Route of Administration of Functionalized Gold Nanoparticles Improves Tumor Accumulation In Vivo
Bibliographic record
Abstract
Despite promising achievements of gold nanoparticles (GNPs) as either drug delivery vehicles or radiosensitizing agents in vitro, their clinical application remains limited. A major consideration for their translation to the clinic is optimizing their accumulation in malignant tissue while limiting their sequestration and toxicity in healthy organs. Achieving optimal tumor accumulation in vivo of GNPs requires extensive consideration of their functionalization and route of administration. Surface modifications with integrin binding domain RGD and polyethylene glycol (PEG) are both common practices to improve the blood circulation of GNPs and preferential tumor accumulation. However, the route of administration and functionalization strategy could significantly affect their biodistribution due to the presence of phagocytic elements of the host that recognize the RGD motif. In our study, we systematically evaluate the biodistribution of GNPs after intravenous (i.v.) or intratumoral (i.t.) injections and surface modification with PEG and RGD. After i.v. injections, RGD surface-functionalized GNPs had poor blood circulation time and demonstrated a 91% reduction in tumor accumulation relative to PEGylation alone, indicating significant recognition of the RGD motif by phagocytosing elements. In contrast, i.t. injections of RGD-functionalized GNPs showed increased tumor retention compared to PEGylation alone and reduced GNP accumulation in phagocytosing organs compared to i.v. injections. Our results highlight the importance of optimizing targeting moieties of GNPs when administered through either i.v. or i.t. routes and warrant further investigations into alternative surface ligands to improve their delivery and retention in tumors.
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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 | 0.001 | 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 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".