The Potential of Human Peptide LL-37 as an Antimicrobial and Anti-Biofilm Agent
Bibliographic record
Abstract
The rise in antimicrobial resistant bacteria threatens the current methods utilized to treat bacterial infections. The development of novel therapeutic agents is crucial in avoiding a post-antibiotic era and the associated deaths from antibiotic resistant pathogens. The human antimicrobial peptide LL-37 has been considered as a potential alternative to conventional antibiotics as it displays broad spectrum antibacterial and anti-biofilm activities as well as immunomodulatory functions. While LL-37 has shown promising results, it has yet to receive regulatory approval as a peptide antibiotic. Despite the strong antimicrobial properties, LL-37 has several limitations including high cost, lower activity in physiological environments, susceptibility to proteolytic degradation, and high toxicity to human cells. This review will discuss the challenges associated with making LL-37 into a viable antibiotic treatment option, with a focus on antimicrobial resistance and cross-resistance as well as adaptive responses to sub-inhibitory concentrations of the peptide. The possible methods to overcome these challenges, including immobilization techniques, LL-37 delivery systems, the development of LL-37 derivatives, and synergistic combinations will also be considered. Herein, we describe how combination therapy and structural modifications to the sequence, helicity, hydrophobicity, charge, and configuration of LL-37 could optimize the antimicrobial and anti-biofilm activities of LL-37 for future clinical use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".