SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Author Summary

This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach

The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.

Drug repurposing: databases and pipelines

The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection

Introduction

Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear.

Methods

We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved.

Results

Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients.

Discussion

Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.

Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets

Background

Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.

Methods

We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.

Results

As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Three genes expressed in relation to lipid metabolism considered as potential biomarkers for the diagnosis and treatment of diabetic peripheral neuropathy

Diabetic neuropathy is one of the most common chronic complications and is present in approximately 50% of diabetic patients. A bioinformatic approach was used to analyze candidate genes involved in diabetic distal symmetric polyneuropathy and their potential mechanisms. GSE95849 was downloaded from the Gene Expression Omnibus database for differential analysis, together with the identified diabetic peripheral neuropathy-associated genes and the three major metabolism-associated genes in the CTD database to obtain overlapping Differentially Expressed Genes (DEGs). Gene Set Enrichment Analysis and Functional Enrichment Analysis were performed. Protein-Protein Interaction and hub gene networks were constructed using the STRING database and Cytoscape software. The expression levels of target genes were evaluated using GSE24290 samples, followed by Receiver operating characteristic, curve analysis. And Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the target genes. Finally, mRNA-miRNA networks were constructed. A total of 442 co-expressed DEGs were obtained through differential analysis, of which 353 expressed up-regulated genes and 89 expressed down-regulated genes. The up-regulated DEGs were involved in 742 GOs and 10 KEGG enrichment results, mainly associated with lipid metabolism-related pathways, TGF-β receptor signaling pathway, lipid transport, and PPAR signaling pathway. A total of 4 target genes (CREBBP, EP300, ME1, CD36) were identified. Analysis of subject operating characteristic curves indicated that CREBBP (AUC = 1), EP300 (AUC = 0.917), ME1 (AUC = 0.944) and CD36 (AUC = 1) may be candidate serum biomarkers for DPN. Conclusion: Diabetic peripheral neuropathy pathogenesis and progression is caused by multiple pathways, which also provides clinicians with potential therapeutic tools.

Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations

COVID-19 is an infectious disease caused by SARS-CoV-2 leading to the ongoing global pandemic. Infected patients developed a range of respiratory symptoms, including respiratory failure, as well as other extrapulmonary complications. Multiple comorbidities, including hypertension, diabetes, cardiovascular diseases, and chronic kidney diseases, are associated with the severity and increased mortality of COVID-19. SARS-CoV-2 infection also causes a range of cardiovascular complications, including myocarditis, myocardial injury, heart failure, arrhythmias, acute coronary syndrome, and venous thromboembolism. Although a variety of methods have been developed and many clinical trials have been launched for drug repositioning for COVID-19, treatments that consider cardiovascular manifestations and cardiovascular disease comorbidities specifically are limited. In this review, we summarize recent advances in drug repositioning for COVID-19, including experimental drug repositioning, high-throughput drug screening, omics data-based, and network medicine-based computational drug repositioning, with particular attention on those drug treatments that consider cardiovascular manifestations of COVID-19. We discuss prospective opportunities and potential methods for repurposing drugs to treat cardiovascular complications of COVID-19.

Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology {AuQ: Edit OK?}, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins were generated to simulate specific organs and to predict treatment efficacy at the individual patient level {AuQ: Edit OK?}. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease

Acute respiratory distress syndrome (ARDS) is intricately linked with SARS-CoV-2-associated disease severity and mortality, especially in patients with co-morbidities. Lung tissue injury caused as a consequence of ARDS leads to fluid build-up in the alveolar sacs, which in turn affects oxygen supply from the capillaries. ARDS is a result of a hyperinflammatory, non-specific local immune response (cytokine storm), which is aggravated as the virus evades and meddles with protective anti-viral innate immune responses. Treatment and management of ARDS remain a major challenge, first, because the condition develops as the virus keeps replicating and, therefore, immunomodulatory drugs are required to be used with caution. Second, the hyperinflammatory responses observed during ARDS are quite heterogeneous and dependent on the stage of the disease and the clinical history of the patients. In this review, we present different anti-rheumatic drugs, natural compounds, monoclonal antibodies, and RNA therapeutics and discuss their application in the management of ARDS. We also discuss on the suitability of each of these drug classes at different stages of the disease. In the last section, we discuss the potential applications of advanced computational approaches in identifying reliable drug targets and in screening out credible lead compounds against ARDS.

