Synergistic Drug Combinations for Tuberculosis Therapy Identified by a Novel High-Throughput Screen
Bibliographic record
Abstract
Therapeutic options for tuberculosis (TB) are limited and notoriously ineffective despite the wide variety of potent antibiotics available for treating other bacterial infections. We investigated an approach that enables an expansion of TB therapeutic strategies by using synergistic combinations of drugs. To achieve this, we devised a high-throughput synergy screen (HTSS) of chemical libraries having known pharmaceutical properties, including thousands that are clinically approved. Spectinomycin was used to test the concept that clinically available antibiotics with limited efficacy against Mycobacterium tuberculosis might be used for TB treatment when coadministered with a synergistic partner compound used as a sensitizer. Screens using Mycobacterium smegmatis revealed many compounds in our libraries that acted synergistically with spectinomycin. Among them, several families of antimicrobial compounds, including macrolides and azoles, were also synergistic against M. tuberculosis in vitro and in a macrophage model of M. tuberculosis infection. Strikingly, each sensitizer identified for synergy with spectinomycin uniquely enhanced the activities of other clinically used antibiotics, revealing a remarkable number of unexplored synergistic drug combinations. HTSS also revealed a novel activity for bromperidol, a butyrophenone used as an antipsychotic drug, which was discovered to be bactericidal and greatly enhanced the activities of several antibiotics and drug combinations against M. tuberculosis. Our results suggest that many compounds in the currently available pharmacopoeia could be readily mobilized for TB treatment, including disease caused by multi- and extensively drug-resistant strains for which there are no effective 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".