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 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) | 0.001 | 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 | 0.001 |
| 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".