Fig 1.
Combination approach for pathway-agnostic identification of compounds for drug repositioning.
(a) Overview of the combination approach to drug repositioning. (b) The NeMoCAD gene network analysis tool is a drug repurposing algorithm that uses Bayesian statistical network analysis combined with data from publicly available datasets (e.g., LINCS, KEGG, CTD, TRRUST) for reference transcriptional signatures and to define regulatory network architecture. The algorithm identifies transcriptome-wide differential expression profiles between two biological states (e.g., healthy vs. diseased) in experimental or published transcriptomic datasets and defines the target normalization signature, i.e., the subset of genes that would need to reverse their expression to revert one state to the other. The output of NeMoCAD includes correlation and causation predictions for numerous chemical compounds and approved drugs in the LINCS database based on their ability to reverse the differential expression profile of interest. (c) Patient selection and analysis of electronic health records. (Figure panels a & b created with BioRender.com.).
Table 1.
RNA-sequencing datasets used as inputs for network-based drug predictions.
Fig 2.
Simvastatin is identified by gene network-based predictions as the most likely drug in its category to reverse COVID-19 transcriptional profiles.
(a) Statins are predicted to shift the COVID-19 state to a healthy state, with simvastatin predicted for all datasets analyzed (14). Rosuvastatin was the only statin not predicted (n.p.) for any COVID-19 transcriptomics signatures. (b) 8 of 9 statins in the LINCS database were in the top 25% of drugs predicted for at least one dataset investigated. (c) Frequency of prediction for each statin when input datasets are stratified by sample source and tissue origin. Prediction frequency is normalized by the number of input datasets from each sample source and tissue origin.
Table 2.
Drugs predicted 14 times out of the 14 state changes investigated.
Table 3.
Cohort characteristics before propensity score matching (PSM), reflecting differing percentages of characteristics (demographics) with standardized mean differences (SMD) for those prescribed a specific statin compared to the control cohort not treated with a statin.
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.
Table 5.
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 result for each comparison.
Fig 3.
Effects of statins on coronavirus infections in vitro.
(a) Inhibitory and cytotoxicity parameters from SARS-CoV-2 infection of Vero6 wild-type or GFP-expressing cells for a subset of statins contained in the LINCS database. Mean parameters for each statin are derived from two independent experiments. (b) Dose-response curves demonstrating the ability for simvastatin to inhibit GFP-SARS-CoV-2 infection (MOI = 0.1) in a dose-dependent manner in Vero6 cells. (c) Simvastatin also inhibits the human coronavirus, OC43, in HUVEC cells when added at 1 or 5 μM concentrations (***p < 0.001, **p < 0.01). Error bars represent average ± SD; repeated in n = 2 independent biological experiments displayed in light (experiment 1) and dark (experiment 2) shaded data points.
Fig 4.
Comparison of LINCS drug-gene interaction probability data for each statin.
(a) PCA plots of drug-gene interaction data for each statin reveals clustering of simvastatin, atorvastatin, and lovastatin when assessed across all genes in this dataset. (b) Distribution of LINCS drug-gene probabilities for statins that reduce mortality in patients. (c) Simvastatin, atorvastatin, and rosuvastatin share top gene targets (> 90th percentile), with the most shared between atorvastatin and simvastatin.