Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics.

Dan-Yang Liu,Hong-Mei Xiao,Xiang-He Meng,Shuang Liang,Li-Jun Tan,Hong-Wen Deng,Jia-Chen Liu,Jonathan Greenbaum

doi:10.3390/pharmaceutics13040545

Dan-Yang Liu, Hong-Mei Xiao + Show 6 more

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https://doi.org/10.3390/pharmaceutics13040545

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Abstract

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug–disease pharmacological effect between drug–target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

Highlights

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the coronavirus disease 2019 (COVID-19) and triggered the largest pandemic since 1918 [1], which was responsible for >100 million cases and >2 million deaths reported globally [2]
We screened novel drug combinations for COVID-19 by integrated network-based pharmacology and transcriptome analysis based on the following steps: (1) collection of COVID-19 related genes; (2) collection of target-available drugs and construction of drug–target modules; (3) calculation of network-based proximity between drug–target modules and COVID-19 related genes; (4) filtering drugs based on gene set enrichment analysis (GSEA); (5) network-based prediction of drug combinations (Figure 1)
65 SARS-CoV-2 human host proteins were selected from the coronavirus genomes of National Center for Biotechnology Information (NCBI) datasets and 1070 potential COVID-19 related genes were obtained from the transcriptomic study by selecting the differential expression genes (DEGs) between individual COVID-19 samples (n = 7) and healthy controls (n = 6) in 7 cell types, that was 409 genes from CD14+ Monocytes, 257 genes from CD16+ Monocytes, 261 genes from Dendritic Cells, 173 genes from

Summary

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the coronavirus disease 2019 (COVID-19) and triggered the largest pandemic since 1918 [1], which was responsible for >100 million cases and >2 million deaths reported globally [2]. There are no specific antiviral drugs for SARS-CoV-2 infection so far, for the reduction of morbidity and mortality of COVID-19, active symptomatic support was urgently needed [3]. According to recent reports [4,5,6], the majority of COVID-19 patients are currently given antiviral and antibiotic treatments or combination therapy including oseltamivir, ribavirin, lopinavir, ritonavir, and moxifloxacin. Several drugs are under clinical trials to verify their safety and efficacy for COVID-19 treatment, such as favipiravir, remdesivir, and hydroxychloroquine [7].

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