Prediction of severe adverse events, modes of action and drug treatments for COVID-19’s complications

AbstractFollowing SARS-CoV-2 infection, some COVID-19 patients experience severe adverse events caused by pathogenic host responses. To treat these complications, their underlying etiology must be identified. Thus, a novel AI-based methodology, MOATAI-VIR, which predicts disease-protein-pathway relationships for 22 clinical manifestations attributed to COVID-19 was developed. SARS-CoV-2 interacting human proteins and GWAS identified respiratory failure associated risk genes provide the input from which the mode-of-action (MOA) proteins/pathways of the resulting disease comorbidities are predicted. These comorbidities are then mapped to their clinical manifestations. Three uncharacterized manifestation categories are found: neoplasms, mental and behavioral disorders, and congenital malformations, deformations, and chromosomal abnormalities. The prevalence of neoplasms suggests a possible association between COVID-19 and cancer, whether by shared molecular mechanisms between oncogenesis and viral replication, or perhaps, SARS-CoV-2 is an oncovirus. To assess the molecular basis of each manifestation, the proteins shared across each group of comorbidities were prioritized and subject to global pathway analysis. From these most frequent pathways, the molecular features associated with hallmark COVID-19 phenotypes, such as loss of sense of smell/taste, unusual neurological symptoms, cytokine storm, and blood clots were explored. Results of MOATAI-VIR are available for academic users at: http://pwp.gatech.edu/cssb/MOATAI-VIR/.

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Recent Advances in Multi-target Anti-Alzheimer Disease Compounds (2013 Up to the Present)

Current Medicinal Chemistry ◽

10.2174/0929867326666181203124102 ◽

2019 ◽

Vol 26 (30) ◽

pp. 5684-5710 ◽

Cited By ~ 8

Author(s):

Ning Wang ◽

Panpan Qiu ◽

Wei Cui ◽

Xiaojun Yan ◽

Bin Zhang ◽

...

Keyword(s):

Molecular Mechanisms ◽

Amyloid Β ◽

Amyloid Β Peptide ◽

Chemical Structures ◽

Target Drug ◽

Single Target ◽

Approved Drugs ◽

Multiple Drugs ◽

Fda Approved Drugs ◽

Ad Therapy

: Since the last century, when scientists proposed the lock-and-key model, the discovery of drugs has focused on the development of drugs acting on single target. However, single-target drug therapies are not effective to complex diseases with multi-factorial pathogenesis. Moreover, the combination of single-target drugs readily causes drug resistance and side effects. In recent years, multi-target drugs have increasingly been represented among FDA-approved drugs. Alzheimer’s Disease (AD) is a complex and multi-factorial disease for which the precise molecular mechanisms are still not fully understood. In recent years, rational multi-target drug design methods, which combine the pharmacophores of multiple drugs, have been increasingly applied in the development of anti-AD drugs. In this review, we give a brief description of the pathogenesis of AD and provide detailed discussions about the recent development of chemical structures of anti-AD agents (2013 up to present) that have multiple targets, such as amyloid-β peptide, Tau protein, cholinesterases, monoamine oxidase, β-site amyloid-precursor protein-cleaving enzyme 1, free radicals, metal ions (Fe2+, Cu2+, Zn2+) and so on. In this paper, we also added some novel targets or possible pathogenesis which have been reported in recent years for AD therapy. We hope that these findings may provide new perspectives for the pharmacological treatment of AD.

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Repurposing of FDA-Approved Drugs against Mycobacterium tuberculosis Target MMA4 and CmaA2

Biointerface Research in Applied Chemistry ◽

10.33263/briac116.1468814696 ◽

2021 ◽

Vol 11 (6) ◽

pp. 14688-14696

Keyword(s):

Mycobacterium Tuberculosis ◽

Bacterial Infections ◽

Clinical Manifestations ◽

Mycolic Acid ◽

Causative Organism ◽

In Silico Study ◽

Latent Tb ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Tuberculosis Disease

