Losartan Inhibits SARS-CoV-2 Replication in Vitro
Bibliographic record
Abstract
PURPOSE: SARS-CoV-2 infection is associated with substantial mortality and high morbidity. This study tested the effect of angiotensin II type I receptor blocker, losartan, on SARS-CoV-2 replication and inhibition of the papain-like protease of the virus. METHODS: The dose-dependent inhibitory effect of losartan, in concentrations from 1μM to 100μM as determined by quantitative cell analysis combining fluorescence microscopy, image processing, and cellular measurements (Cellomics analysis) on SARS-CoV-2 replication was investigated in Vero E6 cells. The impact of losartan on deubiquitination and deISGylation of SARS-CoV-2 papain-like protease (PLpro) were also evaluated. Results: Losartan reduced PLpro cleavage of tetraUbiquitin to diUbiquitin. It was less effective in inhibiting PLpro's cleavage of ISG15-AMC than Ubiquitin-AMC. To determine if losartan inhibited SARS-CoV-2 replication, losartan treatment of SARS-CoV-2 infected Vero E6 was examined. Losartan treatment one hour prior to SARS-CoV-2 infection reduced levels of SARS-CoV-2 nuclear protein, an indicator of virus replication, by 80% and treatment one-hour post-infection decreased viral replication by 70%. CONCLUSION: Losartan was not an effective inhibitor of deubiquitinase or deISGylase activity of the PLpro but affected the SARS-CoV-2 replication of Vero E6 cells in vitro. As losartan has a favorable safety profile and is currently available it has features necessary for efficacious drug repurposing and treatment of COVID-19.
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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) | 0.000 | 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 | 0.001 |
| 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".