Designing Functionally Substituted Pyridine-Carbohydrazides for Potent Antibacterial and Devouring Antifungal Effect on Multidrug Resistant (MDR) Strains
Bibliographic record
Abstract
The emergence of multidrug-resistant (MDR) pathogens and the gradual depletion of available antibiotics have exacerbated the need for novel antimicrobial agents with minimal toxicity. Herein, we report functionally substituted pyridine carbohydrazide with remarkable antimicrobial effect on multi-drug resistant strains. In the series, compound 6 had potent activity against four MDR strains of Candida spp., with minimum inhibitory concentration (MIC) values being in the range of 16–24 µg/mL and percentage inhibition up to 92.57%, which was exceptional when compared to broad-spectrum antifungal drug fluconazole (MIC = 20 µg/mL, 81.88% inhibition). Substitution of the octyl chain in 6 with a shorter butyl chain resulted in a significant anti-bacterial effect of 4 against Pseudomonas aeruginosa (ATCC 27853), the MIC value being 2-fold superior to the standard combination of ampicillin/cloxacillin. Time-kill kinetics assays were used to discern the efficacy and pharmacodynamics of the potent compounds. Further, hemolysis tests confirmed that both compounds had better safety profiles than the standard drugs. Besides, molecular docking simulations were used to further explore their mode of interaction with target proteins. Overall results suggest that these compounds have the potential to become promising antimicrobial drugs against MDR strains.
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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".