PEG-Free Polyion Complex Nanocarriers for Brain-Derived Neurotrophic Factor
Bibliographic record
Abstract
Many therapeutic formulations incorporate poly(ethylene glycol) (PEG) as a stealth component to minimize early clearance. However, PEG is immunogenic and susceptible to accelerated clearance after multiple administrations. Here, we present two novel reformulations of a polyion complex (PIC), originally composed of poly(ethylene glycol)113-b-poly(glutamic acid)50 (PEG-PLE) and brain-derived neurotrophic factor (BDNF), termed Nano-BDNF (Nano-BDNF PEG-PLE). We replace the PEG based block copolymer with two new polymers, poly(sarcosine)127-b-poly(glutamic acid)50 (PSR-PLE) and poly(methyl-2-oxazolines)38-b-poly(oxazolepropanoic acid)27-b-poly(methyl-2-oxazoline)38 (PMeOx-PPaOx-PMeOx), which are driven to association with BDNF via electrostatic interactions and hydrogen bonding to form a PIC. Formulation using a microfluidic mixer yields small and narrowly disperse nanoparticles which associate following similar principles. Additionally, we demonstrate that encapsulation does not inhibit access by the receptor kinase, which affects BDNF’s physiologic benefits. Finally, we investigate the formation of nascent nanoparticles through a series of characterization experiments and isothermal titration experiments which show the effects of pH in the context of particle self-assembly. Our findings indicate that thoughtful reformulation of PEG based, therapeutic PICs with non-PEG alternatives can be accomplished without compromising the self-assembly of the PIC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".