Biodegradable lipid nanoparticles for genome editing in the brain via intrathecal administration
Bibliographic record
Abstract
Messenger RNA (mRNA)-based nonviral delivery of gene editors offers transformative potential for therapeutic genome editing in neurological diseases, but efficient and safe delivery to the brain remains a formidable challenge due to the restrictive blood–brain barrier. Intrathecal administration provides a clinically validated route to bypass this barrier, yet the design principles for biodegradable lipid nanoparticles (LNPs) optimized for central nervous system (CNS) delivery remain poorly defined. Here, we synthesized a 200-member combinatorial library of structurally diverse, biodegradable ionizable lipids using the Passerini three-component reaction. High-throughput in vivo screening identified P3B, a lead lipid incorporating degradable linkages and optimized ionizable head groups, which enables potent and well-tolerated intrathecal mRNA delivery. In Ai9 reporter mice, P3B-LNPs encapsulating Cas9 mRNA/sgRNA induced robust and widespread tdTomato expression in neurons and astrocytes across multiple brain regions, achieving substantially higher editing efficiency than the clinical benchmark DLin-MC3-DMA (MC3). In LumA reporter mice, P3B-LNPs mediated efficient adenine base editing, restoring luciferase expression throughout the brain with 14.8% on-target correction and minimal off-target activity. Compared with MC3, P3B-LNPs exhibited enhanced tolerability, with attenuated inflammatory responses and a safety profile supportive of repeated dosing. These findings establish P3B-LNPs as a potent, safe, and biodegradable platform for genome editing in the brain and underscore the power of combinatorial lipid chemistry and high-throughput in vivo screening to accelerate the development of next-generation LNPs for CNS-targeted mRNA therapeutics.
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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) | 0.001 | 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".