Synthesis of α,ε-N,N’-Di-stearoyl Lysine-Derived Amide Lipids and Their Application to Liposome Formulation: Incorporation of Lipid A-Ligand for Bacterial Targeting and Sialic Acid for Phagocytosis Resistance
Bibliographic record
Abstract
As part of an antimicrobial resistance (AMR) strategy, we have prepared α,ε-N,N’-di-stearoyl lysine-based amide lipids to improve the chemical and biological stabilities of nanoparticles. Those amide lipids incorporated a variety of head groups, including lipid A-binding ligand (polymyxin B nonapeptide, PMBN) for bacterial targeting and sialic acid as an alternative to PEGylation for phagocytosis resistance. The study demonstrated that the PMBN-liposome specifically targeted lipid A-containing Gram-negative Acinetobacter baumannii bacteria, but not Gram-positive Staphylococcus aureus. However, such interaction was interrupted by the adsorption of serum proteins onto liposomes, demonstrating the complexity and challenge of targeted delivery. As expected, slower uptake of sialic acid-liposomes by human leukemia monocytic THP-1 cells was observed, suggesting their resistance to phagocytosis. Additionally, in a mouse model, the sialic acid-containing liposomes showed more favorable biodistribution and longer retention time than the comparable phospholipid-only liposomes. We observed that both sialic acid-incorporated and PEGylated liposomes distributed over the whole mouse bodies and remained for over 48 h. In contrast, the phospholipid-only liposomes rapidly migrated to the liver (5–15 min). In conclusion, although this study did not achieve bacteria-targeted liposome delivery, it provided evidence that the sialic acid-amide lipid can serve as an alternative to PEGylation in future nanomedicine.
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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) | 0.000 | 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 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".