Repurposing of thalidomide and its derivatives for the treatment of SARS‐coV‐2 infections: Hints on molecular action
Bibliographic record
Abstract
AIMS: The SARS-coV-2 pandemic continues to cause an unprecedented global destabilization requiring urgent attention towards drug and vaccine development. Thalidomide, a drug with known anti-inflammatory and immunomodulatory effects has been indicated to be effective in treating a SARS-coV-2 pneumonia patient. Here, we study the possible mechanisms through which thalidomide might affect coronavirus disease-19 (COVID-19). METHODS: The present study explores the possibility of repurposing thalidomide for the treatment of SARS-coV-2 pneumonia by reanalysing transcriptomes of SARS-coV-2 infected tissues with thalidomide and lenalidomide induced transcriptomic changes in transformed lung and haematopoietic models as procured from databases, and further comparing them with the transcriptome of primary endothelial cells. RESULTS: Thalidomide and lenalidomide exhibited pleiotropic effects affecting a range of biological processes including inflammation, immune response, angiogenesis, MAPK signalling, NOD-like receptor signalling, Toll-like receptor signalling, leucocyte differentiation and innate immunity, the processes that are aberrantly regulated in severe COVID-19 patients. CONCLUSION: The present study indicates thalidomide analogues as a better fit for treating severe cases of novel viral infections, healing the damaged network by compensating the impairment caused by the 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 | 0.000 |
| 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".