Repurposing benztropine as TB host directed therapy
Bibliographic record
Abstract
With an estimated 1.6 million annual deaths, tuberculosis (TB) remains the major cause for bacterial mortality worldwide (1). Despite significant success in reducing mortality over the past few decades, fully controlling TB with existing antibiotics remains a considerable challenge. Current treatment for TB involves a combination of at least 3 different antibiotics that directly target the causative agent of TB, Mycobacterium tuberculosis (Mtb). Major shortcomings of current TB drugs include (i) failure to shorten treatment duration and (ii) poor efficacy against multi drug resistant (MDR) Mtb. To overcome these challenges, new therapies with novel modes of action are sought, amongst which host-directed therapeutics (HDTs) are considered a novel and attractive approach (2–4). A focused single dose screening of the COVID box library identified the Parkinson’s drug benztropine as active against intracellular Mycobacterium tuberculosis H37Rv (Mtb). Benztropine exhibited a dose dependent growth inhibition, at μM concentrations, against intracellular Mtb, Mycobacterium bovis (BCG) and Salmonella typhimurium with no direct antibacterial activity in broth media. A combination of pharmacological and RNA interference and CRISPR knock out technologies were used to determine benztropine mode of action revealing that benztropine exerts its intracellular anti-microbial activity through the macrophage histamine receptor 1 (H1). Furthermore, we show that histamine promotes intracellular Mtb growth in macrophages through H1 receptor activation. Altogether, our findings suggest the potential of benztropine to be repurposed against tuberculosis (TB) and may also pave the way for development of a new host-directed anti-infective intracellular drug class that targets H1 in infected macrophages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".