Bacterial Carbonic Anhydrase Inhibitor CAI0019 Demonstrates Efficacy in <i> <i>Enterococcus faecium</i> </i> Septicemic Peritonitis Mouse Model While Sparing the Microbiome
Bibliographic record
Abstract
Vancomycin-resistant enterococci are multidrug-resistant bacteria that as of 2021 continue to pervade the U.S. healthcare system as the second-most prevalent source of healthcare-acquired infections behind Escherichia coli . Given the limited treatment options for vancomycin-resistant enterococci and growing concerns about antibiotic-induced gut microbiome dysbiosis, there is an urgent need for narrow-spectrum antibiotics that can selectively target vancomycin-resistant enterococci while preserving the integrity of the gut microbiome. Previous studies have demonstrated the in vivo potential of orally dosed acetazolamide-based compounds to reduce vancomycin-resistant enterococci bioburden in the gastrointestinal tract and internal organs of mice. However, while it is hypothesized that these molecules inhibit bacterial carbonic anhydrases, the exact target of the acetazolamide scaffold in vancomycin-resistant enterococci has remained unconfirmed. Additionally, the impact of the scaffold on in vivo gut microbiome diversity remains uncharacterized. The work herein reports the chemoproteomic identification of α-carbonic anhydrase as the primary target of the acetazolamide scaffold in E. faecium and presents its uniqueness as a narrow-spectrum antibiotic target that can be exploited by CAI0019, a lead acetazolamide derivative with in vivo efficacy, while sparing gut microbiome diversity in mice. This work presents compelling data that not only confirm α-carbonic anhydrase as an antibiotic target in Enterococcus but also demonstrate that narrow-spectrum in vivo antienterococcal efficacy can be achieved through targeting α-carbonic anhydrase such that gut commensal microbiota remain unimpacted.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".