The biodistribution and pharmacokinetic evaluation of choline-bound gold nanoparticles in a human prostate tumor xenogra_ model
Bibliographic record
Abstract
PURPOSE: Gold nanoparticles (GNPs) have attracted significant attention in the treatment of cancer due to their potential as novel radiation enhancers, particularly when functionalized with various targeting ligands. The aim of this study was to assess the biodistribution and pharmacokinetic characteristics of a novel choline-bound GNP (choline-GNP) stabilized with polyethelenimine (PEI). METHODS: Choline bound to 27 nm diameter GNPs was characterized using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). Toxicity of choline-GNPs was examined on DU-145 prostate cancer cells using an MTT assay. Using balb/c mice bearing flank DU-145 prostate tumors, choline-GNPs bio-distribution was measured using inductively coupled mass spectroscopy (ICP-MS). Blood, heart, lung, liver, spleen, brain, kidney and tumor gold content were examined at multiple time points over a 24-hour period after tail vein injection. RESULTS: An MTT assay using DU-145 prostate cancer cells yielded a 95% cell viability 72 hours after choline-GNP administration. The tumor GNP area under the concentration-time curve during the first 4 hours (AUC0-4) was 2.2 µg/ml h, representing 13% of the circulating blood GNP concentration over the same time period. The maximum intra-tumor GNP concentration observed was 1.4% of the injected dose per gram of tumor tissue (%ID/g) one hour post injection. CONCLUSIONS: GNPs functionalized with choline demonstrates a viable future nanoparticle platform with increased intra-tumor uptake as compared to unconjugated GNPs. Decreased intra-hepatic accumulation appears to be the reason for the improved systemic bioavailability. The next logical translational investigation will incorporate external beam radiation with the observed maximum intra-tumor uptake.
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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) | 0.000 | 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 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".