Disarming the enemy: targeting bacterial toxins with small molecules
Bibliographic record
Abstract
The rapid emergence of antibiotic-resistant bacterial strains has prompted efforts to find new and more efficacious treatment strategies. Targeting virulence factors produced by pathogenic bacteria has gained particular attention in the last few years. One of the inherent advantages of this approach is that it provides less selective pressure for the development of resistance mechanisms. In addition, antivirulence drugs could potentially be the answer for diseases in which the use of conventional antibiotics is counterproductive. That is the case for bacterial toxin-mediated diseases, in which the severity of the symptoms is a consequence of the exotoxins produced by the pathogen. Examples of these are haemolytic-uraemic syndrome produced by Shiga toxins, the profuse and dangerous dehydration caused by Cholera toxin or the life-threatening colitis occasioned by clostridial toxins. This review focuses on the recent advances on the development of small molecules with antitoxin activity against Enterohaemorrhagic Escherichia coli, Vibrio cholerae and Clostridium difficile given their epidemiological importance. The present work includes studies of small molecules with antitoxin properties that act directly on the toxin (direct inhibitors) or that act by preventing expression of the toxin (indirect inhibitors).
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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 | 0.001 | 0.000 |
| 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".