Admixing of mRNA with Pre‐Formed Lipid Nanoparticles Containing a Slightly‐Cationic Ionizable Lipid Allows for Efficient mRNA Transfection In Vitro and In Vivo
Bibliographic record
Abstract
Therapeutic mRNA has emerged as a powerful tool in medicine. However, due to its fragility and large size, mRNA requires a carrier for delivery into the cellular cytosol. Lipid nanoparticles (LNPs), produced by rapidly mixing an aqueous mRNA solution with an ethanolic solution containing lipids, are currently considered the most advanced carriers for this purpose. Electrostatic interactions between mRNA and the ionizable cationic lipid, combined with hydrophobic interactions among all lipids, lead to self-assembly into LNPs that accommodate the mRNA in their core. In this study, whether mixing mRNA with pre-formed, empty LNPs (eLNPs) in an aqueous medium can be a viable alternative for mRNA expression is investigated. It is confirmed that mRNA can associate with eLNPs via electrostatic interactions, with the effectiveness of this association depending on the surface charge of the eLNPs and the ionizable lipid component. Furthermore, post-loading mRNA into eLNPs demonstrates mRNA expression levels comparable to conventional LNP(mRNA) formulations, both in vitro and in mice. This method of leveraging eLNPs offers a practical alternative to conventional LNP(mRNA) formulation for the rapid screening of multiple mRNAs. It can also enable straightforward use of LNPs for mRNA transfection by users who do not have the capacity to perform LNP formulation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".