Phenothiazines as dual inhibitors of SARS-CoV-2 main protease and COVID-19 inflammation
Bibliographic record
Abstract
COVID-19, caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2), currently has no treatment for acute infection. The main protease (M pro ) of SARS-CoV-2 is an essential enzyme for viral replication and an attractive target for disease intervention. The phenothiazine moiety has demonstrated drug versatility for biological systems, including inhibition of butyrylcholinesterase, a property important in the cholinesterase anti-inflammatory cascade. Nineteen phenothiazine drugs were investigated using in silico modelling techniques to predict binding energies and inhibition constants (K i values) with SARS-CoV-2 M pro . Because most side-effects of phenothiazines are due to interactions with various neurotransmitter receptors and transporters, phenothiazines with few such interactions were also investigated. All compounds were found to bind to the active site of SARS-CoV-2 M pro and showed K i values ranging from 1.30 to 52.4 µM in a rigid active site. Nine phenothiazines showed inhibition constants <10 µM. The compounds with limited interactions with neurotransmitter receptors and transporters showed micromolar (µM) K i values. Docking results were compared with remdesivir and showed similar interactions with key residues Glu-166 and Gln-189 in the active site. This work has identified several phenothiazines with limited neurotransmitter receptor and transporter interactions and that may provide the dual action of inhibiting SARS-CoV-2 M pro to prevent viral replication and promote the release of anti-inflammatory cytokines to curb viral-induced inflammation. These compounds are promising candidates for further investigation against SARS-CoV-2.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".