Ionizable Lipid Containing Nanocarriers for Antimicrobial Agent Delivery
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a global health crisis demanding innovative solutions. Traditional antibiotics, though pivotal over the past century in combating bacterial infections, face diminished efficacy against evolving bacterial defense mechanisms, especially in Gram-negative strains. This study explores self-assembled ionizable lipid nanoparticles (LNPs) with the incorporation of two ionizable lipid components (one cationic, one anionic) in nanocarriers for advanced antimicrobial drug delivery of the broad-spectrum antibiotic Piperacillin (Pip). Incorporating cationic ionizable lipid ALC-0315, recognized as a functional lipid in the Pfizer-BioNTech mRNA-based SARS-CoV-2 vaccine, into LNPs allowed mesophase transition, pH responsiveness, and ionization behavior in acidic environments found in sites of bacterial infections, to be studied using synchrotron small angle X-ray scattering, dynamic light scattering, and a 2-(p-toluidino)-6-naphthalene sulfonic acid assay. Incorporating another anionic ionizable lipid, oleic acid not only modulates the LNPs' physicochemical properties, such as size, internal phase nanostructure, and surface charge but also synergistically enhances the antimicrobial potency together with ALC-0315 with a benefit enhancing permeability and fusion with bacterial membranes. This study introduces a strategy for tailoring ionizable lipid compositions in LNPs, providing a new approach to antimicrobial treatment contributing to the fight against AMR.
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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 | 0.000 |
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".