Effects of Docetaxel and Host Immune Status on Nanoparticle Biodistribution and Tumor Uptake
Bibliographic record
Abstract
Despite promising preclinical research on nanoparticle-based strategies for improving cancer therapeutics, their clinical translation remains limited. The efficacy of these strategies often depends on the efficient delivery of nanoparticles to the tumor environment, making accurate representations of their biodistribution paramount in evaluating their potential. However, many studies commonly employ immunodeficient animal models to assess their therapeutic efficacy. While useful, these models do not accurately depict nanoparticle interactions with the immune system, which could lead to results being overstated due to the misrepresentation of their biodistribution. In our study, we demonstrate that immunocompetent mouse models exhibit significant alterations to the biodistribution of gold nanoparticles compared to conventional immunodeficient mouse models. The presence of a fully functional murine immune system was found to significantly increase the accumulation of gold nanoparticles in phagocytosing organs, consequently decreasing the concentration in tumors up to 95%, highlighting the importance of employing syngeneic tumor models to better predict clinical performance. Furthermore, we show that the concurrent administration of the chemotherapeutic agent docetaxel can assist in remediating this decrease by significantly enhancing gold nanoparticle accumulation in syngeneic tumors, demonstrating its potential as a combinatorial approach with nanoparticle-based cancer therapeutics. Based on our results, this elicits further investigation on the interactions of immune cells with gold nanoparticles and surface ligands used in our study to fully optimize their delivery to tumors.
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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".