Pharmacokinetic and toxicological evaluation of multi-functional thiol-6-fluoro-6-deoxy-d-glucose gold nanoparticles<i>in vivo</i>
Bibliographic record
Abstract
We synthesized a novel, multi-functional, radiosensitizing agent by covalently linking 6-fluoro-6-deoxy-D-glucose (6-FDG) to gold nanoparticles (6-FDG-GNPs) via a thiol functional group. We then assessed the bio-distribution and pharmacokinetic properties of 6-FDG-GNPs in vivo using a murine model. At 2 h, following intravenous injection of 6-FDG-GNPs into the murine model, approximately 30% of the 6-FDG-GNPs were distributed to three major organs: the liver, the spleen and the kidney. PEGylation of the 6-FDG-GNPs was found to significantly improve the bio-distribution of 6-FDG-GNPs by avoiding unintentional uptake into these organs, while simultaneously doubling the cellular uptake of GNPs in implanted breast MCF-7 adenocarcinoma. When combined with radiation, PEG-6-FDG-GNPs were found to increase the apoptosis of the MCF-7 breast adenocarinoma cells by radiation both in vitro and in vivo. Pharmacokinetic data indicate that GNPs reach their maximal concentrations at a time window of two to four hours post-injection, during which optimal radiation efficiency can be achieved. PEG-6-FDG-GNPs are thus novel nanoparticles that preferentially accumulate in targeted cancer cells where they act as potent radiosensitizing agents. Future research will aim to substitute the (18)F atom into the 6-FDG molecule so that the PEG-6-FDG-GNPs can also function as radiotracers for use in positron emission tomography scanning to aid cancer diagnosis and image guided radiation therapy planning.
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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".