Core material and surface chemistry of Layer-by-Layer (LbL) nanoparticles independently direct uptake, transport, and trafficking in preclinical blood-brain barrier (BBB) models
Bibliographic record
Abstract
Abstract Development of new treatments for neurological disorders, especially brain tumors and neurodegenerative diseases, is hampered by poor accumulation of new therapeutic candidates in the brain. Drug carrying nanoparticles are a promising strategy to deliver therapeutics, but there is a major need to understand interactions between nanomaterials and the cells of the blood-brain barrier (BBB), and to what degree these interactions can be predicted by preclinical models. Here, we use a library of eighteen layer-by-layer electrostatically assembled nanoparticles (LbL-NPs) to independently assess the impact of nanoparticle core stiffness and surface chemistry on in vitro uptake and transport in three common assays, as well as intracellular trafficking in hCMEC/D3 endothelial cells. We demonstrate that nanoparticle core stiffness impacts the magnitude of material transported, while surface chemistry influences how the nanoparticles are trafficked within the cell. Finally, we demonstrate that these factors similarly dictate in vivo BBB transport using intravital imaging through cranial windows in mice, and we discover that a hyaluronic acid surface chemistry provides an unpredicted boost to transport. Taken together, these findings highlight the importance of considering factors such as assay geometry, nanomaterial labelling strategies, and fluid flow in designing preclinical assays to improve nanoparticle screening throughput for drug delivery to the brain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".