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.
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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) | 0.000 | 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 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".