Exploring the Effects of Incorporating Different Bioactive Phospholipids into Messenger Ribonucleic Acid Lipid Nanoparticle (mRNA LNP) Formulations
Bibliographic record
Abstract
The current rapid advancement in ribonucleic acid (RNA) therapeutics research depends on innovations in drug delivery, especially the development of a lipid-nanoparticle (LNP)-based system. The conventional LNP formulation typically contains four components, including an ionizable cationic lipid, a phospholipid, cholesterol or a cholesterol derivative, and poly(ethylene glycol) (PEG)-lipid, with each contributing to the formulation's overall stability and effectiveness. Among these four types of lipids, the phospholipid component is often known to provide structural support for the nanoparticles but is also a class of bioactive molecules with strong cell signaling potential. This study explores the possibility of incorporating some known structurally related bioactive phospholipids as the fifth component of a conventional four-component LNP formulation and assesses the impacts of such an approach on the physicochemical properties and biological functions of the mRNA LNP formulation. We screened a library of mRNA LNP formulations containing 7 different structurally related bioactive phospholipids at molar concentrations of 5%, 15% and 30% in addition to a conventional four-component LNP formulation (base). We observed differences in physicochemical properties between the mRNA LNP formulations that could be attributed to both the types of phospholipids examined and the molar concentrations used. Cryo-EM analysis revealed structural similarity between the Base formulation and the other formulations. We also characterized the protein expression level in HeLa cells and picked up a distinct cytokine panel signature for each formulation in human peripheral blood mononuclear cells (hPBMCs). Further immunophenotyping analysis showed that most cells that were transfected were CD4+ T cells, and the addition of the different bioactive phospholipids slightly altered cellular tropism. This exploratory study illustrates how adding the bioactive phospholipid can be used to modulate the LNP function, further expanding the design space for RNA LNP formulations and potentiating LNPs for use as RNA therapeutics.
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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".