<i>In Situ</i> Forming, Enzyme-Responsive Peptoid-Peptide Hydrogels: An Advanced Long-Acting Injectable Drug Delivery System
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Long-acting drug delivery systems are promising platforms to improve patient adherence to medication by delivering drugs over sustained periods and removing the need for patients to comply with oral regimens. This research paper provides a proof-of-concept for the development of a new optimized in situ forming injectable depot based on a tetrabenzylamine-tetraglycine- d -lysine-O-phospho- d -tyrosine peptoid-D-peptide formulation (( N Phe) 4 GGGGk(AZT)y(p)-OH). The chemical versatility of the peptoid-peptide motif allows low-molecular-weight drugs to be precisely and covalently conjugated. After subcutaneous injection, a hydrogel depot forms from the solubilized peptoid-peptide-drug formulation in response to phosphatase enzymes present within the skin space. This system is able to deliver clinically relevant concentrations of a model drug, the antiretroviral zidovudine (AZT), for 35 days in Sprague–Dawley rats. Oscillatory rheology demonstrated that hydrogel formation began within ∼30 s, an important characteristic of in situ systems for reducing initial drug bursts. Gel formation continued for up to ∼90 min. Small-angle neutron scattering data reveal narrow-radius fibers (∼0.78–1.8 nm) that closely fit formation via a flexible cylinder elliptical model. The inclusion of non-native peptoid monomers and D-variant amino acids confers protease resistance, enabling enhanced biostability to be demonstrated in vitro . Drug release proceeds via hydrolysis of an ester linkage under physiological conditions, releasing the drug in an unmodified form and further reducing the initial drug burst. Subcutaneous administration of ( N Phe) 4 GGGGk(AZT)y(p)-OH to Sprague–Dawley rats resulted in zidovudine blood plasma concentrations within the 90% maximal inhibitory concentration (IC 90 ) range (30–130 ng mL –1 ) for 35 days.
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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".