Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients
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Abstract
Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.
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SciScore for 10.1101/2022.04.12.22273802: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: Human patient database analyses: The study was approved by the University of California, San Francisco, institutional review board. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources Viral infection of Vero6 cells with SARS-CoV-2 virus: All work with native SARS-CoV-2 virus was performed in a BSL3 laboratory and approved by our Institutional Biosafety Committee. Vero6suggested: NoneAll drug screens to assess SARS-CoV-2 inhibition and cytotoxicity were performed with Vero E6 (Vero6) cells (ATCC# CRL 1586) using published … SciScore for 10.1101/2022.04.12.22273802: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: Human patient database analyses: The study was approved by the University of California, San Francisco, institutional review board. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources Viral infection of Vero6 cells with SARS-CoV-2 virus: All work with native SARS-CoV-2 virus was performed in a BSL3 laboratory and approved by our Institutional Biosafety Committee. Vero6suggested: NoneAll drug screens to assess SARS-CoV-2 inhibition and cytotoxicity were performed with Vero E6 (Vero6) cells (ATCC# CRL 1586) using published methods (Supplemental Methods). Vero E6suggested: NoneViral infection & host response of HUVECs with OC43 virus: To measure the impact of selected drugs on HCoV-OC43 infection, 96-well plates seeded with human umbilical vein endothelial cells (HUVECs) were infected with HCoV-OC43 and treated with drugs (Supplemental Methods). HUVECssuggested: NoneSoftware and Algorithms Sentences Resources To understand underlying differences in LINCS drug-gene probability signatures that could influence drug predictions, drugs were compared by principal component analysis using the packages ggfortify and ggplot2 (R version 4.0.5). ggplot2suggested: (ggplot2, RRID:SCR_014601)Visualizations: Plotting was performed in Prism 9 (GraphPad Software LLC) or in R versions 3.0.2 and 4.0.5. GraphPadsuggested: (GraphPad Prism, RRID:SCR_002798)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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Results from scite Reference Check: We found no unreliable references.
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