Targeting lipid nanoparticles to the blood brain barrier to ameliorate acute ischemic stroke
Bibliographic record
Abstract
After more than 100 failed drug trials for acute ischemic stroke (AIS), one of the most commonly cited reasons for the failure has been that drugs achieve very low concentrations in the at-risk penumbra. To address this problem, here we employ nanotechnology to significantly enhance drug concentration in the penumbra's blood-brain barrier (BBB), whose increased permeability in AIS has long been hypothesized to kill neurons by exposing them to toxic plasma proteins. To devise drug-loaded nanocarriers targeted to the BBB, we conjugated them with antibodies that bind to various cell adhesion molecules on the BBB endothelium. In the transient middle cerebral artery occlusion (tMCAO) mouse model, nanocarriers targeted with VCAM antibodies achieved the highest level of brain delivery, nearly 2 orders of magnitude higher than untargeted ones. VCAM-targeted lipid nanoparticles loaded with either a small molecule drug (dexamethasone) or mRNA (encoding IL-10) reduced cerebral infarct volume by 35% or 73%, respectively, and both significantly lowered mortality rates. In contrast, the drugs delivered without the nanocarriers had no effect on AIS outcomes. Thus, VCAM-targeted lipid nanoparticles represent a new platform for strongly concentrating drugs within the compromised BBB of penumbra, thereby ameliorating AIS. Graphical abstract: Acute ischemic stroke induces upregulation of VCAM. We specifically targeted upregulated VCAM in the injured region of the brain with drug- or mRNA-loaded targeted nanocarriers. Nanocarriers targeted with VCAM antibodies achieved the highest brain delivery, nearly orders of magnitude higher than untargeted ones. VCAM-targeted nanocarriers loaded with dexamethasone and mRNA encoding IL-10 reduced infarct volume by 35% and 73%, respectively, and improved survival rates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".