Tricyclic boronic acids as broad-spectrum serine and metallo-β-lactamase inhibitors with <i>in vitro</i> activity against <i>Acinetobacter baumannii</i> : a patent evaluation (US 2025/0223303)
Bibliographic record
Abstract
Introduction With β-lactams remaining the most widely prescribed antibacterials worldwide, their continuing clinical efficacy remains an important therapeutic goal. Rapid spread of serine and metallo-ß-lactamases (SBLs and MBLs, respectively), which can inactivate β-lactams, is increasingly threatening this objective. Finding clinically useful inhibitors of MBLs, for which no FDA approved treatment currently exists, is of interest.Areas covered This article concisely reviews structurally novel xeruborbactam-inspired tricyclic boronates (reported in US 2025/0223303) with promising inhibitory activities in vitro. The literature search was conducted using SciFinder and Patentscope. By introducing novel thioether-based C5 sidechains onto the previously optimized bicyclic boronate core, the inventors explored novel chemical space yielding SBL/MBL inhibitors with seemingly improved activities against carbapenem-resistant (CR) Escherichia coli, Klebsiella pneumoniae, and, importantly, Acinetobacter baumannii, when used in combination with meropenem and/or biapenem (at least with respect to taniborbactam, i.e. boronate inhibitor in late-stage clinical development).Expert opinion Due to the major societal importance of β-lactams for modern medicine, and the clearly demonstrated clinical potential of functionalized cyclic boronates as potent dual-acting SBL/MBL inhibitors when used in combination therapies, there is ample opportunity and scope for continued investigation of this pharmacophore, particularly in the context of discovering new therapeutic options for CR 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".