Unsaturated, Trialkyl Ionizable Lipids are Versatile Lipid‐Nanoparticle Components for Therapeutic and Vaccine Applications
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) have proven a successful platform for the delivery of nucleic acid (NA)-based therapeutics and vaccines, with the ionizable lipid component playing a key role in modulating potency and tolerability. Here, a library of 16 novel ionizable lipids is screened hypothesizing that short, branched trialkyl hydrophobic domains can improve LNP fusogenicity or endosomal escape, and potency. LNPs formulated with the top-performing trialkyl lipid (Lipid 10) encapsulating transthyretin siRNA elicit significantly greater gene silencing and are better tolerated than those with the benchmark Onpattro lipid DLin-MC3-DMA. Lipid 10 also demonstrates superior liver delivery of mRNA when compared to other literature ionizable lipids, is well tolerated, and successfully repeat-doses in nonhuman primates. In a prime-boost hemagglutinin rodent vaccine model, intramuscular administration of Lipid-10 LNP elicits comparable or better antibody titers to the SM-102 and ALC-0315 lipid compositions used in the U.S. Food and Drug Administration approved mRNA COVID vaccines. These data suggest that Lipid 10 is a particularly versatile ionizable lipid, well-suited for both systemic therapeutic and intramuscular vaccine applications and able to successfully deliver diverse NA payloads.
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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".