Editorial: Antivirulence Drugs Against Bacterial Infections
Bibliographic record
Abstract
Antimicrobial resistance poses a well-recognized public health threat worldwide due to the global dissemination of bacteria resistant to multiple antibiotic classes and the development of severe infections such as bacteremia, pneumonia, urinary tract, and intraabdominal infections. Finding solutions to fight against antibiotic-resistant bacterial infections is deemed a global priority by the World Health Organization and other international institutions. Few novel antibiotic families are under clinical evaluation, and those in the most advanced stage of development are being developed against Gram-positive bacteria. For example, Ridinilazole, an inhibitor of cell division and toxin production, is under phase 3 clinical trial for treatment of Clostridium difficile infections. Afabicin, an inhibitor of FabI (NAD(P)H)-dependent trans-2-enoyl-ACP reductase, is under phase 2 clinical trial for treatment of acute skin, bone, and joint infections by Staphylococcus aureus. Regarding antibacterials active against Gram-negatives, GSK3882347, a FimH antagonist developed for treatment of urinary tract infection by Escherichia coli, and RC-01, an LpxC inhibitor developed for treatment of severe infections by Gram-negative bacilli, are under phase 1 clinical trials. Meanwhile POL7080, a peptidomimetic targeting the lipopolysaccharide transport protein LptD, has successfully completed a phase 2 trial in patients with Pseudomonas aeruginosa lung infections. But its phase 3 clinical trial development has been stopped due to renal failure in patients. The need to increase the arsenal of antimicrobial agents warrants the exploration of new antimicrobial therapeutic strategies for use alone or in combination with currently available antibiotics. In this environment, drugs that specifically target bacterial virulence factors involved in the bacterial pathogenesis such as outer membrane proteins, toxins, siderophores, and secretion systems, etc., rather than cell viability have received considerable attention. Such an approach is less likely to induce selective pressure for resistance on pathogenic bacteria and may provide novel solutions against the emergence and spread of antimicrobial resistance. Despite this attractiveness, only a few antivirulence therapeutics are currently in clinical development.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.022 |
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".