atomic level and revealed that polymer design is dependent on both the availability and accessibility of mucoadhesive functional groups.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.087
Threshold uncertainty score1.000

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)0.0060.000

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 head0.042
GPT teacher head0.341
Teacher spread0.299 · 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