Self-Immolative Polymer Nanoparticles with Precise and Controllable pH-Dependent Degradation
Bibliographic record
Abstract
Polymer nanoparticles have generated significant interest as delivery systems for therapeutic cargo. Self-immolative polymers (SIPs) are an interesting category of materials for delivery applications, as the characteristic property of end-to-end depolymerization allows for the disintegration of the delivery system, facilitating a more effective release of the cargo and clearance from the body after use. In this work, nanoparticles based on a pH-responsive polymer poly(ethylene glycol)- b -(2-diisopropyl)amino ethyl methacrylate) and a self-immolative polymer poly[ N, N -(diisopropylamino)ethyl glyoxylamide- r - N, N -(dibutylamino)ethyl glyoxylamide] (P(DPAEGAm- r -DBAEGAm)) were developed. Four particles were synthesized based on P(DPAEGAm- r -DBAEGAm) polymers with varied diisopropylamino to dibutylamino ratios of 4:1, 2:1, 2:3, and 0:1, termed 4:1, 2:1, 2:3, and 0:1 PGAm particles. The pH of particle disassembly was tuned from pH 7.0 to pH 5.0 by adjusting the ratio of diisopropylamino to dibutylamino substituents on the pendant tertiary amine. The P(DPAEGAm- r -DBAEGAm) polymers were observed to depolymerize (60–80%) below the particle disassembly pH after ∼2 h, compared to <10% at pH 7.4 and maintained reasonable stability at pH 7.4 (20–50% depolymerization) after 1 week. While all particles exhibited the ability to load a peptide cargo, only the 4:1 PGAm particles had higher endosomal escape efficiency (∼4%) compared to the 2:3 or 0:1 PGAm particles (<1%). The 4:1 PGAm particle is a promising candidate for further optimization as an intracellular drug delivery system with rapid and precisely controlled degradation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".