Investigating the Impact of Nanoparticle Size on Active and Passive Tumor Targeting Efficiency
Bibliographic record
Abstract
Understanding the principles governing the design of nanoparticles for tumor targeting is essential for the effective diagnosis and treatment of solid tumors. There is currently a poor understanding of how to rationally engineer nanoparticles for tumor targeting. Here, we engineered different-sized spherical gold nanoparticles to discern the effect of particle diameter on passive (poly(ethylene glycol)-coated) and active (transferrin-coated) targeting of MDA-MB-435 orthotopic tumor xenografts. Tumor accumulation of actively targeted nanoparticles was found to be 5 times faster and approximately 2-fold higher relative to their passive counterparts within the 60 nm diameter range. For 15, 30, and 100 nm, we observed no significant differences. We hypothesize that such enhancements are the result of an increased capacity to penetrate into tumors and preferentially associate with cancer cells. We also use computational modeling to explore the mechanistic parameters that can impact tumor accumulation efficacy. We demonstrate that tumor accumulation can be mediated by high nanoparticle avidity and are weakly dependent on their plasma clearance rate. Such findings suggest that empirical models can be used to rapidly screen novel nanomaterials for relative differences in tumor targeting without the need for animal work. Although our findings are specific to MDA-MB-435 tumor xenografts, our experimental and computational findings help to enrich knowledge of design considerations that will aid in the optimal engineering of spherical gold nanoparticles for cancer applications in the future.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".