Convection-Enhanced Delivery of Auristatin-Conjugated Layer-by-Layer Nanoparticles for Glioblastoma Treatment
Bibliographic record
Abstract
Glioblastoma (GBM) has limited treatment options, as the restrictive blood–brain barrier (BBB) prevents most therapeutics from accumulating at sufficient levels in the brain. Convection-enhanced delivery (CED) offers a method for administering therapeutics directly into brain tumor tissue, but free drugs can be cleared rapidly and may be toxic to off-target cells. Drug-loaded nanoparticles (NPs) are a promising platform to prolong the residence time and improve cellular targeting of therapeutics. We designed drug-conjugated NPs comprising a liposomal core modified with a layer-by-layer (LbL) polymer coating to promote tumor penetration, retention, and tumor-selective cellular association. Covalent conjugation of the potent microtubule inhibitor monomethyl auristatin-F (MMAF) to lipid headgroups resulted in striking potency against a range of patient-derived GBM cell lines compared to free MMAF and outperformed an EGFR-targeted antibody–drug conjugate of MMAF under clinical investigation. In vivo, a single CED infusion of LbL-functionalized MMAF NPs in orthotopic GBM-bearing mice displayed improved distribution and retention of both the NPs and the MMAF payload within the tumor. The LbL coating promotes selective uptake by GBM cells and prolongs drug retention, overcoming limitations of rapid clearance associated with traditional CED approaches. This treatment inhibited tumor progression and significantly extended survival compared to free MMAF, MMAF-conjugated liposomes, and an EGFR-MMAF antibody–drug conjugate. This NP platform offers a promising strategy for enhancing local GBM therapy by improving drug exposure within tumors while minimizing systemic toxicity.
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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) | 0.000 | 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 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".