Discovery of novel fluorescent amino-pyrazolines that detect and kill Mycobacterium tuberculosis
Bibliographic record
Abstract
The emergence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) necessitates novel therapeutics with distinct mechanisms. Here, we report amino-pyrazoline derivatives as a new class of dual-functional antimycobacterial agents, integrating potent bactericidal activity with fluorescence-based bacterial imaging. Initial screening identified AP-07 as a promising hit compound (MIC 99 : 40 μM against Mycobacterium smegmatis , 49 μM against Mycobacterium bovis BCG). Structure-based optimization led to the discovery of AP-02 and AP-05 as lead compounds, with enhanced activity (MIC 99 : 13-16 μM against M. smegmatis ; 20-25 μM against M. bovis BCG). Additionally, spontaneous resistance assays detected no resistant colonies, suggesting a low risk of resistance development. Mechanistic studies confirmed Ag85C as the primary molecular target, disrupting late-stage mycolic acid biosynthesis and impairing cell wall integrity. Notably, pyrazoline derivatives exhibit intrinsic fluorescence, selectively labeling intracellular mycobacteria while remaining non-toxic to host macrophages, enabling real-time bacterial imaging. This work establishes fluorescent amino-pyrazolines as a promising foundation for next-generation antitubercular agents, bridging diagnostics and therapy in tuberculosis 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 | 0.000 | 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 | 0.001 | 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".