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An In-Silico Analysis of Drugs Potentially Modulating the Cytokine Storm Triggered by SARS-CoV-2 Infection

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Submitted:

05 June 2020

Posted:

07 June 2020

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Abstract
The ongoing COVID-19 pandemic is one of the biggest health and societal challenges of the recent decades. Among the causes of mortality triggered by SARS-CoV-2 infection, the presence of an inflammatory "cytokine storm" (CS) at later stages of the disease has been found to play a determinant role. Here, we used available transcriptomic data from the bronchoalveolar lavage fluid (BALF) of COVID-19 patients suffering from a CS to obtain gene-signatures associated to this pathological process. Using these signatures, we interrogated the Connectivity MAP (CMap) dataset that contains the effects of over 5,000 small molecules on the transcriptome of human cancer cell lines, and looked for molecules which effects on transcription mimic or oppose those associated to the CS. Consistent with their medical use, this analysis found a significant enrichment of glucocorticoids or inhibitors of the Janus Kinases (JAK) as drugs that could revert the CS. On the other hand, molecules that potentiate the immune response such as PKC activators are predicted to worsen the CS. Besides these expected findings, our analysis also reveals a potential effect of the antibiotic doxycycline or MAPK/RAF/MEK inhibitors in reverting the CS, or of topoisomerase inhibitors and the anti-alcohol abuse drug disulfiram in potentiating its effects. Finally, our analyses support that the gender-related differences in the severity of COVID-19 are related to the anti-inflammatory properties of female hormones. While acknowledging that this is an analysis based on limited available data, we decided to share it as a resource that might help others in the selection of drugs that could be tested in the context of experimental models of CS triggered by viral infections.
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Subject: Biology and Life Sciences  -   Immunology and Microbiology
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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