Repurposing clinically approved cephalosporins for tuberculosis therapy
Bibliographic record
Abstract
While modern cephalosporins developed for broad spectrum antibacterial activities have never been pursued for tuberculosis (TB) therapy, we identified first generation cephalosporins having clinically relevant inhibitory concentrations, both alone and in synergistic drug combinations. Common chemical patterns required for activity against Mycobacterium tuberculosis were identified using structure-activity relationships (SAR) studies. Numerous cephalosporins were synergistic with rifampicin, the cornerstone drug for TB therapy, and ethambutol, a first-line anti-TB drug. Synergy was observed even under intracellular growth conditions where beta-lactams typically have limited activities. Cephalosporins and rifampicin were 4- to 64-fold more active in combination than either drug alone; however, limited synergy was observed with rifapentine or rifabutin. Clavulanate was a key synergistic partner in triple combinations. Cephalosporins (and other beta-lactams) together with clavulanate rescued the activity of rifampicin against a rifampicin resistant strain. Synergy was not due exclusively to increased rifampicin accumulation within the mycobacterial cells. Cephalosporins were also synergistic with new anti-TB drugs such as bedaquiline and delamanid. Studies will be needed to validate their in vivo activities. However, the fact that cephalosporins are orally bioavailable with good safety profiles, together with their anti-mycobacterial activities reported here, suggest that they could be repurposed within new combinatorial TB therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".