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Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications.
Sci Rep. 2021 10 21; 11(1):20864.SR

Abstract

Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell.

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Authors+Show Affiliations

Astore C
Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA.
Zhou H
Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA.
Jacob J
Emory Vaccine Center, Emory University, Atlanta, GA, 30329, USA. Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30329, USA. Department of Microbiology and Immunology, Emory Vaccine Center, School of Medicine, Emory University, Atlanta, GA, 30329, USA.
Skolnick J
Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA, 30332, USA. skolnick@gatech.edu.

MeSH

BenchmarkingCOVID-19ComorbidityComputational BiologyComputer SimulationCytokine Release SyndromeDrug DiscoveryHumansMachine LearningMolecular MedicineNeoplasmsNervous System DiseasesPhenotypeSARS-CoV-2ThrombosisTreatment OutcomeVirus ReplicationCOVID-19 Drug Treatment

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

34675303

Citation

Astore, Courtney, et al. "Prediction of Severe Adverse Events, Modes of Action and Drug Treatments for COVID-19's Complications." Scientific Reports, vol. 11, no. 1, 2021, p. 20864.
Astore C, Zhou H, Jacob J, et al. Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications. Sci Rep. 2021;11(1):20864.
Astore, C., Zhou, H., Jacob, J., & Skolnick, J. (2021). Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications. Scientific Reports, 11(1), 20864. https://doi.org/10.1038/s41598-021-00368-6
Astore C, et al. Prediction of Severe Adverse Events, Modes of Action and Drug Treatments for COVID-19's Complications. Sci Rep. 2021 10 21;11(1):20864. PubMed PMID: 34675303.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of severe adverse events, modes of action and drug treatments for COVID-19's complications. AU - Astore,Courtney, AU - Zhou,Hongyi, AU - Jacob,Joshy, AU - Skolnick,Jeffrey, Y1 - 2021/10/21/ PY - 2021/07/02/received PY - 2021/10/06/accepted PY - 2021/10/22/entrez PY - 2021/10/23/pubmed PY - 2021/11/16/medline SP - 20864 EP - 20864 JF - Scientific reports JO - Sci Rep VL - 11 IS - 1 N2 - Following SARS-CoV-2 infection, some COVID-19 patients experience severe host driven adverse events. To treat these complications, their underlying etiology and drug treatments must be identified. Thus, a novel AI methodology MOATAI-VIR, which predicts disease-protein-pathway relationships and repurposed FDA-approved drugs to treat COVID-19's clinical manifestations was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. To assess each manifestation's molecular basis, their prioritized shared proteins were subject to global pathway analysis. Next, the molecular features associated with hallmark COVID-19 phenotypes, e.g. unusual neurological symptoms, cytokine storms, and blood clots were explored. In practice, 24/26 of the major clinical manifestations are successfully predicted. Three major uncharacterized manifestation categories including neoplasms are also found. The prevalence of neoplasms suggests that SARS-CoV-2 might be an oncovirus due to shared molecular mechanisms between oncogenesis and viral replication. Then, repurposed FDA-approved drugs that might treat COVID-19's clinical manifestations are predicted by virtual ligand screening of the most frequent comorbid protein targets. These drugs might help treat both COVID-19's severe adverse events and lesser ones such as loss of taste/smell. SN - 2045-2322 UR - https://www.unboundmedicine.com/medline/citation/34675303/Prediction_of_severe_adverse_events_modes_of_action_and_drug_treatments_for_COVID_19's_complications_ DB - PRIME DP - Unbound Medicine ER -