Design, Synthesis and In Vitro Studies of 3-Amidocoumarins as Novel Antibiofilm Agents
Bibliographic record
Abstract
Pseudomonas aeruginosa, a life-threatening bacteria listed as a priority pathogen by World Health Organization WHO, is known to cause severe nosocomial infections and fatality in immunocompromised individuals through its quorum sensing (QS) mediated biofilm formation. P. aeruginosa’s antibiotic-resistant biofilms are highly challenging to the existing antibiotic treatment options. There is an urgent clinical need to develop novel alternative therapeutic molecules such as antibiofilm and antiquorum sensing agents to counter the emergence of an unprecedented pace of antibiotic resistance of pathogens. In this context, a library of seventy 3-amidocoumarin derivatives was designed, and docking studies were performed against the P. aeruginosa LasR receptor using AutoDock 4.0. Based on docking results, a final series of sixteen 3-amidocoumarin derivatives (4a–p) were synthesized and evaluated for antibiofilm activity in vitro. Eight compounds significantly inhibited the formation of P. aeruginosa PAO1 biofilm. Compounds 4f, 4l and 4o showed maximum % inhibition in antibiotic-resistant P. aeruginosa PAO1 biofilm formation in the range of 80% to 86%. Further, the structure–activity relationship (SAR) studies revealed that the presence of electron-donating and bromo substituents at benzamido and coumarin moieties, respectively, effectively enhances the antibiofilm activity. In addition, the binding interactions between the synthesized compounds and active sites of the LasR QS receptor (Protein Data Bank Code: 2uv0) in P. aeruginosa were also investigated by molecular docking. The high binding affinities indicate that these compounds might be suitable for development into potent inhibitors of QS and biofilm disruptors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".