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

Matthew C. Morris; Cole A. Lyman; Spencer Richman; Hong Bao Cao; Chris Cheadle; Gordon Broderick

doi:10.1109/BIBE50027.2020.00080

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

Matthew C. Morris, Cole A. Lyman, Spencer Richman, Hong Bao Cao, Chris Cheadle, Gordon Broderick

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	458-465
Number of pages	8
ISBN (Electronic)	9781728195742
DOIs	https://doi.org/10.1109/BIBE50027.2020.00080
State	Published - Oct 2020
Externally published	Yes
Event	20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States Duration: Oct 26 2020 → Oct 28 2020

Publication series

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

Conference

Conference	20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
Country/Territory	United States
City	Virtual, Cincinnati
Period	10/26/20 → 10/28/20

Keywords

Coronavirus
discrete logic
natural language processing
rapid prototyping
simulation

Access to Document

10.1109/BIBE50027.2020.00080

Cite this

Morris, M. C., Lyman, C. A., Richman, S., Cao, H. B., Cheadle, C., & Broderick, G. (2020). Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 (pp. 458-465). Article 9288142 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE50027.2020.00080

Morris, Matthew C. ; Lyman, Cole A. ; Richman, Spencer et al. / Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm. Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 458-465 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020).

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title = "Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm",

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.",

keywords = "Coronavirus, discrete logic, natural language processing, rapid prototyping, simulation",

author = "Morris, {Matthew C.} and Lyman, {Cole A.} and Spencer Richman and Cao, {Hong Bao} and Chris Cheadle and Gordon Broderick",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 ; Conference date: 26-10-2020 Through 28-10-2020",

year = "2020",

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Morris, MC, Lyman, CA, Richman, S, Cao, HB, Cheadle, C & Broderick, G 2020, Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm. in Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020., 9288142, Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 458-465, 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020, Virtual, Cincinnati, United States, 10/26/20. https://doi.org/10.1109/BIBE50027.2020.00080

Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm. / Morris, Matthew C.; Lyman, Cole A.; Richman, Spencer et al.
Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 458-465 9288142 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Morris, Matthew C.

AU - Lyman, Cole A.

AU - Richman, Spencer

AU - Cao, Hong Bao

AU - Cheadle, Chris

AU - Broderick, Gordon

PY - 2020/10

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N2 - 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.

AB - 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.

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KW - discrete logic

KW - natural language processing

KW - rapid prototyping

KW - simulation

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DO - 10.1109/BIBE50027.2020.00080

M3 - Contribución a la conferencia

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T3 - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

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

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Morris MC, Lyman CA, Richman S, Cao HB, Cheadle C, Broderick G. Predicting the Immune Response to Repurposed Drugs in Coronavirus-induced Cytokine Storm. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 458-465. 9288142. (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020). doi: 10.1109/BIBE50027.2020.00080

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

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