Repurposing of thalidomide and its derivatives for the treatment of SARS-coV-2 infections: Hints on molecular action
Bibliographic record
Abstract
Background and Purpose: SARS-coV-2 pandemic continues to cause an unprecedented global destabilization. There is an urgent need to develop vaccines or identify molecules to treat severe cases and repurposing of drugs is the best approach at this hour. Thalidomide, despite having an infamous history has been successfully repurposed and tested for various disease conditions including inflammatory diseases and tumor. Few reports emphasize the use of thalidomide with a SARS-coV-2 pneumonia patient being successfully treated with thalidomide. Experimental Approach: A meta-analysis comparing the transcriptomes of SARS-coV-2 infected tissues with thalidomide and lenalidomide-induced transcriptomic changes in transformed lung, endothelial and hematopoietic models was performed. Key Results: Thalidomide and lenalidomide exhibited pleiotropic effects affecting a range of biological processes including inflammation, immune response, angiogenesis, MAPK signaling, NOD-like receptor signaling, TLR signaling, leukocyte differentiation and innate immunity, the processes which are aberrantly regulated in severe COVID-19 patients. In addition, we show the similarities between the expression profiles of SARS-coV-2 infected lung and systemic lupus erythematous. Conclusion and Implications: The present study recommends thalidomide analogs as a “better fit” to treat severe cases of novel viral infections, healing the damaged network by compensating the impairment caused by the Coronavirus disease-2019 (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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".