Stability and Biodistribution of Thiol-Functionalized and <sup>177</sup>Lu-Labeled Metal Chelating Polymers Bound to Gold Nanoparticles
Bibliographic record
Abstract
We are studying a novel radiation nanomedicine approach to treatment of breast cancer using 30 nm gold nanoparticles (AuNP) modified with polyethylene glycol (PEG) metal-chelating polymers (MCP) that incorporate 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelators for complexing the β-particle emitter, (177)Lu. Our objective was to compare the stability of AuNP conjugated to MCP via a single thiol [DOTA-PEG-ortho-pyridyl disulfide (OPSS)], a dithiol [DOTA-PEG-lipoic acid (LA)] or multithiol end-group [PEG-pGlu(DOTA)8-LA4] and determine the elimination and biodistribution of these (177)Lu-labeled MCP-AuNP in mice. Stability to aggregation in the presence of thiol-containing dithiothreitol (DTT), L-cysteine or glutathione was assessed and dissociation of (177)Lu-MCP from AuNP in human plasma measured. Elimination of radioactivity from the body of athymic mice and excretion into the urine and feces was measured up to 168 h post-intravenous (i.v.) injection of (177)Lu-MCP-AuNP and normal tissue uptake was determined. ICP-AES was used to quantify Au in the liver and spleen and these were compared to (177)Lu. Our results showed that PEG-pGlu(DOTA)8-LA4-AuNP were more stable to aggregation in vitro than DOTA-PEG-LA-AuNP and both forms of AuNP were more stable to thiol challenge than DOTA-PEG-OPSS-AuNP. PEG-pGlu((177)Lu-DOTA)8-LA4 was the most stable in plasma. Whole body elimination of (177)Lu was most rapid for mice injected with (177)Lu-DOTA-PEG-OPSS-AuNP. Urinary excretion accounted for >90% of eliminated (177)Lu. All (177)Lu-MCP-AuNP accumulated in the liver and spleen. Liver uptake was lowest for PEG-pGlu((177)Lu-DOTA)8-LA4-AuNP but these AuNP exhibited the greatest spleen uptake. There were differences in Au and (177)Lu in the liver for PEG-pGlu((177)Lu-DOTA)8-LA4-AuNP. These differences were not correlated with in vitro stability of the (177)Lu-MCP-AuNP. We conclude that conjugation of AuNP with PEG-pGlu((177)Lu-DOTA)8-LA4 via a multithiol functional group provided the greatest stability in vitro and lowest liver uptake in vivo and is, therefore, the most promising for constructing (177)Lu-MCP-AuNP for radiation treatment of breast cancer.
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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 | 0.000 | 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 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".