Atomically-precise Au22(Lys-Cys-Lys)16 nanoclusters for radiation sensitization
Bibliographic record
Abstract
Abstract Radiotherapy is a leading method for cancer treatment, effectively eliminating cancer cells but often causing collateral damage to surrounding healthy tissue. Radiosensitizers aim to enhance the therapeutic effects of radiotherapy while minimizing harm to normal cells. We recently reported atomically-precise gold nanoclusters, Au 22 (Lys-Cys-Lys) 16 , synthesized via a photochemical method coupled with a novel accelerated size-focusing procedure. These nanoclusters exhibit a distinct luminescence emission profile, reflecting exceptional optical purity and the absence of contamination from other nanocluster species. They demonstrate efficient oxygen radicals generation under light irradiation. In this study, we comprehensively evaluated the radiosensitization potential of Au 22 (Lys–Cys–Lys) 16 nanoclusters in vitro and in vivo, alongside their pharmacokinetics, biodistribution and toxicity. The nanoclusters demonstrated high stability under physiological conditions and efficient internalization in tumor cells, achieving dose enhancement factors of 2.0 and 1.6 in KB and 4T1 tumor cells, respectively, under 225 kVp X-ray irradiation. Mechanistic investigations revealed enhanced radiation-induced DNA damage and disruption of DNA repair pathways. The radiosensitizing effects were further validated in radioresistant pancreatic ductal adenocarcinoma cells using the clonogenic assay and γH2AX analysis of double-strand breaks, as well as in a duck chorioallantoic membrane model. With ultra small size (~ 1.7 nm) and favorable surface framework, the nanoclusters exhibited relevant pharmacokinetics (circulation half-life, t₁ / ₂ = 10.4 h) and renal clearance. In a KB tumor-bearing mouse model, Au 22 (Lys–Cys–Lys) 16 significantly delayed tumor progression and prolonged survival under 8 Gy irradiation without observed side-effects. These findings establish Au 22 (Lys–Cys–Lys) 16 nanoclusters as a potentially translatable radiosensitizer, advancing cancer radiotherapy strategies.
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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) | 0.000 | 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 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".