Development of Gold Nanoparticle-based Nanoformulations for Cancer Radiotherapy
Bibliographic record
Abstract
One of the major clinical challenges in radiotherapy (RT) is dose-limiting toxicity to surrounding normal tissues. Gold nanoparticles (AuNPs) have emerged as a promising approach to overcoming this challenge by combining the radiation dose enhancement effects of Au with the unique features of nanoparticles that enable tumour-selective delivery of AuNPs. Theoretical and experimental studies have demonstrated that AuNPs have the potential to improve radiotherapeutic efficacy. These improvements are the result of physical enhancement of the local radiation dose, and/or sensitization of cells via biological and chemical pathways. To further enhance the radiosensitization effects of AuNPs, this thesis aims to design and develop AuNP-based nano-formulations that (1) combine AuNPs with an agent with anti-cancer properties (pentamidine or cisplatin), or (2) target the nucleus for physical dose enhancement, and evaluate their in vitro radiation enhancement effects. Overall, this work demonstrates the promising potential of the newly developed nano-formulations to improve the radiation enhancement effects of AuNPs. The combination of AuNPs and pentamidine demonstrated enhanced radiosensitization effects relative to AuNPs alone by promoting cellular uptake and via inhibition of post-IR DNA repair. As well, cisplatin prodrug-conjugated AuNPs combined the AuNP-induced production of reactive oxygen species with persistent DNA damage imparted by cisplatin to result in superior radiosensitization in 2D monolayers and growth inhibition in 3D multicellular tumour spheroids. Finally, nuclear-targeted Au-liposomes formed by encapsulation of AuNPs in pH-sensitive liposomes showed significant radiation enhancement effects whereas no significant effects were observed with untargeted Au-liposome, demonstrating the role of nuclear targeting in physical dose enhancement. Based on these findings, future research is warranted to evaluate the in vivo efficacy and safety of these nano-formulations, and to optimize the nuclear-targeted Au-liposomes to fully elucidate the impact of nuclear localization on physical dose enhancement.
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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".