Tomatidine, a novel antiviral compound towards dengue virus
Bibliographic record
Abstract
Dengue is the most common arboviral disease worldwide with 96 million symptomatic cases annually. Despite its major impact on global human health and huge economic burden there is no antiviral drug available to treat the disease. The first tetravalent dengue virus vaccine was licensed in 2015 for individuals aged 9 to 45, however, most cases are reported in infants and young children. This, together with the limited efficacy of the vaccine to dengue virus (DENV) serotype 2, stresses the need to continue the search for compounds with antiviral activity to DENV. In this report, we describe tomatidine as a novel compound with potent antiviral properties towards all DENV serotypes and the related Zika virus. The strongest effect was observed for DENV-2 with an EC50 and EC90 value of 0.82 and 1.61 μM, respectively, following infection of Huh7 cells at multiplicity of infection of 1. The selectivity index is 97.7. Time-of-drug-addition experiments revealed that tomatidine inhibits virus particle production when added pre, during and up to 12 h post-infection. Subsequent experiments show that tomatidine predominantly acts at a step after virus-cell binding and membrane fusion but prior to the secretion of progeny virions. Tomatidine was found to control the expression of the cellular protein activating transcription factor 4 (ATF4), yet, this protein is not solely responsible for the observed antiviral effect. Here, we propose tomatidine as a candidate for the treatment of dengue given its potent antiviral activity.
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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".