A novel highly potent inhibitor of TMPRSS2-like proteases blocks SARS-CoV-2 variants of concern and is broadly protective against infection and mortality in mice
Bibliographic record
Abstract
Summary The COVID-19 pandemic caused by the SARS-CoV-2 virus remains a global public health crisis. Although widespread vaccination campaigns are underway, their efficacy is reduced against emerging variants of concern (VOCs) 1,2 . Development of host-directed therapeutics and prophylactics could limit such resistance and offer urgently needed protection against VOCs 3,4 . Attractive pharmacological targets to impede viral entry include type-II transmembrane serine proteases (TTSPs), such as TMPRSS2, whose essential role in the virus lifecycle is responsible for the cleavage and priming of the viral spike protein 5–7 . Here, we identify and characterize a small-molecule compound, N-0385, as the most potent inhibitor of TMPRSS2 reported to date. N-0385 exhibited low nanomolar potency and a selectivity index of >10 6 at inhibiting SARS-CoV-2 infection in human lung cells and in donor-derived colonoids 8 . Importantly, N-0385 acted as a broad-spectrum coronavirus inhibitor of two SARS-CoV-2 VOCs, B.1.1.7 and B.1.351. Strikingly, single daily intranasal administration of N-0385 early in infection significantly improved weight loss and clinical outcomes, and yielded 100% survival in the severe K18-human ACE2 transgenic mouse model of SARS-CoV-2 disease. This demonstrates that TTSP-mediated proteolytic maturation of spike is critical for SARS-CoV-2 infection in vivo and suggests that N-0385 provides a novel effective early treatment option against COVID-19 and emerging SARS-CoV-2 VOCs.
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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) | 0.000 | 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) | 0.001 | 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".