Discovery of a Novel Class of Boron-Based Antibacterials with Activity against Gram-Negative Bacteria
Bibliographic record
Abstract
Gram-negative bacteria cause approximately 70% of the infections in intensive care units. A growing number of bacterial isolates responsible for these infections are resistant to currently available antibiotics and to many in development. Most agents under development are modifications of existing drug classes, which only partially overcome existing resistance mechanisms. Therefore, new classes of Gram-negative antibacterials with truly novel modes of action are needed to circumvent these existing resistance mechanisms. We have previously identified a new a way to inhibit an aminoacyl-tRNA synthetase, leucyl-tRNA synthetase (LeuRS), in fungi via the oxaborole tRNA trapping (OBORT) mechanism. Herein, we show how we have modified the OBORT mechanism using a structure-guided approach to develop a new boron-based antibiotic class, the aminomethylbenzoxaboroles, which inhibit bacterial leucyl-tRNA synthetase and have activity against Gram-negative bacteria by largely evading the main efflux mechanisms in Escherichia coli and Pseudomonas aeruginosa. The lead analogue, AN3365, is active against Gram-negative bacteria, including Enterobacteriaceae bearing NDM-1 and KPC carbapenemases, as well as P. aeruginosa. This novel boron-based antibacterial, AN3365, has good mouse pharmacokinetics and was efficacious against E. coli and P. aeruginosa in murine thigh infection models, which suggest that this novel class of antibacterials has the potential to address this unmet medical need.
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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 | 0.000 |
| 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".