AI-Validated Brain Targeted mRNA Lipid Nanoparticles with Neuronal Tropism
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Targeting therapeutic nanoparticles to the brain poses a challenge due to the restrictive nature of the blood–brain barrier (BBB). Here we report the development of mRNA-loaded lipid nanoparticles (LNPs) functionalized with BBB-interacting small molecules, thereby enhancing brain delivery and gene expression. Screening brain-targeted mRNA-LNPs in central nervous system (CNS) in vitro models and through intravenous administration in mice demonstrated that acetylcholine-conjugated LNPs achieved superior brain tropism and gene expression, outperforming LNP modifications with nicotine, glucose, memantine, cocaine, tryptophan, and other small molecules. An artificial intelligence (AI)-based model designed to predict the BBB permeability of small-molecule ligands showed strong alignment with our experimental results, providing in vivo validation of its predictive capacity. Cell-specific biodistribution analysis in Cre-reporter Ai9 mice showed that acetylcholine-functionalized LNPs preferentially transfected neurons and astrocytes following either intravenous or intracerebral administration. Mechanistic studies suggest that acetylcholine-LNP uptake is mediated by the functional engagement of acetylcholine receptors (AchRs) followed by endocytosis, which synergistically enhances intracellular mRNA delivery. Moreover, acetylcholine-LNPs successfully crossed a human BBB-on-a-chip model, enabling transgene expression in human iPSC-derived neurons. Their effective penetration and transfection in human brain organoids further support their potential activity in human-based systems. These findings establish a predictive and modular framework for engineering CNS-targeted LNPs, advancing precision gene delivery for brain disorders.
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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 | 0.000 | 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 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".