A potent synthetic inorganic antibiotic with activity against drug-resistant pathogens
Bibliographic record
Abstract
The acronymously named "ESKAPE" pathogens represent a group of bacteria that continue to pose a serious threat to human health, not only due to their propensity for repeated emergence, but also due to their ability to "eskape" antibiotic treatment. The evolution of multi-drug resistance in these pathogens alone has greatly outpaced the development of new therapeutics, necessitating an alternative strategy for antibiotic development that considers the evolutionary mechanisms driving antibiotic resistance. In this study, we synthesize a novel inorganic antibiotic, phosphopyricin, which has antibiotic activity against the Gram-positive methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE). We show that this potent antibiotic is bactericidal, and exhibits low toxicity in an acute dose assay in mice. As a synthetic compound that does not occur naturally, phosphopyricin would be evolutionarily foreign to microbes, thereby slowing the evolution of resistance. In addition, it loses antibiotic activity upon exposure to light, meaning that the active antibiotic will not accumulate in the general environment where strong selective pressures imposed by antibiotic residuals are known to accelerate resistance. Phosphopyricin represents an innovation in antimicrobials, having a synthetic core, and a photosensitive chemical architecture that would reduce accumulation in the environment.
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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) | 0.001 | 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".