Open Source Antibiotics - Simple Diarylimidazoles are Potent Against Methicillin Resistant Staphylococcus Aureus
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is widely acknowledged as one of the most serious public health threats facing the world, yet the private sector finds it challenging to generate much-needed medicines. As an alternative discovery approach, a small array of diarylimidazoles was screened against the ESKAPE pathogens (https://en.wikipedia.org/wiki/ESKAPE) and the results made publicly available through the Open Source Antibiotics (OSA) consortium (https://github.com/opensourceantibiotics). Of the 18 compounds tested (at 32 μg/mL), 15 showed >90% growth inhibition activity against MRSA alone. In the subsequent hit-to-lead optimization of this chemotype, 147 new heterocyclic compounds containing the diarylimidazole and other core motifs were synthesized, tested against MRSA and structure-activity relationships identified. While potent, these compounds have moderate to high intrinsic clearance and some associated toxicity. The best overall balance of parameters was found with OSA_975, a compound with good potency, solubility and reduced intrinsic clearance in rat hepatocytes. We have progressed towards the knowledge of the molecular target of these phenotypically active compounds, with proteomic techniques suggesting TGFRB1 is potentially involved in the mechanism of action. Further development of these compounds towards antimicrobial medicines is available to anyone under the licensing terms of the project.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".