Combination therapy enhances the antiviral activity of IFN-λ against SARS-CoV-2 and MERS-CoV
Bibliographic record
Abstract
• SARS-CoV-2 lineages showed varying, but increased resistance to IFN-λ treatment in comparison to the ancestral isolate. • MERS-CoV was found to be more resistant (50 times higher EC 90 ) to IFN-λ treatment compared to the ancestral strain of SARS-CoV-2. • We demonstrate the benefit of combining IFN-λ with direct-acting agents as a broad-spectrum therapeutic strategy against coronaviruses. Therapeutic options against pathogenic human coronaviruses remain limited. In a recent clinical trial, we demonstrated the therapeutic efficacy of pegylated-IFN-λ in COVID-19 outpatients. However, the emergence of variants that have the potential to evade IFN-mediated antiviral responses raises concerns regarding the continued efficacy of this approach. In this work, we compared the sensitivity of SARS-CoV-2 variants and MERS-CoV to IFN-λ treatment in vitro and explored the potential of combination therapy with other FDA-authorized or approved antiviral agents. We observed that in contrast to the ancestral strain, all other SARS-CoV-2 lineages showed varying, but increased resistance to IFN-λ treatment, from a 5.7-fold increase in EC 50 value for the P.1 strain to a 32.7-fold increase for the B.1.1.7 variant. We further show that combination treatment with remdesivir or nirmatrelvir enhanced the antiviral effect of IFN-λ against both SARS-CoV-2 and MERS-CoV. These findings justify the initiation of further in vivo testing that ultimately can help inform the development of more effective therapeutic guidelines against pathogenic coronaviruses.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".