Different HMGCR-inhibiting statins vary in their association with increased survival in patients with COVID-19

Background 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. Here we explored the possibility that different statins might differ in their ability to exert protective effects based on computational predictions. Methods 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, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. A database containing over 4,000 COVID-19 patients on statins was also analyzed to determine mortality risk in patients prescribed specific statins versus untreated matched controls. Findings Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins were predicted to be active in > 50% of analyses. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. 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. Interpretation Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and validate non-obvious mechanisms and drug repurposing opportunities. Funding DARPA, Wyss Institute, Hess Research Fund, UCSF Program for Breakthrough Biomedical Research, and NIH


Introduction
The emergence of the COVID-19 pandemic presented an urgent need for new and effective therapeutics, and repurposing of approved drugs with known safety profiles offered a path to identify viable treatment options. Recent retrospective studies by members of our group and others have shown that COVID-19 patients taking drugs from one of the most prescribed drug classes in the world -statins -exhibit a reduced mortality rate, but these studies pooled all statin compounds (e.g., lovastatin, simvastatin, atorvastatin, etc.) together in their analyses. [1][2][3] All statins are prescribed to lower lipid and cholesterol levels, and share a common mechanism involving inhibition of HMG-CoA reductase (HMGCR); however, statins are also known to have anti-inflammatory and immunomodulatory properties, through mechanisms that involve several pathways, 2-7 potentially by upregulating heme oxygenase-1 (HO-1). 4 In addition, while three retrospective studies that pooled all statins demonstrated a significant reduction in mortality risk, no improvement in outcomes could be detected in another study. 7 This raises the possibility that different statins might differ in their ability to reduce morbidity and mortality in COVID -19 patients, which could influence the results of studies based on which drugs were included.
Moreover, if this were true, it would be important information to distribute widely because it 4 could influence clinical decision-making with regard to statin selection during the current COVID-19 crisis.
Throughout the pandemic, multiple scientific teams predicted that existing drugs could be repurposed as potential COVID-19 therapeutics computationally through the application of high throughput in silico screens based on artificial intelligence, network diffusion, or network proximity algorithms using the human interactome, SARS-CoV-2 targets, drug targets, docking structures, or biomedical literature as algorithmic inputs. [8][9][10] These screens proposed hundreds of potential therapeutic options and led to further testing in SARS-CoV-2 infected culture systems and animal models. 8 However, while in vitro and pre-clinical testing have offered promising predictions, clinical validation and translation of predicted compounds are much more challenging and few, if any, of these drugs proposed to be repurposed for COVID-19 have demonstrated clinical efficacy. Thus, there is a need for combining improved drug prediction capabilities, despite complex and often inadequately understood biology, with real world evidence, such as electronic health records (EHRs), 1,11 to better inform which predicted compounds should advance toward clinical evaluation.
With drug repurposing in mind, we used a Network Model for Causality-Aware Discovery (NeMoCAD) computational tool based on Bayesian statistical network modeling 12 to analyze transcriptomics signatures in tissue samples obtained from COVID-19 positive patients or SARS-CoV-2 infected human cell or organoid cultures to identify FDA-approved drugs that shift the host transcriptomic response to SARS-CoV-2 towards a healthy state. This was accomplished without an a priori defined drug target or mechanism of action. This analysis revealed that a subset of commonly administered statins were among the drugs most frequently predicted to revert the genome-wide gene expression profile of COVID-19 samples to that of a healthy state. While experimental in vitro studies confirmed that the drug most frequently predicted to reverse the COVID-19 state ¾ simvastatin ¾ potently inhibited infection of Vero6 5 cells by SARS-CoV2 in vitro, other statins were less effective, suggesting that different statins might vary in their ability to protect COVID-19 patients. Furthermore, despite limited chemical diversity, statins induce a range of side effects 13 suggesting the potential for distinct biological activities outside their known shared HMGCR mechanism, which potentially could be harnessed for drug repurposing. To explore this possibility in a clinical setting, a retrospective analysis was carried out using a database containing EHRs of over 490,000 COVID-19 patients, more than 4,000 of which are actively taking statins. This analysis demonstrated that use of only a subset of statins, including simvastatin and atorvastatin, correlated with decreased morbidity and increased survival in COVID-19 patients, confirming hidden divergent activities within a seemingly homogeneous drug class.

