Nanoparticle–blood interactions: the implications on solid tumour targeting
Bibliographic record
Abstract
Nanoparticles are suitable platforms for cancer targeting and diagnostic applications. Typically, less than 10% of all systemically administered nanoparticles accumulate in the tumour. Here we explore the interactions of blood components with nanoparticles and describe how these interactions influence solid tumour targeting. In the blood, serum proteins adsorb onto nanoparticles to form a protein corona in a manner dependent on nanoparticle physicochemical properties. These serum proteins can block nanoparticle tumour targeting ligands from binding to tumour cell receptors. Additionally, serum proteins can also encourage nanoparticle uptake by macrophages, which decreases nanoparticle availability in the blood and limits tumour accumulation. The formation of this protein corona will also increase the nanoparticle hydrodynamic size or induce aggregation, which makes nanoparticles too large to enter into the tumour through pores of the leaky vessels, and prevents their deep penetration into tumours for cell targeting. Recent studies have focused on developing new chemical strategies to reduce or eliminate serum protein adsorption, and rescue the targeting potential of nanoparticles to tumour cells. An in-depth and complete understanding of nanoparticle-blood interactions is key to designing nanoparticles with optimal physicochemical properties with high tumour accumulation. The purpose of this review article is to describe how the protein corona alters the targeting of nanoparticles to solid tumours and explains current solutions to solve this problem.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".