Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm

Department

Research

Document Type

Conference Proceeding

Publication Title

Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Abstract

A significant cause of morbidity in COVID-19 infected patients admitted to the hospital is a severe dysregulated inflammatory response characterized as a cytokine storm, a key component of acute respiratory distress syndrome (ARDS). Here we have assembled a basic immune regulatory model from a list of 19 immune mediators with reported involvement in cytokine storm. Automated text-mining of over 2,500 full text journal publications using the MedScan natural language processing (NLP) engine identified 112 documented regulatory interactions coordinating the dynamic response of this network. This same text mining highlighted reported bi-directional associations between Coronavirus infection and a broad set of immune mediators producing a complex feedback pattern of host-pathogen interaction. Decisional logic parameters supporting the network's dynamic response were identified such that observed responses to SARS-CoV infection in an in vitro system of Calu3 human lung adenocarcinoma cells could be accurately predicted. Of the 19 competing models, 2 supported a dominant inactive immune resting state, with a predicted onset of cytokine storm in 63% and 26% of simulated infections respectively. Discrete event simulation based on the latter suggest that some repurposing strategies might outperform popular use of hydroxychloroquine as a companion to anti-viral therapy.

First Page

458

Last Page

465

DOI

10.1109/BIBE50027.2020.00080

Publication Date

10-1-2020

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