Gold–Protein Composite Nanoparticles for Enhanced X-ray Interactions: A Potential Formulation for Triggered Release
Bibliographic record
Abstract
Drug-delivery vehicles have been used extensively to modulate the biodistribution of drugs for the purpose of maximizing their therapeutic effects while minimizing systemic toxicity. The release characteristics of the vehicle must be balanced with its encapsulation properties to achieve optimal delivery of the drug. An alternative approach is to design a delivery vehicle that preferentially releases its contents under specific endogenous (e.g., tissue pH) or exogenous (e.g., applied temperature) stimuli. In the present manuscript, we report on a novel delivery system with potential for triggered release using external beam radiation. Our group evaluated Zein protein as the basis for the delivery vehicle and used radiation as the exogenous stimulus. Proteins are known to react with free radicals, produced during irradiation in aqueous suspensions, leading to aggregation, fragmentation, amino acid modification, and proteolytic susceptibility. Additionally, we incorporated gold particles into the Zein protein matrix to create hybrid Zein-gold nanoparticles (ZAuNPs). Zein-only nanoparticles (ZNPs) and ZAuNPs were subsequently exposed to kVp radiation (single dose ranging from 2 to 80 Gy; fractionated doses of 2 Gy delivered 10 times) and characterized before and after irradiation. Our data indicated that the presence of gold particles within Zein particles was correlated with significantly higher levels of alterations to the protein, and was associated with higher rates of release of the encapsulated drug compound, Irinotecan. The aggregate results demonstrated a proof-of-principle that radiation can be used with gold nanoparticles to modulate the release rates of protein-based drug-delivery vehicles, such as ZNPs.
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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".