Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells
Bibliographic record
Abstract
(1) Background: There is a strong need for prevention and treatment strategies for COVID-19 that are not impacted by SARS-CoV-2 mutations emerging in variants of concern. After virus infection, host ER resident sigma receptors form direct interactions with non-structural SARS-CoV-2 proteins present in the replication complex. (2) Methods: In this work, highly specific sigma receptor ligands were investigated for their ability to inhibit both SARS-CoV-2 genome replication and virus induced cellular toxicity. This study found antiviral activity associated with agonism of the sigma-1 receptor (e.g., SA4503), ligation of the sigma-2 receptor (e.g., CM398), and a combination of the two pathways (e.g., AZ66). (3) Results: Intermolecular contacts between these ligands and sigma receptors were identified by structural modeling. (4) Conclusions: Sigma receptor ligands and drugs with off-target sigma receptor binding characteristics were effective at inhibiting SARS-CoV-2 infection in primate and human cells, representing a potential therapeutic avenue for COVID-19 prevention and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".