Ceragenins: Cholic Acid-Based Mimics of Antimicrobial Peptides
Bibliographic record
Abstract
The prevalence of drug-resistant bacteria drives the quest for new antimicrobials, including those that are not expected to readily engender resistance. One option is to mimic Nature's most ubiquitous means of controlling bacterial growth, antimicrobial peptides, which have evolved over eons. In general, bacteria remain susceptible to these peptides. Human antimicrobial peptides play a central role in innate immunity, and deficiencies in these peptides have been tied to increased rates of infection. However, clinical use of antimicrobial peptides is hampered by issues of cost and stability. The development of nonpeptide mimics of antimicrobial peptides may provide the best of both worlds: a means of using the same mechanism chosen by Nature to control bacterial growth without the problems associated with peptide therapeutics. The ceragenins were developed to mimic the cationic, facially amphiphilic structures of most antimicrobial peptides. These compounds reproduce the required morphology using a bile-acid scaffolding and appended amine groups. The resulting compounds are actively bactericidal against both gram-positive and gram-negative organisms, including drug-resistant bacteria. This antimicrobial activity originates from selective association of the ceragenins with negatively charged bacterial membrane components. Association has been studied with synthetic models of bacterial membrane components, with bacterial lipopolysaccharide, with vesicles derived from bacterial phospholipids, and with whole cells. Comparisons of the antimicrobial activities of ceragenins and representative antimicrobial peptides suggest that these classes of compounds share a mechanism of action. Rapid membrane depolarization is caused by both classes as well as blebbing of bacterial membranes. Bacteria express the same genes in response to both classes of compounds. On the basis of the antibacterial activities of ceragenins and preliminary in vivo studies, we expect these compounds to find use in augmenting or replacing antimicrobial peptides in treating human disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".