A Pipeline for Screening Small Molecules with Growth Inhibitory Activity against Burkholderia cenocepacia
Bibliographic record
Abstract
Infections with the bacteria Burkholderia cepacia complex (Bcc) are very difficult to eradicate in cystic fibrosis patients due the intrinsic resistance of Bcc to most available antibiotics and the emergence of multiple antibiotic resistant strains during antibiotic treatment. In this work, we used a whole-cell based assay to screen a diverse collection of small molecules for growth inhibitors of a relevant strain of Bcc, B. cenocepacia K56-2. The primary screen used bacterial growth in 96-well plate format and identified 206 primary actives among 30,259 compounds. From 100 compounds with no previous record of antibacterial activity secondary screening and data mining selected a total of Bce bioactives that were further analyzed. An experimental pipeline, evaluating in vitro antibacterial and antibiofilm activity, toxicity and in vivo antibacterial activity using C. elegans was used for prioritizing compounds with better chances to be further investigated as potential Bcc antibacterial drugs. This high throughput screen, along with the in vitro and in vivo analysis highlights the utility of this experimental method to quickly identify bioactives as a starting point of antibacterial drug discovery.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".