Antibacterial activity of guanidinylated neomycin B- and kanamycin A-derived amphiphilic lipid conjugates
Bibliographic record
Abstract
OBJECTIVES: Neomycin B exhibits poor antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa, while kanamycin A shows weak activity against MRSA, methicillin-resistant Staphylococcus epidermidis (MRSE) and P. aeruginosa. The main purpose of this work was to study whether lipid conjugation of guanidinylated neomycin B- and kanamycin A-derived cationic headgroups could restore antibacterial activity against neomycin B- and kanamycin A-resistant strains, while retaining antibacterial activity against non-resistant strains. METHODS: Seven polyguanidinylated neomycin B-lipids differing in the nature of the lipid tail and two cationic kanamycin A-lipids were prepared, and their in vitro activity was assessed against a variety of neomycin B- and kanamycin A-resistant and neomycin B- and kanamycin A-non-resistant Gram-positive and Gram-negative strains. RESULTS: Conjugation of neomycin B- and kanamycin A-derived polyamine or polyguanidinylated headgroups to hydrophobic C16 or C20 lipid tails restored the anti-MRSA activity of both aminoglycosides and the anti-MRSE activity of kanamycin A. Polyguanidinylation of the neomycin B-derived headgroup lowers the hydrophobic requirement of the lipid tail segment to provide broad-spectrum antibacterial activity from C16 to C12. Moreover, guanidinylation of the polycationic headgroup in neomycin B-derived cationic lipids enhances antibacterial activity against a neomycin B-, kanamycin A- and gentamicin-resistant P. aeruginosa strain, and reduces haemolytic activity. CONCLUSIONS: These findings suggest that lipid conjugation of neomycin B- and kanamycin A-derived cationic lipids provides a general tool to enhance the antibacterial activity of these two aminoglycosides against resistant strains.
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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".