Biodegradable lipid nanoparticles for genome editing in the brain via intrathecal administration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it