Combinatorial Regimens Augment Drug Monotherapy for SARS-CoV-2 Clearance in Mice
Bibliographic record
Abstract
Direct acting antivirals (DAAs) represent critical tools for combating SARS-CoV-2 variants of concern (VOCs) that evolve to escape spike-based immunity and future coronaviruses with pandemic potential. Here, we used bioluminescence imaging to evaluate therapeutic efficacy of DAAs that target SARS-CoV-2 RNA-dependent RNA polymerase (favipiravir, molnupiravir) or Main protease (nirmatrelvir) against Delta or Omicron VOCs in K18-hACE2 mice. Nirmatrelvir displayed the best efficacy followed by molnupiravir and favipiravir in suppressing viral loads in the lung. Unlike neutralizing antibody treatment, DAA monotherapy did not eliminate SARS-CoV-2 in mice. However, targeting two viral enzymes by combining molnupiravir with nirmatrelvir resulted in superior efficacy and virus clearance. Furthermore, combining molnupiravir with Caspase-1/4 inhibitor mitigated inflammation and lung pathology whereas combining molnupiravir with COVID-19 convalescent plasma yielded rapid virus clearance and 100% survival. Thus, our study provides insights into treatment efficacies of DAAs and other effective combinations to bolster COVID-19 therapeutic arsenal.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".