Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar 26;23(7):3649.
doi: 10.3390/ijms23073649.

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

Ching-Tse Ting  1 , Bor-Sen Chen  1
Affiliations

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

Ching-Tse Ting et al. Int J Mol Sci. .

Abstract

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among 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-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug-target interaction (DNN-DTI) model could 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 to repurpose potential multiple-molecule drugs by the 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-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.

Keywords: COVID-19; DTI model; HPI-GWGEN; SARS-CoV-2; biomarkers; deep neural network; etiologic mechanism; host-pathogen RNA-Seq data; non-viral ARDS; systems biology.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart for constructing candidate HPI-GWGEN, real HPI-GWGEN, core HPI-GWGEN, and core signaling pathways for biomarker identification for systems drug discovery and design of potential multiple-molecule drugs for therapeutic treatment of COVID-19-associated ARDS and non-viral ARDS.
Figure 2
Figure 2
The core host-pathogen interspecies genome-wide genetic and epigenetic network (core HPI-GWGEN) of COVID-19-associated ARDS. Purple lines indicate the protein–protein interactions and orange lines denote the gene regulations. The node numbers of proteins, Rcps, TFs, RcpTFs, miRNA, LncRNA, Virus are 2525, 480, 23, 22, 670, and 13, respectively.
Figure 3
Figure 3
The core host-pathogen interspecies genome-wide genetic and epigenetic network (core HPI-GWGEN) of non-viral ARDS. Purple lines indicate the protein–protein interactions and orange lines denote the gene regulations. The nodes numbers of proteins, Rcps, TFs, RcpTFs, miRNA, LncRNA are 2591, 487, 214, 22, 49, and 657, respectively.
Figure 4
Figure 4
The common and specific core signaling pathways between COVID-19-associated ARDS and non-viral ARDS. This figure summarizes the genetic and epigenetic progression mechanism of COVID-19-associated ARDS and non-viral ARDS. The blue color background covers specific signaling pathways in non-viral ARDS. Overlapping core signaling pathways of COVID-19-associated ARDS and non-viral ARDS, namely common core signaling pathways, are covered in pink background. The skin color background covers specific signaling pathways in COVID-19-associated ARDS. The arrowheads in circle shapes indicate downregulation. The arrowheads in triangular shapes indicate upregulation. The solid lines indicate protein–protein interaction. The green nodes indicate high expression of protein/gene. The red nodes indicate low expression of protein/gene.
Figure 5
Figure 5
The flowchart for multiple-molecule drug design of COVID-19-associated ARDS and non-viral ARDS. In the right column, the drug–target interaction data are obtained from drug–target interaction databases to construct drug-target pair data. After data preprocessing, these data are divided into training data and testing data to train the DNN-DTI model for the trained DNN-DTI model in the left column. In the left column, the feature vectors of biomarkers and the feature vectors of drugs from drug–target interaction databases consist of drug-target feature pairs and are mounted into the trained DNN-DTI model to predict potential drugs for these biomarkers (drug targets). Then, these potential drugs are filtered by five drug design specifications to obtain candidate drugs as multiple-molecule drugs for COVID-19-associated ARDS and non-viral ARDS.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Dong E., Du H., Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 2020;20:533–534. doi: 10.1016/S1473-3099(20)30120-1. - DOI - PMC - PubMed
    1. Chen T., Wu D., Chen H., Yan W., Yang D., Chen G., Ma K., Xu D., Yu H., Wang H., et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ. 2020;368:m1091. doi: 10.1136/bmj.m1091. - DOI - PMC - PubMed
    1. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5. - DOI - PMC - PubMed
    1. Liu K., Fang Y.-Y., Deng Y., Liu W., Wang M.-F., Ma J.-P., Xiao W., Wang Y.-N., Zhong M.-H., Li C.-H., et al. Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province. Chin. Med. J. 2020;133:1025–1031. doi: 10.1097/CM9.0000000000000744. - DOI - PMC - PubMed
    1. Wang D., Hu B., Hu C., Zhu F., Liu X., Zhang J., Wang B., Xiang H., Cheng Z., Xiong Y., et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus—Infected Pneumonia in Wuhan, China. JAMA. 2020;323:1061–1069. doi: 10.1001/jama.2020.1585. - DOI - PMC - PubMed