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Liquid Degradable Poly(trimethylene-carbonate-<i>co</i>-5-hydroxy-trimethylene carbonate): An Injectable Drug Delivery Vehicle for Acid-Sensitive Drugs

2020· article· en· W3006448706 on OpenAlexafffund
Sara Mohajeri, Fei Chen, Mitchell de Prinse, Ta Phung, Jonah Burke‐Kleinman, Donald H. Maurice, Brian G. Amsden

Bibliographic record

VenueMolecular Pharmaceutics · 2020
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsKingston General HospitalQueen's University
FundersCanadian Institutes of Health Research
KeywordsTrimethylene carbonateCarbonateChemistryCopolymerDegradation (telecommunications)PolymerGlycerolPolymer chemistryOrganic chemistryNuclear chemistry

Abstract

fetched live from OpenAlex

Liquid, injectable hydrophobic polymers have advantages as degradable drug delivery vehicles; however, polymers examined for this purpose to date form acidic degradation products that may damage acid-sensitive drugs. Herein, we report on a new viscous liquid vehicle, poly(trimethylene carbonate-co-5-hydroxy-trimethylene carbonate), which degrades through intramolecular cyclization producing glycerol, carbon dioxide, and water-soluble trimethylene carbonate. Copolymer degradation durations from weeks to months were achieved with the 5-hydroxy-trimethylene carbonate (HTMC) content of the oligomer having the greatest impact on the degradation rate, with oligomers possessing a higher HTMC content degrading fastest. The degradation products were non-cytotoxic towards 3T3 fibroblasts and RAW 264.7 macrophages. These copolymers can be injected manually through standard gauge needles and, importantly, during in vitro degradation, the microenvironmental pH within the oligomers remained near neutral. Complete and sustained release of the acid-sensitive protein vascular endothelial growth factor was achieved, with the protein remaining highly bioactive throughout the release period. These copolymers represent a promising formulation for local and sustained release of acid sensitive drugs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.035
GPT teacher head0.282
Teacher spread0.247 · 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.

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
Published2020
Admission routes2
Has abstractyes

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