Limitations and niches of the active targeting approach for nanoparticle drug delivery
Bibliographic record
Abstract
Abstract The active targeting approach has been widely employed to improve nanoparticle drug delivery. Contrary to popular conceptions, attachment of a targeting ligand to a nanopaticle does not alter its biodistribution, but only increases its internalization by target cells. Despite its potential, this strategy has drawbacks that can negate efficacy against tumors. Specifically, compared to non-targeted nanoparticles, a number of active targeting nanoparticles have decreased blood circulation time due to non-specific binding or immunogenicity, reduced tumor penetration, and high susceptibility to lysosomal degradation after internalization. In order to maximize the advantages and overcome the disadvantages, the active targeting approach is best suited for delivering membrane impermeable drugs to targets directly exposed to i.v. injected nanoparticles, such as those in circulation or in the luminal site of tumor vasculatures.
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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 | 0.003 | 0.001 |
| 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) | 0.000 | 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".