Network modeling helps to tackle the complexity of drug-disease systems

From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.

Network-based drug repurposing for the treatment of COVID-19 patients in different clinical stages

In the severe acute respiratory coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need to develop effective treatments. Through a network-based drug repurposing approach, several effective drug candidates are identified for treating COVID-19 patients in different clinical stages. The proposed approach takes advantage of computational prediction methods by integrating publicly available clinical transcriptome and experimental data. We identify 51 drugs that regulate proteins interacted with SARS-CoV-2 protein through biological pathways against COVID-19, some of which have been experimented in clinical trials. Among the repurposed drug candidates, lovastatin leads to differential gene expression in clinical transcriptome for mild COVID-19 patients, and estradiol cypionate mainly regulates hormone-related biological functions to treat severe COVID-19 patients. Multi-target mechanisms of drug candidates are also explored. Erlotinib targets the viral protein interacted with cytokine and cytokine receptors to affect SARS-CoV-2 attachment and invasion. Lovastatin and testosterone block the angiotensin system to suppress the SARS-CoV-2 infection. In summary, our study has identified effective drug candidates against COVID-19 for patients in different clinical stages and provides comprehensive understanding of potential drug mechanisms.

Knowledge Mapping of Drug Repositioning's Theme and Development

Background

In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.

Methodology

The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.

Results

The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.

Conclusion

The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

Identification of Potential Repurposable Drugs in Alzheimer’s Disease Exploiting a Bioinformatics Analysis

Alzheimer’s disease (AD) is a neurologic disorder causing brain atrophy and the death of brain cells. It is a progressive condition marked by cognitive and behavioral impairment that significantly interferes with daily activities. AD symptoms develop gradually over many years and eventually become more severe, and no cure has been found yet to arrest this process. The present study is directed towards suggesting putative novel solutions and paradigms for fighting AD pathogenesis by exploiting new insights from network medicine and drug repurposing strategies. To identify new drug–AD associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the vicinity of disease-associated genes to drug targets in the human interactome. We complemented the analysis with an in silico validation of the candidate compounds through a gene set enrichment analysis, aiming to determine if the modulation of the gene expression induced by the predicted drugs could be counteracted by the modulation elicited by the disease. We identified some interesting compounds belonging to the beta-blocker family, originally approved for treating hypertension, such as betaxolol, bisoprolol, and metoprolol, whose connection with a lower risk to develop Alzheimer’s disease has already been observed. Moreover, our algorithm predicted multi-kinase inhibitors such as regorafenib, whose beneficial effects were recently investigated for neuroinflammation and AD pathology, and mTOR inhibitors such as sirolimus, whose modulation has been associated with AD.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities

Complex diseases are associated with a wide range of cellular, physiological, and clinical phenotypes. To advance our understanding of disease mechanisms and our ability to treat these diseases, it is critical to delineate the molecular basis and therapeutic avenues of specific disease phenotypes, especially those that are associated with multiple diseases. Inflammatory processes constitute one such prominent phenotype, being involved in a wide range of health problems including ischemic heart disease, stroke, cancer, diabetes mellitus, chronic kidney disease, non-alcoholic fatty liver disease, and autoimmune and neurodegenerative conditions. While hundreds of genes might play a role in the etiology of each of these diseases, isolating the genes involved in the specific phenotype (e.g., inflammation “component”) could help us understand the genes and pathways underlying this phenotype across diseases and predict potential drugs to target the phenotype. Here, we present a computational approach that integrates gene interaction networks, disease-/trait-gene associations, and drug-target information to accomplish this goal. We apply this approach to isolate gene signatures of complex diseases that correspond to chronic inflammation and use SAveRUNNER to prioritize drugs to reveal new therapeutic opportunities.

Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.

