<sup>18</sup>F-Radiolabeling and <i>In Vivo</i> Analysis of SiFA-Derivatized Polymeric Core–Shell Nanoparticles
Bibliographic record
Abstract
Nanoparticles represent the most widely studied drug delivery systems targeting cancer. Polymeric nanoparticles can be easily generated through a microemulsion polymerization. Herein, the synthesis, radiolabeling, and in vivo evaluation of nanoparticles (NPs) functionalized by an organosilicon fluoride acceptor (SiFA) are reported which can be radiolabeled without further chemical modifications. Four nanoparticles in the sub-100 nm range with distinct hydrodynamic diameters of 20 nm (NP1), 33 nm (NP2), 45 nm (NP3), and 72 nm (NP4), respectively, were synthesized under size-controlled conditions. The SiFA-labeling building block acted as an initiator for the polymerization of polymer P1. The nanoparticles were radiolabeled with fluorine-18 ( 18 F) through simple isotopic exchange (IE) and analyzed in vivo in a murine mammary tumor model (EMT6). The facile 18 F radiolabeling SiFA methodology, performed in ethanol under mild reaction conditions, gave radiochemical yields (RCYs) of 19–26% and specific activities (SA) of 0.2–0.3 GBq/mg. Based on preclinical PET analysis, the tumor uptake and clearance profiles were analyzed depending on particle size. The nanoparticle size of 33 nm showed the highest tumor accumulation of SUV mean 0.97 (= 4.4%ID/g) after 4 h p.i. through passive diffusion based on the Enhanced Permeability and Retention (EPR) effect. Overall, this approach exhibits a simple, robust, and reliable synthesis of 18 F radiolabeled polymeric nanoparticles with a favorable in vivo evaluation profile. This approach represents a straightforward synthetically accessible alternative to produce radiolabeled nanoparticles without any further surface modification to attach a radioisotope.
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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".