Comprehensive analysis of the tumor targeting efficiency of functionalized nanoparticles in an immunocompetent environment
Bibliographic record
Abstract
The success of nanoparticle-based cancer therapeutics relies on their efficient tumor uptake and retention. Given this, improving nanoparticle localization in tumors is paramount to maximize their therapeutic potential. A common approach to achieve this is to functionalize nanoparticles with active targeting moieties that bind to specific tumor-associated receptors. Among these, arginine-glycine-aspartic acid (RGD) peptides have shown a potential to promote tumor accumulation by targeting the α ν β 3 integrin receptor, a receptor commonly overexpressed by tumors owing to its role in promoting angiogenesis, metastasis and proliferation. Yet, its efficacy is commonly assessed using immunocompromised mice models. While useful, these models do not accurately account for immune-related interactions, which could lead to an overestimation of targeting efficacy. In our study, we investigated the efficacy of RGD peptides to improve the tumor accumulation of PEGylated gold nanoparticles (GNPs) using an immunocompetent mouse model. While RGD functionalization increased GNP uptake in cancer cells in vitro, it significantly reduced tumor accumulation in vivo due to enhanced off-target clearance by the mononuclear phagocyte system, with elevated accumulation in the spleen and liver. These findings highlight that RGD functionalization can promote immune-driven clearance in vivo, despite improving GNP uptake in cancer cells in vitro, emphasizing the importance of assessing targeting strategies in immunocompetent models for more physiologically relevant assessments.
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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) | 0.001 | 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".