Hybrid Polyester Self-Immolative Polymer Nanoparticles for Controlled Drug Release
Bibliographic record
Abstract
Abstract Delivery systems have been developed to address problematic properties of drugs, but the specific release of drugs at their targets is still a challenge. Polymers that depolymerize end-to-end in response to the cleavage of stimuli-responsive end-caps from their termini, commonly referred to as self-immolative polymers, offer high sensitivity to stimuli and have potential for the development of new high-performance delivery systems. In this work, we prepared hybrid particles composed of varying ratios of self-immolative poly(ethyl glyoxylate) (PEtG) and slowly degrading poly(d,l-lactic acid) (PLA). These systems were designed to provide a dual release mechanism consisting of a rapid burst release of drug from the PEtG domains and a slower release from the PLA domains. Using end-caps responsive to UV light and reducing thiols, it was found that triggered particles exhibited partial degradation, as indicated by a reduction in their dynamic light-scattering count rate that depended on the PEtG:PLA ratio. The particles were also shown to release the hydrophobic dye Nile red and the drug celecoxib in a manner that depended on triggering and the PEtG:PLA ratio. In vitro toxicity assays showed an effect of the stimuli on the toxicity of the celecoxib-loaded particles but also suggested it would be ideal to replace the sodium cholate surfactant that was used in the particle synthesis procedure in order to reduce the background toxicity of the delivery system. Overall, these hybrid systems show promise for tuning and controlling the release of drugs in response to stimuli.
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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) | 0.001 | 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".