Challenges for targeting SARS-CoV-2 proteases as a therapeutic strategy for COVID-19
Bibliographic record
Abstract
ABSTRACT Two proteases produced by the SARS-CoV-2 virus, M pro and PL pro , are essential for viral replication and have become the focus of drug development programs for treatment of COVID-19. We screened a highly focused library of compounds containing covalent warheads designed to target cysteine proteases to identify new lead scaffolds for both M pro and PL pro proteases. These efforts identified a small number of hits for the M pro protease and no viable hits for the PL pro protease. Of the M pro hits identified as inhibitors of the purified recombinant protease, only two compounds inhibited viral infectivity in cellular infection assays. However, we observed a substantial drop in antiviral potency upon expression of TMPRSS2, a transmembrane serine protease that acts in an alternative viral entry pathway to the lysosomal cathepsins. This loss of potency is explained by the fact that our lead M pro inhibitors are also potent inhibitors of host cell cysteine cathepsins. To determine if this is a general property of M pro inhibitors, we evaluated several recently reported compounds and found that they are also effective inhibitors of purified human cathepsin L and B and showed similar loss in activity in cells expressing TMPRSS2. Our results highlight the challenges of targeting M pro and PL pro proteases and demonstrate the need to carefully assess selectivity of SARS-CoV-2 protease inhibitors to prevent clinical advancement of compounds that function through inhibition of a redundant viral entry pathway.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".