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. 2022 Oct 10;8:38. doi: 10.1038/s41540-022-00250-9

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 8 — The computational workflow for identifying and ranking drugs available for repurposing to COVID-induced ARDS consists of several sequential steps, namely (1) a regulatory network is assembled that links the biological mediators of interest through documented regulatory interactions extracted by text-mining of the peer-reviewed literature, (2) sets of logical parameters capturing receptor affinity effects and context-specific regulatory responses are identified that support adherence to available data using a Constraint Satisfaction approach, (3) the resulting sets of competing dynamic network models are used to identify sets of target nodes that if manipulated concurrently would succeed in disrupting a persistent pathology like ARDS to restore normal immune regulation, (4) candidate compounds are then ranked based on how well and how specifically they support one or several of these idealized interventions. Finally, (5) the actions of top-ranking drugs and drug combinations are simulated to verify expected response dynamics.