PEG Conjugated Zein Nanoparticles for In Vivo Use
Bibliographic record
Abstract
Zein can be utilized to form nanoscale particles for drug delivery applications. Despite the ease of synthesis, these particles often aggregate when exposed to physiologically relevant conditions (e.g., pH and salt concentrations). This instability has prevented their further development in applications requiring intravenous administration. To mitigate this colloidal instability, this research explored Zein nanoparticles (NP)s that were modified with polyethylene glycol (PEG) either through functionalized PEG pre- or post-NP formation. The results suggest that the pre-functionalization of the Zein using N-hydroxysuccinimide ester terminated PEG is the method of choice for synthesizing Zein NPs with conjugated PEG (Zein:PEG-Zein NPs). Zein:PEG-Zein NPs formed using this method displayed excellent stability in physiologically relevant conditions over 72 h and were stable at 4 °C for at least 3 months. When the NPs were cultured with cells for 72 h, no cytotoxicity or early signs of apoptosis were identified. Cellular uptake of the Zein:PEG-Zein NPs did not seem to be impacted by the amount of PEG incorporated in the NP but were concentration-, time-, and temperature-dependent. The lowest percent, stable Zein:PEG-Zein NP formulation (80% unmodified Zein and 20% PEG-modified Zein) induced no observable toxicity over 14 days in CD-1 mice dosed at 70 mg/kg via the tail vein. However, repeat dose pharmacokinetic (PK) studies demonstrated that following the first dose, the second dose caused health issues that required euthanasia shortly after administration. For those animals that survived, there was faster plasma elimination of the Zein:PEG-Zein NPs. Despite this, the Zein:PEG-Zein NPs represent a significantly improved formulation approach, one that displays a long circulation half-life and is suitable for single-use administration. Repeat dose applications will require additional methods to silence the immune response that is generated when using these NPs intravenously.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".