System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular <em>Mycobacterium tuberculosis</em>
Bibliographic record
Abstract
Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), is a leading cause of morbidity and mortality worldwide. With the increased spread of multi drug-resistant TB (MDR-TB), there is a real urgency to develop new therapeutic strategies against M. tuberculosis infections. Traditionally, compounds are evaluated based on their antibacterial activity under in vitro growth conditions in broth; however, results are often misleading for intracellular pathogens like M. tuberculosis since in-broth phenotypic screening conditions are significantly different from the actual disease conditions within the human body. Screening for inhibitors that work inside macrophages has been traditionally difficult due to the complexity, variability in infection, and slow replication rate of M. tuberculosis. In this study, we report a new approach to rapidly assess the effectiveness of compounds on the viability of M. tuberculosis in a macrophage infection model. Using a combination of a cytotoxicity assay and an in-broth M. tuberculosis viability assay, we were able to create a screening system that generates a comprehensive analysis of compounds of interest. This system is capable of producing quantitative data at a low cost that is within reach of most labs and yet is highly scalable to fit large industrial settings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".