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通过系统生物学方法和基于五个药物设计规范的 DNN-DTI 模型,重新利用多种分子药物治疗 COVID-19 相关急性呼吸窘迫综合征和非病毒性急性呼吸窘迫综合征。

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.

机构信息

Laboratory of Automatic Control, Signaling Processing and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

出版信息

Int J Mol Sci. 2022 Mar 26;23(7):3649. doi: 10.3390/ijms23073649.

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.

摘要

新型冠状病毒病 2019(COVID-19)疫情正在全球迅速蔓延。在 COVID-19 患者中,SARS-CoV-2 相关的急性呼吸窘迫综合征(ARDS)是导致高发病率和死亡率的主要原因。然而,SARS-CoV-2 相关 ARDS 和非 SARS-CoV-2 相关 ARDS 的临床表现相当常见,由于其复杂的病理生理学尚未完全了解,治疗方法有限。在这项研究中,为了研究 SARS-CoV-2 相关 ARDS 和非 SARS-CoV-2 相关 ARDS 的发病机制,我们首先通过数据库挖掘构建了候选宿主-病原体种间全基因组遗传和表观遗传网络(HPI-GWGEN)。借助宿主-病原体 RNA 测序(RNA-Seq)数据,通过系统建模、系统识别和 Akaike 信息准则(AIC)模型阶数选择方法,获得了 COVID-19 相关 ARDS 和非病毒性 ARDS 的真实 HPI-GWGEN,以删除候选 HPI-GWGEN 中的假阳性。为了便于缓解,我们利用主网络投影(PNP)方法提取核心 HPI-GWGEN,然后通过核心 HPI-GWGEN 对 COVID-19 相关 ARDS 和非病毒性 ARDS 的核心信号通路进行注释,通过 KEGG 通路。为了设计 COVID-19 相关 ARDS 和非病毒性 ARDS 的多分子药物,我们通过比较 COVID-19 相关 ARDS 和非病毒性 ARDS 的核心信号通路,将核心信号通路作为发病机制的药物靶点进行鉴定。我们可以通过药物-靶点相互作用数据库预先训练药物-靶点相互作用的深度神经网络(DNN-DTI)模型,以预测鉴定的生物标志物的候选药物。我们进一步通过药物设计规范(包括调节能力、敏感性、排泄、毒性和类药性)的过滤器来缩小这些预测药物候选物,以确定潜在的多分子药物。综上所述,我们不仅阐明了 COVID-19 相关 ARDS 和非 SARS-CoV-2 相关 ARDS 的病因机制,还为 COVID-19 相关 ARDS 和非 SARS-CoV-2 相关 ARDS 提供了新的治疗选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bbd/8998971/ac8505722388/ijms-23-03649-g001.jpg

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