Confronting the Threat: Designing Highly Effective bis-Benzimidazolium Agents to Overcome Biofilm Persistence and Antimicrobial Resistance
Bibliographic record
Abstract
The objective of this study is to take the initial steps toward developing novel antibiotics to counteract the escalating problem of antimicrobial and bacterial persistence, particularly in relation to biofilms. Our approach involves emulating the structural characteristics of cationic antimicrobial peptides. To circumvent resistance development, we have designed a library of bis-benzimidazolium salts that selectively target the microbial membranes in a nonspecific manner. To explore their structure–activity relationship, we conducted experiments using these compounds on various pathogens known for their resistance to conventional antibiotics, including Gram-positive methicillin - resistant Staphylococcus aureus ( MRSA ), vancomycin - resistant Enterococcus faecium ( VRE ), and Gram-negative Escherichia coli ( E. coli ). Notably, two bis-benzimidazolium salts exhibited robust antimicrobial activity while maintaining a high level of selectivity compared with mammalian cells. Our investigations revealed significant antibiofilm activity, as these compounds rapidly acted against established biofilms. In addition, bis-benzimidazolium compounds exhibited consistent results in resistance development and cross-resistance studies. Consequently, amphiphilic bis-benzimidazolium salts hold promise as potential candidates to combat resistance-associated infections.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".