Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing
The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools a...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023013221 |
| _version_ | 1797851825903239168 |
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| author | Naomi I. Maria Rosaria Valentina Rapicavoli Salvatore Alaimo Evelyne Bischof Alessia Stasuzzo Jantine A.C. Broek Alfredo Pulvirenti Bud Mishra Ashley J. Duits Alfredo Ferro |
| author_facet | Naomi I. Maria Rosaria Valentina Rapicavoli Salvatore Alaimo Evelyne Bischof Alessia Stasuzzo Jantine A.C. Broek Alfredo Pulvirenti Bud Mishra Ashley J. Duits Alfredo Ferro |
| author_sort | Naomi I. Maria |
| collection | DOAJ |
| description | The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis. |
| first_indexed | 2024-04-09T19:24:06Z |
| format | Article |
| id | doaj.art-0ed24cbe7de84a848aaf54c9a501957a |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-09T19:24:06Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-0ed24cbe7de84a848aaf54c9a501957a2023-04-05T08:20:51ZengElsevierHeliyon2405-84402023-03-0193e14115Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposingNaomi I. Maria0Rosaria Valentina Rapicavoli1Salvatore Alaimo2Evelyne Bischof3Alessia Stasuzzo4Jantine A.C. Broek5Alfredo Pulvirenti6Bud Mishra7Ashley J. Duits8Alfredo Ferro9Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA; Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA; Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra, Northwell Health, Manhasset, NY, USA; Red Cross Blood Bank Foundation Curaçao, Willemstad, Curaçao; Department of Medical Microbiology and Immunology, St. Antonius Ziekenhuis, Niewegein, the Netherlands; Corresponding author. Department of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA.Department of Physics and Astronomy, University of Catania, Italy; Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, ItalyBioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, ItalyDepartment of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini, Naples, Italy; School of Clinical Medicine, Shanghai University of Medicine and Health Sciences, Pudong, Shanghai, China; Insilico Medicine, Hong Kong Special Administrative Region, ChinaDepartment of Chemical Sciences, University of Catania, ItalyDepartment of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USABioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, ItalyDepartment of Computer Science, Mathematics, Engineering and Cell Biology, Courant Institute, Tandon and School of Medicine, New York University, New York, USA; Simon Center for Quantitative Biology, Cold Spring Harbor Lab, Long Island, USA; Corresponding author. Courant Institute of Mathematical Sciences, Room 405, 251 Mercer Street, NY, USA.Red Cross Blood Bank Foundation Curaçao, Willemstad, Curaçao; Curaçao Biomedical Health Research Institute, Willemstad, Curaçao; Institute for Medical Education, University Medical Center Groningen, Groningen, the NetherlandsBioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, ItalyThe current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.http://www.sciencedirect.com/science/article/pii/S2405844023013221COVID-19Drug repurposingSystems biologyCellular simulation modelsCellular SARS-CoV-2 signaturesCellular host-immune response |
| spellingShingle | Naomi I. Maria Rosaria Valentina Rapicavoli Salvatore Alaimo Evelyne Bischof Alessia Stasuzzo Jantine A.C. Broek Alfredo Pulvirenti Bud Mishra Ashley J. Duits Alfredo Ferro Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing Heliyon COVID-19 Drug repurposing Systems biology Cellular simulation models Cellular SARS-CoV-2 signatures Cellular host-immune response |
| title | Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing |
| title_full | Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing |
| title_fullStr | Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing |
| title_full_unstemmed | Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing |
| title_short | Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing |
| title_sort | application of the phenotype simulator for rapid identification of potential candidates in effective covid 19 drug repurposing |
| topic | COVID-19 Drug repurposing Systems biology Cellular simulation models Cellular SARS-CoV-2 signatures Cellular host-immune response |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023013221 |
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