In Silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

There is pressing urgency to better understand the immunological underpinnings of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in order to identify potential therapeutic targets and drugs that allow treating patients effectively. To fill in this gap, we performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. As results, the high-confidence protein interactome network was conformed by 1,588 nodes between immune system proteins and human proteins physically associated with SARS-CoV-2. Subsequently, we screened all these nodes in ACE2 and TMPRSS2 co-expressing cells according to the Alexandria Project, finding 75 potential therapeutic targets significantly overexpressed (Z score > 2) in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs for 25 of the 75 aforementioned proteins. On one hand, we obtained 45 approved drugs, 16 compounds under investigation, and 35 experimental compounds with the highest area under the receiver operating characteristic (AUROCs) for 15 immune system proteins. On the other hand, we obtained 4 approved drugs, 9 compounds under investigation, and 16 experimental compounds with the highest multi-target affinities for 9 immune system proteins. In conclusion, computational structure-based drug discovery focused on immune system proteins is imperative to select potential drugs that, after being effectively analyzed in cell lines and clinical trials, these can be considered for treatment of complex symptoms of COVID-19 patients, and for co-therapies with drugs directly targeting SARS-CoV-2.

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In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Frontiers in Pharmacology ◽

10.3389/fphar.2021.598925 ◽

2021 ◽

Vol 12 ◽

Author(s):

Andrés López-Cortés ◽

Patricia Guevara-Ramírez ◽

Nikolaos C. Kyriakidis ◽

Carlos Barba-Ostria ◽

Ángela León Cáceres ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Immune System ◽

In Silico ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Human Tissues ◽

Interactome Network ◽

Protein Interactome ◽

Single Cell Rna Sequencing

Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively.Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19.Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics.Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of severe COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.

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Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

10.26434/chemrxiv.12449081.v1 ◽

2020 ◽

Author(s):

Scott Bembenek

Keyword(s):

Molecular Modeling ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Preclinical Research ◽

Main Protease ◽

Novel Coronavirus ◽

Approved Drugs ◽

Usual Process ◽

Potential Inhibitors ◽

Computational Platform

The recent outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (Mpro, 3CLpro). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.

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Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

10.26434/chemrxiv.12449081 ◽

2020 ◽

Author(s):

Scott Bembenek

Keyword(s):

Molecular Modeling ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Preclinical Research ◽

Main Protease ◽

Novel Coronavirus ◽

Approved Drugs ◽

Usual Process ◽

Potential Inhibitors ◽

Computational Platform

The recent outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (Mpro, 3CLpro). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.

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Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing

Microbiology Spectrum ◽

10.1128/spectrum.01018-21 ◽

2021 ◽

Author(s):

J. Eduardo Martinez-Hernandez ◽

Zaynab Hammoud ◽

Alessandra Mara de Sousa ◽

Frank Kramer ◽

Rubens L. do Monte-Neto ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Biological Process ◽

Expression Profiles ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Novel Approach ◽

Gene Regulatory ◽

Molecular Functions ◽

Approved Drugs

This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles.

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Identification of key regulators responsible for dysregulated networks in osteoarthritis by large-scale expression analysis

10.21203/rs.3.rs-47411/v2 ◽

2020 ◽

Author(s):

Song Shi ◽

Fuyin Wan ◽

Zhenyu Zhou ◽

Ran Tao ◽

Yue Lu ◽

...

Keyword(s):

Expression Analysis ◽

Cardiac Glycosides ◽

Large Scale ◽

Bone Development ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Functional Enrichment ◽

Hub Genes ◽

Human Cartilage ◽

Approved Drugs

Abstract Background: Osteoarthritis (OA) is a worldwide musculoskeletal disorder. However, disease-modifying therapies for OA are not available. Here, we aimed to characterize the molecular signatures of OA and to identify novel therapeutic targets and strategies to improve the treatment of OA. Methods: We collected genome-wide transcriptome data performed on 132 OA and 74 normal human cartilage or synovium tissues from 7 independent datasets. Differential gene expression analysis and functional enrichment were performed to identify genes and pathways that were dysregulated in OA. The computational drug repurposing method was used to uncover drugs that could be repurposed to treat OA.Results: We identified several pathways associated with the development of OA, such as extracellular matrix organization, inflammation, bone development, and ossification. By protein-protein interaction (PPI) network analysis, we prioritized several hub genes, such as JUN, CDKN1A,VEGFA, and FOXO3. Moreover, we repurposed several FDA-approved drugs, such as cardiac glycosides, that could be used in the treatment of OA. Conclusions: We proposed that the hub genes we identified would play a role in cartilage homeostasis and could be important diagnostic and therapeutic targets. Drugs such as cardiac glycosides provided new possibilities for the treatment of OA.

