The Effects of Particle Size and Molecular Targeting on the Intratumoral and Subcellular Distribution of Polymeric Nanoparticles
Bibliographic record
Abstract
The current study describes the impact of particle size and/or molecular targeting (epidermal growth factor, EGF) on the in vivo transport of block copolymer micelles (BCMs) in athymic mice bearing human breast cancer xenografts that express differential levels of EGF receptors (EGFR). BCMs with diameters of 25 nm (BCM-25) and 60 nm (BCM-60) were labeled with indium-111 ((111)In) or a fluorescent probe to provide a quantitative and qualitative means of evaluating their whole body, intratumoral, and subcellular distributions. BCM-25 was found to clear rapidly from the plasma compared to BCM-60, leading to an almost 2-fold decrease in their total tumor accumulation. However, the tumoral clearance of BCM-25 was delayed through EGF functionalization, enabling the targeted BCM-25 (T-BCM-25) to achieve a comparable level of total tumor deposition as the nontargeted BCM-60 (NT-BCM-60). Confocal fluorescence microscopy combined with MATLAB analyses revealed that NT-BCM-25 diffuses further away from the blood vessels (D(mean) = 42 +/- 9 microm) following extravasation, compared to NT-BCM-60 which mainly remains in the perivascular regions (D(mean) = 23 +/- 4 microm). The introduction of molecular targeting imposes the "binding site barrier" effect, which retards the tumor penetration of T-BCM-25 (D(mean) = 29 +/- 7 microm, p < 0.05). The intrinsic nuclear translocation property of EGF/EGFR leads to a significant increase in the nuclear uptake of T-BCM-25 in vitro and in vivo via active transport. Overall, these results highlight the need to consider multiple design parameters in the development of nanosystems for delivery of anticancer agents.
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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".