Abstract
The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world with a rapid speed. Among the COVID-19 patients, SARS-CoV-2 associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-caused-ARDS and non-SARS-CoV-2-caused-ARDS are quite common and their therapy is limited owing to the intricated pathophysiology are not fully understood. In this study, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining at first. With the help of host-pathogen microarray data, real HPI-GWGEN of COVID-19-ARDS and Non-Viral-ARDS were obtained by system modeling, system identification and Akaike information criterion (AIC) of model order selection method to delete the false positives in candidate HPI-GWGEN. Afterwards, principal network projection (PNP) approach is utilized to extract core HPI-GWGEN and their core signaling pathways of COVID-19-ARDS and Non-Viral-ARDS annotated by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-ARDS and Non-Viral-ARDS, we identified essential biomarkers of pathogenesis by comparing the core signal pathways between COVID-19-ARDS and Non-Viral-ARDS. The deep neural network of drug-target interaction model (DNN-DTI) would be trained by drug-target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates as potential multiple-molecule drug by filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-ARDS but also provided novel therapeutic options for COVID-19-ARDS and Non-viral-ARDS.
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