The Internal Nanostructure of Lipid Nanoparticles Influences Their Diverse Cellular Uptake Pathways
Bibliographic record
Abstract
Lipid nanoparticles have emerged as critical platforms for bioactive agent delivery, with their success in COVID-19 vaccines highlighting the urgent need to address gaps in understanding their biological interactions. Lyotropic liquid crystalline nanoparticles (LLCNPs) represent promising nanocarriers for bioactive agent delivery. In this study, it is revealed for the first time how internal nanostructures of LLCNPs - liposomes, cubosomes, hexosomes, and micellar cubosomes - influence their cellular uptake pathways. By isolating the effects of mesophase while maintaining consistent particle size, charge, and surface coating, it is demonstrated that non-lamellar LLCNPs, particularly cubosomes, significantly enhance cellular uptake via distinct endocytic and non-endocytic mechanisms. These nanoparticles predominantly utilize passive non-endocytic pathways, such as membrane fusion, bypassing endocytic recycling challenges faced by most nanomaterials, including lamellar liposomes. Among active endocytic pathways, macropinocytosis emerges as the dominant route for non-lamellar particles. The findings establish a direct link between LLCNP internal nanostructure and cellular internalization mechanisms, highlighting the critical role of mesophase design in optimizing nanocarrier performance. This knowledge enables the rational engineering of LLCNPs tailored to target specific uptake pathways, facilitating precision delivery for diverse therapeutic applications and addressing key barriers in intracellular drug transport.
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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".