Compound predictions
The drug prediction software, NeMoCAD (Network Modeling for Causal Discovery), was used to predict compounds that would mimic the shift from a COVID-19-positive state to a control state. 12 NeMoCAD is a drug repurposing algorithm that performs correlation analysis of transcriptional gene signatures and a Bayesian statistical analysis of a network comprised of drug-gene and drug-drug interactions to identify compounds capable of changing a transcriptional signature indicative of disease to a healthy state. 12 Using 14 publicly available transcriptomic datasets derived from human patients, tissue samples, organoids, and cells (Table 1), [14][15][16][17][18][19][20] NeMoCAD identified transcriptome-wide differential expression profiles between the control and COVID-19 states for each dataset and defined a target normalization signature to mimic, which would shift the transcriptome from a COVID-19 disease to control state (Supplemental Methods). 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). 6

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. All 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). 21 A curve fitting procedure was used to determine IC50 and CC50 values (Supplemental Methods).

Viral 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). Viral load, Hoechst fluorescence, and IP-10 measurements were measured and normalized to vehicle control samples for each assay. Each group was compared to vehicle controls using the Brown-Forsythe and Welch ANOVA tests and corrected for multiple comparisons using a Dunnett T3 test.

Human patient database analyses
The study was approved by the University of California, San Francisco, institutional review board. Data from the Cerner Real World Data COVID-19 deidentified EHR database containing records of 490,373 patients with a diagnosis of COVID-19 or COVID-19 exposure across 87 health care centers were analyzed. The following statins were included: atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin. Primary outcome was death after the onset of COVID-19. Inclusion criteria, considered comorbidities, and statistical analysis are detailed in the Supplemental Methods.

Visualizations
Plotting was performed in Prism 9 (GraphPad Software LLC) or in R versions 3.0.2 and 4.0.5. Figure 1 was made in Biorender. All rights reserved. No reuse allowed without permission.

Schematic in
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Role of the funding source
Funding sources were not involved in study design, in the collection, analysis, and interpretation of data, or in the writing of the report.

