Artesunate Demonstrates <i>in vitro</i> Synergism with Several Antiviral Agents against Human Cytomegalovirus
Bibliographic record
Abstract
BACKGROUND: Human cytomegalovirus (HCMV) infections remain a major problem in immunocompromised patients. Three antiviral agents, ganciclovir (GCV), foscarnet (FOS) and cidofovir (CDV), are currently approved for the treatment of HCMV infections. They all target the viral DNA polymerase and are associated with significant side effects. Combinations of novel antiviral compounds acting on different targets such as artesunate (ART) with currently approved drugs or eventually letermovir or maribavir (MBV) may result in synergistic effects. Here, we evaluated the in vitro activity of a series of two-drug combinations against a wild-type recombinant HCMV strain by the Gaussia luciferase (GLuc) reporter assay. METHODS: The in vitro activity of each drug was first tested individually against HCMV by using the GLuc reporter assay. The activity of two-drug combinations consisting of ART and currently approved drugs, as well as letermovir or MBV, was then analysed by the Chou-Talalay method. RESULTS: values) were 3.92 ±1.64 µM, 62.45 ±8.39 µM, 0.68 ±0.19 µM and 3.86 ±1.25 µM, respectively, whereas those of MBV and letermovir were 64 ±22 nM and 2.50 ±0.83 nM, respectively. The combination of ART with GCV, CDV or MBV was associated with synergism, whereas combination of ART with FOS or letermovir resulted in moderate synergism. As expected, the combination of MBV with GCV was antagonistic. CONCLUSIONS: These results suggest that the combination of ART with the antiviral agents tested in this study could be an interesting strategy for the treatment of HCMV infections to reduce toxicity and drug-resistance development.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".