Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

摘要

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. Author summaryRetrospective studies have shown that COVID-19 patients taking HMGCR-inhibiting statins exhibit a reduced risk of mortality. However, potential variability in statin effects has been detected and studies from diverse sub-fields also suggest that this seemingly homogeneous class of compounds may function differently despite close chemical similarity. Based on predictions of a computational drug repurposing algorithm in combination with clinical evidence gathered during the COVID-19 pandemic, we identified that different statin types appear to have different biological properties despite similar chemical structure and they vary in their association with increased survival in COVID-19 patients. Together, in silico predictions from patient transcriptomics data, in vitro assays of infection, cytotoxicity, and host response, and retrospective clinical data provide a more comprehensive assessment of individual statin activity. Our findings, along with other emerging data, suggest that some statins (i.e., simvastatin, atorvastatin, and rosuvastatin), but not others, have a mitigating effect on the severity of COVID-19 disease reflected in a reduction in mortality. Importantly, different statins seem to exert different biological activities despite similar chemical structure and shared known mechanism of action related to lipid metabolism. These findings suggest that drug repurposing efforts may require consideration of drug-specific effects rather than accepted mechanisms.