Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications

The coronavirus disease 2019 (COVID-19) pandemic has claimed many lives since it was first reported in late December 2019. However, there is still no drug proven to be effective against the virus. In this study, a candidate host–pathogen–interactive (HPI) genome-wide genetic and epigenetic network (...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Stresses
المؤلفون الرئيسيون: Cheng-Gang Wang, Bor-Sen Chen
التنسيق: مقال
اللغة:الإنجليزية
منشور في: MDPI AG 2022-11-01
الموضوعات:
الوصول للمادة أونلاين:https://www.mdpi.com/2673-7140/2/4/29
_version_ 1850320482164801536
author Cheng-Gang Wang
Bor-Sen Chen
author_facet Cheng-Gang Wang
Bor-Sen Chen
author_sort Cheng-Gang Wang
collection DOAJ
container_title Stresses
description The coronavirus disease 2019 (COVID-19) pandemic has claimed many lives since it was first reported in late December 2019. However, there is still no drug proven to be effective against the virus. In this study, a candidate host–pathogen–interactive (HPI) genome-wide genetic and epigenetic network (HPI-GWGEN) was constructed via big data mining. The reverse engineering method was applied to investigate the pathogenesis of SARS-CoV-2 infection by pruning the false positives in candidate HPI-GWGEN through the HPI RNA-seq time profile data. Subsequently, using the principal network projection (PNP) method and the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, we identified the significant biomarkers usable as drug targets for destroying favorable environments for the replication of SARS-CoV-2 or enhancing the defense of host cells against it. To discover multiple-molecule drugs that target the significant biomarkers (as drug targets), a deep neural network (DNN)-based drug–target interaction (DTI) model was trained by DTI databases to predict candidate molecular drugs for these drug targets. Using the DNN-based DTI model, we predicted the candidate drugs targeting the significant biomarkers (drug targets). After screening candidate drugs with drug design specifications, we finally proposed the combination of bosutinib, erlotinib, and 17-beta-estradiol as a multiple-molecule drug for the treatment of the amplification stage of SARS-CoV-2 infection and the combination of erlotinib, 17-beta-estradiol, and sertraline as a multiple-molecule drug for the treatment of saturation stage of mild-to-moderate SARS-CoV-2 infection.
format Article
id doaj-art-e8a5b4338e034ced83891ca648f4be4d
institution Directory of Open Access Journals
issn 2673-7140
language English
publishDate 2022-11-01
publisher MDPI AG
record_format Article
spelling doaj-art-e8a5b4338e034ced83891ca648f4be4d2025-08-19T23:22:44ZengMDPI AGStresses2673-71402022-11-012440543610.3390/stresses2040029Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design SpecificationsCheng-Gang Wang0Bor-Sen Chen1Laboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, TaiwanLaboratory of Automatic Control, Signal Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, TaiwanThe coronavirus disease 2019 (COVID-19) pandemic has claimed many lives since it was first reported in late December 2019. However, there is still no drug proven to be effective against the virus. In this study, a candidate host–pathogen–interactive (HPI) genome-wide genetic and epigenetic network (HPI-GWGEN) was constructed via big data mining. The reverse engineering method was applied to investigate the pathogenesis of SARS-CoV-2 infection by pruning the false positives in candidate HPI-GWGEN through the HPI RNA-seq time profile data. Subsequently, using the principal network projection (PNP) method and the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, we identified the significant biomarkers usable as drug targets for destroying favorable environments for the replication of SARS-CoV-2 or enhancing the defense of host cells against it. To discover multiple-molecule drugs that target the significant biomarkers (as drug targets), a deep neural network (DNN)-based drug–target interaction (DTI) model was trained by DTI databases to predict candidate molecular drugs for these drug targets. Using the DNN-based DTI model, we predicted the candidate drugs targeting the significant biomarkers (drug targets). After screening candidate drugs with drug design specifications, we finally proposed the combination of bosutinib, erlotinib, and 17-beta-estradiol as a multiple-molecule drug for the treatment of the amplification stage of SARS-CoV-2 infection and the combination of erlotinib, 17-beta-estradiol, and sertraline as a multiple-molecule drug for the treatment of saturation stage of mild-to-moderate SARS-CoV-2 infection.https://www.mdpi.com/2673-7140/2/4/29SARS-CoV-2COVID-19systems biologyreverse engineeringprincipal network projection (PNP)drug–target interaction (DTI) model
spellingShingle Cheng-Gang Wang
Bor-Sen Chen
Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
SARS-CoV-2
COVID-19
systems biology
reverse engineering
principal network projection (PNP)
drug–target interaction (DTI) model
title Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
title_full Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
title_fullStr Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
title_full_unstemmed Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
title_short Multiple-Molecule Drug Repositioning for Disrupting Progression of SARS-CoV-2 Infection by Utilizing the Systems Biology Method through Host-Pathogen-Interactive Time Profile Data and DNN-Based DTI Model with Drug Design Specifications
title_sort multiple molecule drug repositioning for disrupting progression of sars cov 2 infection by utilizing the systems biology method through host pathogen interactive time profile data and dnn based dti model with drug design specifications
topic SARS-CoV-2
COVID-19
systems biology
reverse engineering
principal network projection (PNP)
drug–target interaction (DTI) model
url https://www.mdpi.com/2673-7140/2/4/29
work_keys_str_mv AT chenggangwang multiplemoleculedrugrepositioningfordisruptingprogressionofsarscov2infectionbyutilizingthesystemsbiologymethodthroughhostpathogeninteractivetimeprofiledataanddnnbaseddtimodelwithdrugdesignspecifications
AT borsenchen multiplemoleculedrugrepositioningfordisruptingprogressionofsarscov2infectionbyutilizingthesystemsbiologymethodthroughhostpathogeninteractivetimeprofiledataanddnnbaseddtimodelwithdrugdesignspecifications