Implantable Depots for Interstitial Delivery of Radiolabeled Gold Nanoparticles using a Brachytherapy Technique
Bibliographic record
Abstract
Radiolabeled gold nanoparticles (AuNP) are emerging as a class of therapeutics for cancer due to their unique physical and chemical properties that differentiate them from other small or bulk materials, as well as other nanoscale particles. In the ideal condition their use as a radiotherapeutic agent is facilitated by preferential extravasation from the tumour vasculature and accumulation in the interstitial space as a result of the enhanced permeability and retention (EPR) effect, following systemic delivery. This delivery scenario was anticipated to reduce the accumulation of radiolabeled AuNP in normal tissues, thereby increasing the therapeutic ratio of tumour control to normal tissue complications. However, systemic delivery of AuNP has been challenged with the inability to escape phagocytic clearance by the reticuloendothelial system (RES) coupled with suboptimal accumulation of AuNP in the tumour. Furthermore, attempts at direct intratumoural delivery of radiolabeled AuNP to circumvent RES capture and increase tumour concentrations have encountered their own limitations such as clinical feasibility and unpredictable intratumoural radioactivity and dose distribution patterns. In contrast brachytherapy, the oldest form of internal radiotherapy, has well established methods to precisely deliver radioactive material. The use of radiolabeled AuNP in brachytherapy can be mutually beneficial, offering new opportunities for brachytherapy such as use of non-photon emitting radionuclides, potential for adjuvant therapies, and dose homogenization from AuNP redistribution in the tumour interstitial space. In this thesis, a delivery system for radiolabeled AuNP is developed by designing an implantable nanoparticle depot (NPD) compatible with traditional permanent brachytherapy techniques. The design of the NPD is validated in its ability to facilitate controlled release of AuNP, resulting in predictable AuNP distributions in breast cancer xenografts in vivo. Furthermore, micro-SPECT/CT (single-photon emission computed tomography/computed tomography) image based dosimetry techniques are applied to estimate the dose distribution surrounding NPD delivery of AuNP labeled with various electron emitting radionuclides. Finally, the efficacy and toxicity of lutetium-177-AuNP NPD is evaluated in two triple negative breast cancer mouse xenograft models. In summary, this thesis outlines the design, associated dosimetry and preclinical application of radiolabeled AuNP using electron emitting radionuclides, delivered by NPD and a permanent brachytherapy technique.
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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) | 0.001 | 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 | 0.001 |
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".