Size-tunable nanoparticles composed of dextran-b-poly(D,L-lactide) for drug delivery applications
Bibliographic record
Abstract
Nanoparticles (NPs) formulated using self-assembly of block copolymers have attracted significant attention as nano-scaled drug delivery vehicles. Here we report the development of a biodegradable NP using self-assembly of a linear amphiphilic block copolymer, Dex-b-PLA, composed of poly(D,L-lactide), and dextran. The size of the NPs can be precisely tuned between 15 and 70 nm by altering the molecular weight (M W) of the two polymer chains. Using doxorubicin as a model drug, we demonstrated that the NPs can carry up to 21% (w/w) of the drug payload. The release profile of doxorubicin from NPs showed sustained release for over 6 days. Using a rat model, we explored the pharmacokinetics profiles of Dex-b-PLA NPs, and showed proof-of-concept that long circulation lifetime of the NPs can be achieved by tuning the M W of Dex-b-PLA block copolymer. While the terminal half-life of Dex-b-PLA NPs (29.8 h) was similar to that observed in poly(ethylene glycol)-coated (PEG-coated) NPs (27.0 h), 90% of the injected Dex-b-PLA NPs were retained in the blood circulation for 38.3 h after injection, almost eight times longer than the PEG-coated NPs. The area under curve (AUC) of Dex-b-PLA NPs was almost four times higher than PEG-based NPs. The biodistribution study showed lower accumulation of Dex-b-PLA NPs in the spleen with 19.5% initial dose per gram tissue (IDGT) after 24 h compared to PEG-coated poly(lactide-co-glycolide) (PLGA) NPs (29.8% IDGT). These studies show that Dex-b-PLA block copolymer is a promising new biomaterial for making controlled nanoparticles as drug delivery vehicles.
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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) | 0.000 | 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) | 0.000 | 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".