Layer-by-Layer Polymer Functionalization Improves Nanoparticle Penetration and Glioblastoma Targeting in the Brain
Bibliographic record
Abstract
Glioblastoma is characterized by diffuse infiltration into surrounding healthy brain tissues, which makes it challenging to treat. Complete surgical resection is often impossible, and systemically delivered drugs cannot achieve adequate tumor exposure to prevent local recurrence. Convection-enhanced delivery (CED) offers a method for administering therapeutics directly into brain tumor tissue, but its impact has been limited by rapid clearance and off-target cellular uptake. Nanoparticle (NP) encapsulation presents a promising strategy for extending the retention time of locally delivered therapies while specifically targeting glioblastoma cells. However, the brain’s extracellular structure poses challenges for NP distribution due to its narrow, tortuous pores and a harsh ionic environment. In this study, we investigated the impact of NP surface chemistry using layer-by-layer (LbL) assembly to design drug carriers for broad spatial distribution in brain tissue and specific glioblastoma cell targeting. We found that poly- l -glutamate and hyaluronate were effective surface chemistries for targeting glioblastoma cells in vitro . Coadsorbing either polymer with a small fraction of PEGylated polyelectrolytes improved the colloidal stability without sacrificing cancer cell selectivity. Following CED in vivo, gadolinium-functionalized LbL NPs enabled MRI visualization and exhibited a distribution volume up to three times larger than liposomes and doubled the retention half-time up to 13.5 days. Flow cytometric analysis of CED-treated murine orthotopic brain tumors indicated greater cancer cell uptake and reduced healthy cell uptake for LbL NPs compared to nonfunctionalized liposomes. The distinct cellular outcomes for different colayered LbL NPs provide opportunities to tailor this modular delivery system for various therapeutic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".