Results
The NeMoCAD gene network analysis tool 12 was used to identify FDA-approved drugs predicted to normalize the COVID-19 gene expression profile based on transcriptomic signatures of human cells or organoids infected with SARS-CoV-2 as well as cells or tissues obtained from COVID-19 patients or healthy control subjects. NeMoCAD identified gene changes across the transcriptome, compared them with gene expression changes induced by approved drugs in existing databases (e.g., LINCS, KEGG, TRRUST, CTD), and then prioritized compounds based on their ability to shift the disease transcriptomic signature state back to a healthy state ( Figure 1A). COVID-19 normalizing drugs were predicted based on 14 differential RNA-seq expression datasets (COVID-19 vs. healthy) from 12 independent transcriptomics studies ( Table 1). Across all datasets, NeMoCAD prioritized a different number of drugs for each dataset (Table 1), with 172 drugs representing the intersection of all these results and therefore shared drugs relevant to all samples ( Table 2). On average, each of the 2,436 drugs we investigated was predicted 3.2 times across the 14 differential expression datasets, with 1,477 drugs not predicted to normalize any disease signature. Across all compounds predicted to normalize at least one disease signature, each drug was predicted an average of 8.1 times across datasets. Surprisingly, we found multiple statins that inhibit HMGCR to be predicted more frequently than expected by the average, with simvastatin predicted 14/14 times, pravastatin 13/14 times, and lovastatin 12/14 times ( Figure 1B). Of the 9 statins included in the NIH LINCS program All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted April 13, 2022. ; https://doi.org/10.1101/2022.04.12.22273802 doi: medRxiv preprint 8 database, 8 were in the top 25% of drugs predicted for at least one dataset investigated ( Figure   1C). Across all datasets, simvastatin and fluvastatin were most frequently among the top 25% of predicted compounds.
We further assessed our predictions to understand how different types of input data might impact the types of compounds predicted. Stratification of the input datasets by sample source (COVID patient, autopsy sample, or cell culture/organoid) and tissue origin (lung/bronchi or other) revealed that simvastatin is frequently predicted across all dataset types ( Figure 1D). In addition, atorvastatin is often predicted when cell culture and organoid samples are used as data inputs, whereas fluvastatin is commonly predicted in human autopsy samples, and lovastatin and pravastatin are predicted at an intermediate frequency using patient input data.
Specific investigation of tissue origin revealed that simvastatin and fluvastatin are most frequently predicted when input datasets are derived from lung or bronchi tissue ( Figure 1D).
Simvastatin, atorvastatin, and lovastatin are also frequently predicted using samples from other non-lung tissues, including nasopharyngeal swabs, blood, liver, pancreas, and cardiac cells.
Based on drug predictions, statins were tested as part of a larger drug screening program in SARS-CoV-2-infected Vero6 cells. Within the statin drug class, simvastatin most potently inhibited infection with a half maximal inhibitory concentration (IC50) of 0.8 µM and almost a 10fold higher 50% cytotoxic concentration (CC50 = 6.5 µM) ( Figure 1E and Figure 2A). The other statins were either unable to significantly inhibit SARS-CoV-2 infection in Vero6 cells or they were found to be toxic at doses required to see inhibitory effects ( Figure 1E and Supplemental Figure S1). It is important to note that discordance observed between predictions made by NeMoCAD and SARS-CoV-2 inhibition in Vero6 cells is expected since NeMoCAD predicts drugs that will affect host response to infection, not necessarily directly act on the virus to inhibit infection (e.g., reduce entry or replication). However, we also know that viral replication induces a host response and the transcriptional outcome of infection will always depend on interactions All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted April 13, 2022. ; https://doi.org/10.1101/2022.04.12.22273802 doi: medRxiv preprint occurring within the virus-host system. 22,23 Therefore, we cannot completely decouple the effects of predicted drugs on viral inhibition versus more conventional host measurements, such as cytokine levels. Furthermore, we found that simvastatin also inhibited infection of HUVECs by a related coronavirus (OC43) ( Figure 2C) and potently reduced cytokine (IP-10) production without cytotoxic effects (Supplemental Figure S2). Similar results were observed with IL-6 and GM-CSF although the virus induced levels of these cytokines were variable. Thus, this particular statin appears to exhibit direct antiviral activity in addition to the HMGCR activity it shares with the other stains.
This finding that statins might differ in their ability to suppress responses to SARS-CoV-2 infection and that these effects could be independent of their common lipid lowering activity induced us to explore whether different statins also exhibit disparate activities in COVID-19 patients. We used the Cerner Real World Data COVID-19 deidentified EHR database to assess the effects of various statins on the survival of COVID-19 patients who were prescribed these medications. This large database represents a diverse population of patients diagnosed with COVID-19 from January to September 2020 with a duration of follow-up of as long as 8 months in 87 health centers across the US. Among 70,308 eligible patients, we identified 4,330 patients with who were prescribed atorvastatin, lovastatin, pravastatin, rosuvastatin, or simvastatin (Supplemental Figure S3). There were no patients who were prescribed fluvastatin in this database. The remaining 65,978 patients had no history of statin exposure (control patients).
Cohort characteristics are shown in Table 3 and Table 4. As disease severity could vary between the medication exposed and unexposed groups, we accounted for the type of encounter (Urgent care, ER, Admission for Observation, or Inpatient) at the time of COVID diagnosis and found that after matching, there was adequate balance in the encounter type between the compared medication exposed and unexposed groups, with the absolute value of standardized mean difference (SMD) of less than 0.1 for encounter type (Supplemental All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted April 13, 2022. ; https://doi.org/10.1101/2022.04.12.22273802 doi: medRxiv preprint Figures S4 and S5). Overall, the propensity score distributions and SMDs of all matched covariates between treated and control groups before and after matching showed adequate balance between groups after matching, with absolute SMD values of less than 0.1 for all covariates, including demographics, heart diseases and other COVID-19 comorbidities, as well as conditions for which statins are prescribed (Supplemental Figures S4 and S5).
Importantly, among individual statins, we found that only treatment with atorvastatin, rosuvastatin, or simvastatin was associated with a statistically significant decrease in the relative risk of death in statin-treated patients compared to matched controls (  Table 5 and Supplementary Table S5).

