Preclinical Evaluation of Stable Integrin αvβ3-Specific [198Au]Gold Nanoparticles for Tumor Therapy
Bibliographic record
Abstract
Objectives: This paper reports the preclinical evaluation of stable tumor-specific gold nanoparticles (AuNPs) activated by neutron irradiation as a therapeutic option for the treatment of cancers characterized by high tumor angiogenesis. Methods: A selection of promising AuNPs with high avidity to αvβ3-expressing glioma (U-87 MG) cells (IC50 = 82–104 nM) were chosen with different surface loading of Arg-Gly-Asp (RGD) peptides as tumor targeting vectors for integrin αvβ3, a target which is overexpressed in tissues displaying high tumor angiogenesis. Three different [198Au]AuNPs were evaluated applying three injection methods, intravenous (i.v.), intraperitoneal (i.p.), and intratumoral (i.t.), each in a group of six U-87 MG xenograft–bearing mice (54 female athymic nude mice in total). Their biodistribution and tumor accumulation was assessed by in vivo imaging within 1–7 days after injection and 7 days after injection by ex vivo measurement. Results: The developed [198Au]AuNPs exhibited suboptimal biodistribution by i.v. application (accumulation pattern tail > liver > spleen, no significant tumor accumulation) and by i.p. application (accumulation pattern spleen >> liver > pancreas, slight tumor accumulation of <0.3 %ID/g). However, an acceptable biodistribution by i.t. application was observed (5.5 %ID/g in liver, 4.9 %ID/g in spleen, and 3.0 %ID/g in tumor). Conclusions: Despite the very promising in vitro results, the in vivo evaluation suggests that the [198Au]AuNPs represent a platform for the development of restricted therapeutic strategies.
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 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 | 0.005 | 0.001 |
| 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".