Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

By Yadi Zhou, Yuan Hou, Jiayu Shen, Yin Huang, William R. Martin, Feixiong Cheng. Cell Discovery, 2020

1,405 citations recorded by the source corpus.

Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “Complementary Exposure” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

Subfields: Computational Theory and Mathematics, Infectious Diseases

View Paper: https://www.semanticscholar.org/paper/418538e40e195b8a0c7669b3a005ebcdb6a325c3

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In Computational Theory and Mathematics

1. Joseph Loscalzo
2. Marinka Zitnik
3. Albert-László Barabási
4. Onur Varol
5. Manolis Kellis
6. Daniel R. Zerbino

Citations in the corpus

• Network medicine framework for identifying drug-repurposing opportunities for COVID-19 (2020, background): “2B), indicating that the SARS-CoV-2 targets aggregate in the same network vicinity (13, 20).”
• GENCODE 2021 (2020, background): “Our list of genes for reannotation comes from several sources including recently published drug repurposing studies identifying host proteins associated with other related coronaviruses (14) and human proteins found to physically associate with SARS-CoV-2 viral proteins in the cell (15).”
• COVID-19 and the cardiovascular system: implications for risk assessment, diagnosis, and treatment options (2020, background): “SARS utilized ACE2 for cell attachment and infection through the viral envelope spike (S) protein(99) and a subsequent interaction with a cellular protease, TMPRSS2, which primes S protein for cell entry.(10) The closely related SARS-CoV-2, also thought to have originated in bats,(9) encodes an S pr”
• A Review of SARS-CoV-2 and the Ongoing Clinical Trials (2020, background): “Noteworthy is the recent study by Zhou et al., whereby an integrative analysis of interactome networks associated with human coronaviruses and drugs targeting the components of these networks was applied to reveal 16 novel candidates for drug repurposing [86] .”
• Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach (2020, background): “The SARS-COV-2 is the third most highly virulent human coronavirus of the 21 st century followed by the SARS-COV and MERS-COV, expressing the highest fatality rate (Zhou et al. 2020; Wang et al., 2020).”