Rifabutin Acts in Synergy and Is Bactericidal with Frontline Mycobacterium abscessus Antibiotics Clarithromycin and Tigecycline, Suggesting a Potent Treatment Combination
Bibliographic record
Abstract
ABSTRACT Mycobacterium abscessus is a rapidly emerging mycobacterial pathogen causing dangerous pulmonary infections. Because these bacteria are intrinsically multidrug resistant, treatment options are limited and have questionable efficacy. The current treatment regimen relies on a combination of antibiotics, including clarithromycin paired with amikacin and either imipenem or cefoxitin. Tigecycline may be added when triple therapy is ineffective. We initially screened a library containing the majority of clinically available antibiotics for anti- M. abscessus activity. The screen identified rifabutin, which was then investigated for its interactions with M. abscessus antibiotics used in drug regimens. Combination of rifabutin with either clarithromycin or tigecycline generated synergistic anti- M. abscessus activity, dropping the rifabutin MIC below concentrations found in the lung. Importantly, these combinations generated bactericidal activity. The triple combination of clarithromycin, tigecycline, and rifabutin was also synergistic, and clinically relevant concentrations had a sterilizing effect on M. abscessus cultures. We suggest that combinations including rifabutin should be further investigated for treatment of M. abscessus pulmonary 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".