Tuberculosis (TB) is one of the most dreadful and deadliest diseases, killing millions annually. Its causative organism is a bacterium called Mycobacterium tuberculosis which primarily attacks the lungs. Tuberculosis can be classified as latent and active based on the presence/absence of the clinical manifestations. Also known as active TB, pulmonary TB is characterized by extreme infection, whereas in latent TB, no infection or symptom is seen. In this in silico study, we focus on the molecular docking-based virtual screening of 10 FDA-approved drugs which already used to treat bacterial infections against target methoxy mycolic acid synthase 4 (MMA4) and cyclopropane mycolic acid synthase (CmaA2). Drug-resistant TB is the most challenging factor for the design and formulation of anti-tuberculosis drugs. Mycolic acid plays a crucial role in the pathogenesis of TB and, therefore, can be an extremely valuable target. Based on a study conducted on mice, by rendering the hma genes (MMA4 and cmaA) inactive, no synthesis of mycolic acid is observed. The binding energy scores of each ligand docked against the target shows the affinity that happens against the Tuberculosis disease.

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Leveraging Genomics, Transcriptomics, and Epigenomics to Understand the Biology and Chemoresistance of Ovarian Cancer

Cancers ◽

10.3390/cancers13164029 ◽

2021 ◽

Vol 13 (16) ◽

pp. 4029

Author(s):

Sandra Muñoz-Galván ◽

Amancio Carnero

Keyword(s):

Ovarian Cancer ◽

Molecular Mechanisms ◽

Clinical Manifestations ◽

Therapy Resistance ◽

Omics Technologies ◽

Gynecological Malignancy ◽

Tumor Relapse ◽

Molecular Features ◽

Ovarian Cancers ◽

Late Detection

Ovarian cancer is a major cause of fatality due to a gynecological malignancy. This lethality is largely due to the unspecific clinical manifestations of ovarian cancer, which lead to late detection and to high resistance to conventional therapies based on platinum. In recent years, we have advanced our understanding of the mechanisms provoking tumor relapse, and the advent of so-called omics technologies has provided exceptional tools to evaluate molecular mechanisms leading to therapy resistance in ovarian cancer. Here, we review the contribution of genomics, transcriptomics, and epigenomics techniques to our knowledge about the biology and molecular features of ovarian cancers, with a focus on therapy resistance. The use of these technologies to identify molecular markers and mechanisms leading to chemoresistance in these tumors is discussed, as well as potential further applications.

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Computational analysis of human host binding partners of chikungunya and dengue viruses during coinfection

Pathogens and Disease ◽

10.1093/femspd/ftab046 ◽

2021 ◽

Vol 79 (8) ◽

Author(s):

Ritu Ghildiyal ◽

Reema Gabrani

Keyword(s):

Clinical Manifestations ◽

Host Protein ◽

Severe Illness ◽

P53 Signaling ◽

Clinical Complications ◽

Binding Partners ◽

Viral Interactions ◽

Human Proteins ◽

Approved Drugs ◽

Regulation Of Cell Cycle

ABSTRACT Mosquito-borne viral diseases like chikungunya and dengue infections can cause severe illness and have become major public health concerns. Chikungunya virus (CHIKV) and dengue virus (DENV) infections share similar primary clinical manifestations and are transmitted by the same vector. Thus, the probability of their coinfection gets increased with more severe clinical complications in the patients. The present study was undertaken to elucidate the common human interacting partners of CHIKV and DENV proteins during coinfection. The viral–host protein–protein interactome was constructed using Cytoscape. Subsequently, significant host interactors were identified during coinfection. The network analysis elucidated 57 human proteins interacting with both CHIKV and DENV, represented as hub-bottlenecks. The functional and biological analyses of the 40 hub-bottlenecks revealed that they are associated with phosphoinositide 3-kinases (PI3K)/AKT, p53 signaling pathways, regulation of cell cycle and apoptosis during coinfection. Moreover, the molecular docking analysis uncovered the tight and robust binding of selected hub-bottlenecks with CHIKV/DENV proteins. Additionally, 23 hub-bottlenecks were predicted as druggable candidates that could be targeted to eradicate the host–viral interactions. The elucidated common host binding partners during DENV and CHIKV coinfection as well as indicated approved drugs can support the therapeutics development.

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Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

10.26434/chemrxiv.12366938.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sugandh Kumar ◽

Pratima Kumari ◽

Geetanjali Agnihotri ◽

Preethy VijayKumar ◽

Shaheerah Khan ◽

...

Keyword(s):

Interaction Network ◽

Viral Proteins ◽

Inhibitory Effect ◽

Host Proteins ◽

Disease Manifestation ◽

Protein Protein Interaction ◽

Human Proteins ◽

Approved Drugs ◽

Viral Lifecycle ◽

Fda Approved Drugs

The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect. Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.