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Identification of key regulators responsible for dysregulated networks in osteoarthritis by large-scale expression analysis

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-021-02402-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Song Shi ◽

Fuyin Wan ◽

Zhenyu Zhou ◽

Ran Tao ◽

Yue Lu ◽

...

Keyword(s):

Expression Analysis ◽

Cardiac Glycosides ◽

Large Scale ◽

Bone Development ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Functional Enrichment ◽

Hub Genes ◽

Human Cartilage ◽

Approved Drugs

Abstract Background Osteoarthritis (OA) is a worldwide musculoskeletal disorder. However, disease-modifying therapies for OA are not available. Here, we aimed to characterize the molecular signatures of OA and to identify novel therapeutic targets and strategies to improve the treatment of OA. Methods We collected genome-wide transcriptome data performed on 132 OA and 74 normal human cartilage or synovium tissues from 7 independent datasets. Differential gene expression analysis and functional enrichment were performed to identify genes and pathways that were dysregulated in OA. The computational drug repurposing method was used to uncover drugs that could be repurposed to treat OA. Results We identified several pathways associated with the development of OA, such as extracellular matrix organization, inflammation, bone development, and ossification. By protein-protein interaction (PPI) network analysis, we prioritized several hub genes, such as JUN, CDKN1A, VEGFA, and FOXO3. Moreover, we repurposed several FDA-approved drugs, such as cardiac glycosides, that could be used in the treatment of OA. Conclusions We proposed that the hub genes we identified would play a role in cartilage homeostasis and could be important diagnostic and therapeutic targets. Drugs such as cardiac glycosides provided new possibilities for the treatment of OA.

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Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

10.26434/chemrxiv.12449081.v2 ◽

2020 ◽

Author(s):

Scott Bembenek

Keyword(s):

Molecular Modeling ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Preclinical Research ◽

Main Protease ◽

Novel Coronavirus ◽

Approved Drugs ◽

Usual Process ◽

Potential Inhibitors ◽

Computational Platform

The recent outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (Mpro, 3CLpro). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.

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Structural Basis of Antisickling Effects of Selected FDA Approved Drugs: A Drug Repurposing Study

Current Computer - Aided Drug Design ◽

10.2174/1573409914666180129163711 ◽

2018 ◽

Vol 14 (2) ◽

pp. 106-116 ◽

Cited By ~ 1

Author(s):

Olujide O. Olubiyi ◽

Maryam O. Olagunju ◽

James O. Oni ◽

Abidemi O. Olubiyi

Keyword(s):

Drug Repurposing ◽

Structural Basis ◽

Approved Drugs ◽

Fda Approved Drugs

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Screening of an FDA-Approved Library for Novel Drugs against Y. pestis

Antibiotics ◽

10.3390/antibiotics10010040 ◽

2021 ◽

Vol 10 (1) ◽

pp. 40

Author(s):

David Gur ◽

Theodor Chitlaru ◽

Emanuelle Mamroud ◽

Ayelet Zauberman

Keyword(s):

Drug Repurposing ◽

Mortality Rates ◽

Systematic Screening ◽

Antibiotic Resistant ◽

Gram Negative ◽

Therapy Initiation ◽

Novel Drugs ◽

Resistant Strains ◽

Approved Drugs ◽

Fda Approved Drugs

Yersinia pestis is a Gram-negative pathogen that causes plague, a devastating disease that kills millions worldwide. Although plague is efficiently treatable by recommended antibiotics, the time of antibiotic therapy initiation is critical, as high mortality rates have been observed if treatment is delayed for longer than 24 h after symptom onset. To overcome the emergence of antibiotic resistant strains, we attempted a systematic screening of Food and Drug Administration (FDA)-approved drugs to identify alternative compounds which may possess antibacterial activity against Y. pestis. Here, we describe a drug-repurposing approach, which led to the identification of two antibiotic-like activities of the anticancer drugs bleomycin sulfate and streptozocin that have the potential for designing novel antiplague therapy approaches. The inhibitory characteristics of these two drugs were further addressed as well as their efficiency in affecting the growth of Y. pestis strains resistant to doxycycline and ciprofloxacin, antibiotics recommended for plague treatment.

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