Domperidone inhibits dengue virus infection by targeting the viral envelope protein and nonstructural protein 1
Bibliographic record
Abstract
Dengue is a mosquito-borne disease caused by dengue virus (DENV) infection, which remains a major public health concern worldwide owing to the lack of specific treatments or antiviral drugs available. This study investigated the potential repurposing of domperidone, an antiemetic and gastrokinetic agent, to control DENV infection. Domperidone was identified by pharmacophore-based virtual screening as a small molecule that can bind to both the viral envelope (E) and the nonstructural protein 1 (NS1) of DENV. Molecular dynamics (MD) simulations and surface plasmon resonance (SPR) analysis were subsequently performed to determine specific interactions of domperidone with the DENV E and NS1 proteins and their binding affinity. Treatment of immortalized human hepatocyte-like cells (imHC) with domperidone could inhibit DENV production and NS1 secretion in a dose-dependent manner following infection with DENV serotype 2. These inhibitory effects were mediated by reduction in viral RNA replication and viral E and NS1 protein expression, but not by interference with virus entry into cells or NS1 oligomerization. The suppression of DENV production and NS1 secretion by domperidone was observed across all four DENV serotypes to varying degrees between different virus strains. The findings from our study suggest viral target-based repurposing of domperidone for modulating DENV.
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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".