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Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

10.26434/chemrxiv.12366938 ◽

2020 ◽

Author(s):

Sugandh Kumar ◽

Pratima Kumari ◽

Geetanjali Agnihotri ◽

Preethy VijayKumar ◽

Shaheerah Khan ◽

...

Keyword(s):

Interaction Network ◽

Viral Proteins ◽

Inhibitory Effect ◽

Host Proteins ◽

Disease Manifestation ◽

Protein Protein Interaction ◽

Human Proteins ◽

Approved Drugs ◽

Viral Lifecycle ◽

Fda Approved Drugs

The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect. Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.

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High-Throughput Phenotyping Toolkit for Characterizing Cellular Models of Hypertrophic Cardiomyopathy in Vitro

Methods and Protocols ◽

10.3390/mps2040083 ◽

2019 ◽

Vol 2 (4) ◽

pp. 83 ◽

Cited By ~ 4

Author(s):

Mosqueira ◽

Lis-Slimak ◽

Denning

Keyword(s):

Hypertrophic Cardiomyopathy ◽

High Throughput ◽

Molecular Mechanisms ◽

Clinical Manifestations ◽

Cellular Models ◽

Molecular Features ◽

High Throughput Drug Screening ◽

High Throughput Phenotyping ◽

Screening Approaches

Hypertrophic cardiomyopathy (HCM) is a prevalent and complex cardiovascular disease characterised by multifarious hallmarks, a heterogeneous set of clinical manifestations, and several molecular mechanisms. Various disease models have been developed to study this condition, but they often show contradictory results, due to technical constraints and/or model limitations. Therefore, new tools are needed to better investigate pathological features in an unbiased and technically refined approach, towards improving understanding of disease progression. Herein, we describe three simple protocols to phenotype cellular models of HCM in vitro, in a high-throughput manner where technical artefacts are minimized. These are aimed at investigating: (1) Hypertrophy, by measuring cell volume by flow cytometry; (2) HCM molecular features, through the analysis of a hypertrophic marker, multinucleation, and sarcomeric disarray by high-content imaging; and (3) mitochondrial respiration and content via the Seahorse™ platform. Collectively, these protocols comprise straightforward tools to evaluate molecular and functional parameters of HCM phenotypes in cardiomyocytes in vitro. These facilitate greater understanding of HCM and high-throughput drug screening approaches and are accessible to all researchers of cardiac disease modelling. Whilst HCM is used as an exemplar, the approaches described are applicable to other cellular models where the investigation of identical biological changes is paramount.

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Identification of drugs targeting multiple viral and human proteins using computational analysis for repurposing against COVID-19

10.21203/rs.3.rs-101778/v1 ◽

2020 ◽

Author(s):

Sugandh Kumar ◽

Pratima Kumari ◽

Geetanjali Agnihotri ◽

Preethy V Kumar ◽

Bharti Singh ◽

...

Keyword(s):

Clinical Trial ◽

Differentially Expressed Genes ◽

Computational Analysis ◽

State Of The Art ◽

Viral Proteins ◽

Host Proteins ◽

Disease Manifestation ◽

Human Proteins ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract The SARS-CoV2 is a highly contagious pathogen that causes COVID-19 disease. It has affected millions of people globally with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against nine SARS-CoV2 proteins to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Additionally, we analyzed if the identified molecules can also affect the human proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) as a result of SARS-CoV2 infection were identified using NCBI-GEO data (GEO-ID: GSE-147507). Targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 74 molecules that can bind to various SARS-CoV2 and human host proteins. Their possible use in COVID-19 have also been reviewed in detail. We hope that this study will help development of multipotent drugs, simultaneously targeting the viral and host proteins, for the treatment of COVID-19.

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Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS

10.21203/rs.3.rs-25643/v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Sinead M O’Donovan ◽

Hunter Eby ◽

Nicholas D Henkel ◽

Justin Creeden ◽

Ali Imami ◽

...

Keyword(s):

Protein Kinase ◽

Kinase Inhibitors ◽

Protein Kinase Inhibitors ◽

Disease Spread ◽

The Novel ◽

Integrated Network ◽

Drug Treatments ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Bioinformatics Workflow

Abstract The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.

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