Rational design and modular synthesis of biodegradable ionizable lipids via the Passerini reaction for mRNA delivery
Bibliographic record
Abstract
The ionizable lipid component of lipid nanoparticle (LNP) formulations is essential for mRNA delivery by facilitating endosomal escape. Conventionally, these lipids are synthesized through complex, multistep chemical processes that are both time-consuming and require significant engineering. Furthermore, the development of new ionizable lipids is hindered by a limited understanding of the structure-activity relationships essential for effective mRNA delivery. In this work, we have developed a modular platform utilizing the Passerini reaction to rapidly generate large, chemically diverse libraries of biodegradable ionizable lipids. This high-throughput approach enables the systematic exploration of various lipid components-head groups, tails, and spacers-and their impacts on mRNA delivery efficiency. By investigating the hydrogen bonding potential between the lipid's head groups and the mRNA's ribose phosphate complex, we found that optimizing the methylene units between the lipid's head groups and linkages could enhance endosomal escape and, consequently, mRNA delivery efficiencies. Leveraging this insight, our platform has led to the identification of the biodegradable ionizable lipid A4B4-S3, which outperforms the current clinical benchmark, SM-102, in gene editing efficacy in mouse liver following systemic administration and demonstrates the promise for repeat-dose protein replacement treatments. This work not only offers a rapid, scalable method for ionizable lipid synthesis but also deepens our understanding of their structure-activity relationships, paving the way for more effective 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) | 0.001 | 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) | 0.001 | 0.001 |
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".