Discussion
Taken together, these data show that in silico prediction based on transcriptomics datasets from human patients, tissues, and cells combined with clinical database analyses is a useful approach for identifying and validating non-obvious effects of FDA-approved drugs, thereby enabling rapid repurposing of these compounds. With multiple lines of evidence coming together, we found that clinical observations cannot always be explained by experimental All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted April 13, 2022. biological studies, and vice versa. However, the goal here is to look at multiple lines of evidence and we believe that this is one of the strengths of this work.
Past retrospective studies suggested that patients prescribed drugs within the statin class have an overall lower risk of mortality from COVID-19, 1,3,5 although this was not observed in one study. 7 Predictions by NeMoCAD suggested that statins may differ in their ability to induce a shift from COVID-19 to healthy states, which could in part explain differences in results between these studies if the statin types differed. [1][2][3]7 Specifically, NeMoCAD predicted that simvastatin, fluvastatin, and atorvastatin were the most likely statins to normalize the COVID-19 gene expression profile. Indeed, when we analyzed mortality in a large EHR database of patients with COVID-19, we confirmed that there are differences in the mortality risks of COVID-19 patients prescribed the different statins compared to their respective matched control cohort, with simvastatin, atorvastatin, and rosuvastatin associated with a significant reduction in the relative risk of death. We were unable to find a statistically significant difference in mortality risk among patients prescribed lovastatin or pravastatin represented in our EHR database compared to their respective matched control cohorts. Unfortunately, there were no patients who took fluvastatin and only 13 who took pravastatin in our EHR database, so we cannot make any conclusions about the protective effects of these compounds. Exploring an EHR database with a greater number of patients prescribed these statins in the future should allow for greater insights into any differences in mortality risk associated with these particular drugs.
Simvastatin and atorvastatin were predicted to be active by NeMoCAD, while rosuvastatin was not. Assessment of the LINCS data that defines the probability of each drug affecting specific genes combined with principal component analysis (PCA) also revealed closer clustering amongst simvastatin, lovastatin, and atorvastatin, which is consistent with their frequent coprediction, whereas rosuvastatin is more distant (Figure 3A). Global analysis of the mortalityreducing statins also shows greater similarity in LINCS probability distribution between All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted April 13, 2022. ; 12 simvastatin and atorvastatin, whereas a wider probability distribution is observed for rosuvastatin (Figure 3B), and a detailed comparison of the shared top gene targets (>90 th percentile) between these three statins similarly revealed that the most genes are shared between simvastatin and atorvastatin ( Figure 3C). Therefore, these two statins may act in a more similar manner than rosuvastatin, which could influence the COVID-19 response through alternative (e.g., post-transcriptomic mechanisms) that would not be detected using our transcriptomics-based computational approach.
Collectively, our results suggest that statins exhibit divergent effects on the host response to SARS-CoV-2 infection despite a shared annotated target and common mechanism for treating dyslipidemia. In addition to their host-modulating effects, earlier work indicates that patients on statins seem to have improved outcomes following bacterial infection, an effect which is especially pronounced with respiratory tract infections, including pneumonia. 24 However, metaanalysis of these studies reveals mixed results, again potentially suggesting that statins do not act uniformly as infection modulators. These protective effects of statins against infection may be due to their well-documented anti-inflammatory and immunomodulatory properties. 25,26 Although originally developed to lower serum cholesterol, accumulating evidence suggests that statins have strong anti-inflammatory effects that contribute to their beneficial effects in patients experiencing vascular disease like atherosclerosis. 25 Furthermore, statins may upregulate heme oxygenase-1 (HO-1), 4 which is a central modulator of the immune system, effecting antiinflammation and anti-oxidation, which could prevent the severe "cytokine storm" inflammatory response that is central to morbidity and mortality in COVID-19 patients. 27 By upregulating HO-1, statins, including simvastatin, lovastatin, atorvastatin, or rosuvastatin, also can increase the production of carbon monoxide and bilirubin, 6 both of which have immunomodulatory, antioxidative and anti-inflammatory characteristics. In addition, statins may reduce the likelihood of graft-versus-host disease by inhibiting antigen presentation and shifting pro-inflammatory All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. responses toward anti-inflammatory responses. 26 We do not know precisely why certain statins appear to have superior disease-modifying activity than others. Unlike databases such as DrugBank that outline the known mechanisms of action, NeMoCAD surveys all possible interactions within the transcriptome and thus more work is needed to understand the clinical targets. However, longitudinal studies of the transcriptome in COVID-19 patients prescribed diverse statins could help clarify the mechanisms involved in these responses.
