Drug Combination Treatment as a Strategy for Inhibiting Human Adenovirus Replication
Bibliographic record
Abstract
Human adenoviruses (HAdVs) are ubiquitous human pathogens that infect the respiratory, ocular, and gastrointestinal tissues. Despite the severity of these infections in immunocompromised patients, there are no clinically approved antiviral medications to treat HAdV infections. Over the past decade, many compounds have been found to interfere with different parts of the HAdV replication cycle. One critical barrier to developing a successful HAdV therapy arises if the drug concentration required for antiviral efficacy is clinically unachievable or too toxic for patient use. This problem can be diminished by using a combination of drugs that function synergistically, potentially allowing the use of lower drug concentrations that are clinically achievable and/or exhibit acceptable toxicity profiles. In this exploratory study, we examined the antiviral activity of pairwise combinations of six drugs that have been previously shown to disrupt HAdV replication: ivermectin, digitoxin, deguelin, niclosamide, rosiglitazone, and remdesivir. Combinations of these drugs showed a stronger reduction in HAdV progeny production, protein expression, and genome replication efficiency compared to their individual effects. These experiments serve to illustrate the feasibility and benefits of drug combinations that synergize against HAdV replication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".