Charge-Switchable nanoparticles to enhance tumor penetration and accumulation
Bibliographic record
Abstract
Nanoparticle-based drug delivery systems hold potential in chemotherapy, but their limited accumulation in tumor tissues hinders effective drug concentration for combating tumor growth. Hence, altering the physicochemical properties of nanoparticles, particularly their surface charge, can enhance their performance. This study utilized a computational model to explore a nanoparticle drug delivery system capable of dynamically adjusting its surface charge. In the model, nanoparticles in the bloodstream were assigned a neutral or positive charge, which, upon reaching the tumor microenvironment, switched to a neutral or negative charge, and releasing chemotherapy drugs into the extracellular space. Results revealed that circulating nanoparticles with a positive surface charge, despite having a shorter circulation and high clearance rate compared to their neutral counterparts, could accumulate significantly in the tissue due to their high transvascular rate. After extravasation, neutralized surface-charged nanoparticles tended to accumulate only near blood microvessels due to their low diffusion rate, resulting in substantial released drug drainage back into the bloodstream. On the other hand, nanoparticles with a negative surface charge in the tumor's extracellular space, due to the reduction of nano-bio interactions, were able to penetrate deeper into the tumor, and increasing drug bioavailability by reducing the volume of drained drugs. Furthermore, the analysis suggested that burst drug release yields a higher drug concentration than sustained drug release, however their creation of bioavailability dependent on nanoparticle accumulation in the tissue. The study's findings demonstrate the potential of this delivery system and offer valuable insights for future research in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".