Bayesian model infers drug repurposing candidates for treatment of COVID-19
Submitted for peer review. This abstract has been posted online to share with the scientific community given the severity of the COVID pandemic
Michael A. Kiebish, Punit Shah, Rangaprasad Sarangarajan, Vivek K. Vishnudas, Stephane Gesta, Poornima K. Tekumalla, Chas Bountra, Elder Granger, Eric Schadt, Leonardo O. Rodrigues, and Niven R. Narain
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.
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) . Two different approaches were employed to assess the Bayesian models, a titer-center topology analysis and a drug signature enrichment analysis.
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.
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.