High-Throughput Discovery of Synthetic Siderophores for Trojan Horse Antibiotics
Bibliographic record
Abstract
To cause infection, bacterial pathogens must overcome host immune factors and barriers to nutrient acquisition. Reproducing these aspects of host physiology in vitro has shown great promise for antibacterial drug discovery. When used as a bacterial growth medium, human serum replicates several aspects of the host environment, including innate immunity and iron limitation. We previously reported that a high-throughput chemical screen using serum as the growth medium enabled the discovery of novel growth inhibitors overlooked by conventional screens. Here, we report that a subset of compounds from this high-throughput serum screen display an unexpected growth enhancing phenotype and are enriched for synthetic siderophores. We selected 35 compounds of diverse chemical structure and quantified their ability to enhance bacterial growth in human serum. We show that many of these compounds chelate iron, suggesting they were acting as siderophores and providing iron to the bacteria. For two different pharmacophores represented among these synthetic siderophores, conjugation to the β-lactam antibiotic ampicillin imparted iron-dependent enhancement in antibacterial activity. Conjugation of the most potent growth-enhancing synthetic siderophore with the monobactam aztreonam produced MLEB-22043, a broad-spectrum antibiotic with significantly improved activity against Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa . This synthetic siderophore-monobactam conjugate uses multiple TonB-dependent transporters for uptake into P. aeruginosa . Like aztreonam, MLEB-22043 demonstrated activity against metallo-β-lactamase expressing bacteria, and, when combined with the β-lactamase inhibitor avibactam, was active against clinical strains coexpressing the NDM-1 metallo-β-lactamase and serine β-lactamases. Our work shows that human serum is an effective bacterial growth medium for the high-throughput discovery of synthetic siderophores, enabling the development of novel Trojan Horse antibiotics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".