Furan-functionalized co-polymers for targeted drug delivery: caracterization, self-assembly and drug encapsulation
Bibliographic record
Abstract
We have previously reported furan-maleimide Diels-Alder chemistry as a new methodology to couple maleimide-modified antibodies on furan-functionalized polymeric carriers in the preparation of immuno-nanoparticles for targeted drug delivery. In this report, we focus on the characterization, self-assembly behavior and drug encapsulation of two types of furan-functionalized co-polymers: poly(2-methyl, 2-carboxytrimethylene carbonate-co-D,L-lactide)-furan (poly(TMCC-co-LA)-furan) and poly(2-methyl, 2-carboxytrimethylene carbonate-co-D,L-lactide)-graft-poly(ethylene glycol)-furan (poly(TMCC-co-LA)-g-PEG-furan). The co-polymers were synthesized by modifying the carboxylic acid groups on the poly(TMCC-co-LA) backbone by either furfurylamine or PEG-furan to generate either linear co-polymers of poly(TMCC-co-LA)-furan with furan pendant groups or graft co-polymers of poly(TMCC-co-LA)-g-PEG-furan with furan-terminated PEG grafts, respectively. Using a membrane dialysis method, both of the co-polymers were self-assembled into nanoparticles in aqueous environments driven by the hydrophobic association among polymer chains. The hydrophobic domains in the nanoparticles were confirmed by the incorporation of pyrene molecules and the critical aggregation concentrations were determined to be approximately 5 x 10(-5) mM for poly(TMCC-co-LA)-furan and 2 x 10(-4) mM for poly(TMCC-co-LA)-g-PEG-furan. By the addition of borate buffer in the organic solvent used to dissolve the co-polymers in the dialysis procedure, we were able to control the size of the nanoparticles: 54-169 nm for poly(TMCC-co-LA)-furan and 28-283 nm for poly(TMCC-co-LA)-g-PEG-furan. This unique feature can be explained by the ionization of carboxylic acid groups along the co-polymer backbone. A hydrophobic anticancer drug, doxorubicin (DOX), was encapsulated within the nanoparticles, with the larger size nanoparticles incorporating greater amounts of DOX. Combining the strategy of antibody-mediated targeting, these self-assembled nanoparticles have potential as efficient anti-cancer drug carriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".