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 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 | 0.001 |
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".