Sanfetrinem, an oral β-lactam antibiotic repurposed for the treatment of tuberculosis
Bibliographic record
Abstract
Tuberculosis (TB) is historically the world's deadliest infectious disease. New TB drugs that can avoid pre-existing resistance are desperately needed. The β-lactams are the oldest and most widely used class of antibiotics to treat bacterial infections but, for a variety of reasons, they were largely ignored until recently as a potential treatment option for TB. Recently, a growing body of evidence indicates that later-generation carbapenems in the presence of β-lactamase inhibitors could play a role in TB treatment. However, most of these drugs can only be administered intravenously in the clinic. We performed a screening of β-lactams against intracellular Mycobacterium tuberculosis (Mtb) and identified sanfetrinem cilexetil as a promising oral β-lactam candidate. Preclinical in vitro and in vivo studies demonstrated that: (i) media composition impacts the activity of sanfetrinem against Mtb, being more potent in the presence of physiologically relevant cholesterol as the only carbon source, compared to the standard broth media; (ii) sanfetrinem shows broad spectrum activity against Mtb clinical isolates, including MDR/XDR strains; (iii) sanfetrinem is rapidly bactericidal in vitro against Mtb despite being poorly stable in the assay media; (iv) there are strong in vitro synergistic interactions with amoxicillin, ethambutol, rifampicin and rifapentine and, (v) sanfetrinem cilexetil is active in an in vivo model of infection. These data, together with robust pre-clinical and clinical studies of broad-spectrum carbapenem antibiotics carried out in the 1990s by GSK, identified sanfetrinem as having potential for treating TB and catalyzed a repurposing proof-of-concept Phase 2a clinical study (NCT05388448) in South Africa.
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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) | 0.002 | 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".