Iterative Design of Ionizable Lipids for Intramuscular mRNA Delivery
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are the most clinically advanced delivery vehicles for RNA and have enabled the development of RNA-based drugs such as the mRNA COVID-19 vaccines. Functional delivery of mRNA by an LNP greatly depends on the inclusion of an ionizable lipid, and small changes to these lipid structures can significantly improve delivery. However, the structure–function relationships between ionizable lipids and mRNA delivery are poorly understood, especially for LNPs administered intramuscularly. Here, we show that the iterative design of a novel series of ionizable lipids generates key structure–activity relationships and enables the optimization of chemically distinct lipids with efficacy that is on-par with the current state of the art. We find that the combination of ionizable lipids comprising an ethanolamine core and LNPs with an apparent pK a between 6.6 and 6.9 maximizes intramuscular mRNA delivery. Furthermore, we report a nonlinear relationship between the lipid-to-mRNA mass ratio and protein expression, suggesting that a critical mass ratio exists for LNPs and may depend on ionizable lipid structure. Our findings add to the mechanistic understanding of ionizable lipids and demonstrate that hydrogen bonding, ionization behavior, and lipid-to-mRNA mass ratio are key design parameters affecting intramuscular mRNA delivery. We validate these insights by applying them to the rational design of new ionizable lipids. Overall, our iterative design strategy efficiently generates potent ionizable lipids. This hypothesis-driven method reveals structure–activity relationships that lay the foundation for the optimization of ionizable lipids in future LNP-RNA drugs. We foresee that this design strategy can be extended to other optimization parameters beyond intramuscular expression.
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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 | 0.001 |
| 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".