Network analytics for drug repurposing in COVID-19

Abstract

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.

Keywords: COVID-19; drug repurposing; host factors; network biology; network controllability.

Figure 1

Study design of network controllability for drug repurposing. We included all the approved and investigational small molecule drugs, except for those illicit or nutraceutical. For each drug (red in the bottom-left network) we identified their drug targets [8] (yellow in the bottom-left network). We also included the top 200 host factors required for SARS-CoV-2 infection found by [4] for viral loads at MOI 0.01 and 0.3, and all protein–protein-directed interactions from KEGG [13], OmniPath [14] and SIGNOR [15]. Using the NetControl4BioMed platform [16], we identified all control paths of length at most 3 from drug targets to host factors. We ranked the drugs based on the number of host factors they can control through any of their targets.

Figure 2

Drug categories. (A) The overlap between all approved or investigational drugs. (B) The overlap between drug targets in both networks. (C) The overlap between approved and investigational drugs for each network and their intersection.

Figure 3

drug targets and the number of host factors they can control. (A) MOI 0.01, (B) MOI 0.3. Host factors for the SARS-CoV-2 infection and the number of drug–target genes that can be used to control them. (C) MOI 0.01, (D) MOI 0.3.

Figure 4

Drug classification based on their activity according to NCATS COVID-19 OpenData Portal [37]. (A) Drugs identified in either of the two MOI networks. (B) Drugs identified in both MOI networks. (C) Drugs identified in the MOI 0.01 network. (D) Drugs identified in the MOI 0.3 network. Color code: green – active, orange – not tested, cream – inactive, but not tested in all assays, gray – inactive, yellow – active in the counter assay.

Figure 5

The robustness analysis results. The box plots represent how often various drugs were identified in the control analyses we ran, with the search done in various parameter settings: the host factors selected to be the top 50/100/150/200/250/300 ranked in the [4] datasets; the number of in-between nodes set to be 1/2/3; the maximum length of the control paths set to be 2/3/4.