Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19
| Autor: | Leonardo O. Rodrigues, Rangaprasad Sarangarajan, Niven R. Narain, Stephane Gesta, C. Bountra, Punit P. Shah, Vivek K. Vishnudas, Eric E. Schadt, Elder Granger, Michael A. Kiebish, Poornima Tekumalla |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Coronavirus disease 2019 (COVID-19) Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Bayesian probability ACE2 02 engineering and technology Computational biology Bayesian inference lcsh:Technology Bayesian lcsh:Chemistry 03 medical and health sciences proteomics 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering General Materials Science lcsh:QH301-705.5 CHEK1 Instrumentation Host protein Fluid Flow and Transfer Processes drug repurposing lcsh:T Process Chemistry and Technology General Engineering COVID-19 Bayesian network 021001 nanoscience & nanotechnology lcsh:QC1-999 Computer Science Applications Drug repositioning 030104 developmental biology lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 MAO 020201 artificial intelligence & image processing lcsh:Engineering (General). Civil engineering (General) 0210 nano-technology lcsh:Physics 030217 neurology & neurosurgery Healthcare system |
| Zdroj: | Applied Sciences Volume 11 Issue 6 Applied Sciences, Vol 11, Iss 2466, p 2466 (2021) |
| ISSN: | 2076-3417 |
| Popis: | Background The emergence of COVID-19 progressed into a global pandemic that has functionally put the world at a standstill and catapulted major healthcare systems into an overburdened state. The dire need for therapeutic strategies to mitigate and successfully treat COVID-19 is now a public health crisis with national security implications for many countries.Methods The current study employed Bayesian networks to a longitudinal proteomic dataset generated from Caco-2 cells transfected with SARS-CoV-2 (isolated from patients returning from Wuhan to Frankfurt) [1]. Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis.Results Topology analysis identified a set of proteins directly linked to the SAR-CoV2 titer, including ACE2, a SARS-CoV-2 binding receptor, MAOB and CHECK1. Aligning with the topology analysis, MAOB and CHECK1 were also identified within the enriched drug-signatures.Conclusions Taken together, the data output from this network has identified nodal host proteins that may be connected to 18 chemical compounds, some already marketed, which provides an immediate opportunity to rapidly triage these assets for safety and efficacy against COVID-19. |
| Databáze: | OpenAIRE |
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