Despite strong evidence of their host modulating properties, there are also preliminary indications that some statins have more direct antibacterial and antiviral capabilities. Our in vitro studies with simvastatin suggest that at least this statin type can exhibit direct antiviral activity against both SARS-CoV-2 and the common cold coronavirus OC43, independently of its known lipid-lowering action. Simvastatin has previously been shown to exhibit superior antibacterial effects compared to fluvastatin and pravastatin, including against S. pneumoniae and M. catarrhalis infections. 24,28 In the case of bacterial infection, it has been suggested that the activity of simvastatin is linked to its hydrophobicity, which may perturb the bacterial cell membrane compared to the more hydrophilic fluvastatin and pravastatin. 28 Critically, the prodrug form of simvastatin, tested in our in vitro assays, is rapidly metabolized in vivo and therefore only a fraction of the total dose is maintained in pro-drug form. Therefore, future work should investigate simvastatin combined with an inhibitor of cytochrome P450, a hemeprotein that plays a key role in the metabolism of drugs. By limiting drug metabolism, it would be possible to better assess if the simvastatin pro-drug exhibits anti-viral effects in vivo.
Many of the potential COVID-19 therapeutics identified using past computational drug repurposing strategies failed when tested either using in vitro culture models, animals, or in the clinic. In fact, very preliminary in vivo studies by our team showed no effect of simvastatin on infection nor host response in SARS-CoV-2-infected hamsters and mice, despite attaining plasma levels that were greater than the IC50 for inhibition of infection and host response in All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint this version posted April 13, 2022. Importantly, in these databases we cannot completely rule out a possibility of unaccounted confounders correlated both with the drug and mortality, nor can we be confident that the medical histories of all patients are represented accurately as some information may be incomplete. Moreover, the retrospective nature of EHR analysis only allows us to identify an association between statin treatment and COVID-19 mortality, but not causal effects. Therefore, we envision that the process of generating in silico predictions and validating in databases will be a means to further narrow drug candidates and identify a more curated collection of therapeutics for testing in randomized control trials. Considering the continuing challenges with vaccine distribution and uptake, as well as the vulnerability of older populations to COVID-19, understanding non-obvious effects of approved drugs on patient mortality from infectious disease will be useful for combating this pandemic as well as ones that are likely to emerge in the future.
Finally, our findings suggest that repurposing efforts for COVID-19 patients may require consideration of drug-specific effects rather than taking reported drug targets and mechanisms at face value. Indeed, we believe that our approach, which counterpoints computational network-level analysis of biological interactions with in vitro exploratory screening and retrospective clinical evidence analysis, may form the basis of an altogether more powerful repurposing strategy in a pandemic scenario.

Acknowledgements
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Data sharing agreement
All transcriptomics data used for drug predictions can be accessed through public databases indicated in Table 1. The Cerner patient data analyzed in this study is subject to the following licenses/restrictions: This was an observational study of electronic health records that cannot be made publicly available. Requests to access these datasets should be directed to the Cerner All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Clinical Research Team, coviddatalab@cerner.com. All other data generated or analysed during this study are available from the corresponding author on reasonable request. All rights reserved. No reuse allowed without permission.

Contributors statements
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The copyright holder for this preprint this version posted April 13, 2022. ; https://doi.org/10.1101/2022.04.12.22273802 doi: medRxiv preprint Table 4. Cohort characteristics before propensity score matching (PSM), reflecting differing percentages of characteristics (conditions, and outcome of death) with standardized mean differences (SMD) for those prescribed a specific statin compared to the control cohort not treated with a statin.   All rights reserved. No reuse allowed without permission.
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. Mortality rates of patients treated with (A) atorvastatin, (B) lovastatin, (C) pravastatin, (D) rosuvastatin, and (E) simvastatin, and matched control groups, and relative risk of death with 95% confidence interval and Benjamini-Hochberg adjusted p-value from the iteration with the least significant
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