Old drugs, new tricks: leveraging known compounds to disrupt coronavirus-induced cytokine storm

A major complication in COVID-19 infection consists in the onset of acute respiratory distress fueled by a dysregulation of the host immune network that leads to a run-away cytokine storm. Here, we present an in silico approach that captures the host immune system’s complex regulatory dynamics, allowing us to identify and rank candidate drugs and drug pairs that engage with minimal subsets of immune mediators such that their downstream interactions effectively disrupt the signaling cascades driving cytokine storm. Drug–target regulatory interactions are extracted from peer-reviewed literature using automated text-mining for over 5000 compounds associated with COVID-induced cytokine storm and elements of the underlying biology. The targets and mode of action of each compound, as well as combinations of compounds, were scored against their functional alignment with sets of competing model-predicted optimal intervention strategies, as well as the availability of like-acting compounds and known off-target effects. Top-ranking individual compounds identified included a number of known immune suppressors such as calcineurin and mTOR inhibitors as well as compounds less frequently associated for their immune-modulatory effects, including antimicrobials, statins, and cholinergic agonists. Pairwise combinations of drugs targeting distinct biological pathways tended to perform significantly better than single drugs with dexamethasone emerging as a frequent high-ranking companion. While these predicted drug combinations aim to disrupt COVID-induced acute respiratory distress syndrome, the approach itself can be applied more broadly to other diseases and may provide a standard tool for drug discovery initiatives in evaluating alternative targets and repurposing approved drugs.

Repositioning Drugs for Rare Diseases Based on Biological Features and Computational Approaches

Rare diseases are a group of uncommon diseases in the world population. To date, about 7000 rare diseases have been documented. However, most of them do not have a known treatment. As a result of the relatively low demand for their treatments caused by their scarce prevalence, the pharmaceutical industry has not sufficiently encouraged the research to develop drugs to treat them. This work aims to analyse potential drug-repositioning strategies for this kind of disease. Drug repositioning seeks to find new uses for existing drugs. In this context, it seeks to discover if rare diseases could be treated with medicines previously indicated to heal other diseases. Our approaches tackle the problem by employing computational methods that calculate similarities between rare and non-rare diseases, considering biological features such as genes, proteins, and symptoms. Drug candidates for repositioning will be checked against clinical trials found in the scientific literature. In this study, 13 different rare diseases have been selected for which potential drugs could be repositioned. By verifying these drugs in the scientific literature, successful cases were found for 75% of the rare diseases studied. The genetic associations and phenotypical features of the rare diseases were examined. In addition, the verified drugs were classified according to the anatomical therapeutic chemical (ATC) code to highlight the types with a higher predisposition to be repositioned. These promising results open the door for further research in this field of study.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform

INTRODUCTION

As a mid-size international pharmaceutical company, we initiated four years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named "Patrimony" was built-up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of Artificial Intelligence.

AREAS COVERED

Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in Immuno-inflammatory diseases, and current ongoing extension to applications to Oncology and Neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates.

EXPERT OPINION

We report our achievements, but also our challenges in implementing data access and governance processes, building-up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

Network approaches for modeling the effect of drugs and diseases

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

Repurposing Histaminergic Drugs in Multiple Sclerosis

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1–H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug–drug interaction network and disease similarity network are constructed according to the drug–gene and disease–gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method

BACKGROUND

Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication.

MOTIVATION

Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage.

METHOD

We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies.

RESULTS

There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

A Network-Biology led Computational Drug repurposing Strategy to prioritize therapeutic options for COVID-19

The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs.

Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects

Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.

A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases

Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit.

Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of GJA1 is the highest among all potential driver genes of AD. Moreover, we found GJA1 interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene GJA1 is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

Comprehensive characterization of human–virus protein-protein interactions reveals disease comorbidities and potential antiviral drugs

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Comprehensive collection of experimentally verified human–virus PPIs.

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Viral proteins interact with cell cycle and immune-related genes.

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Viral proteins are likely to interact with central human proteins in PPI network.

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Network analysis reveals associations between viral infections and human diseases.

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Potential anti-viral drugs are prioritized based on interactome analysis.

The protein-protein interactions (PPIs) between human and viruses play important roles in viral infection and host immune responses. Rapid accumulation of experimentally validated human–virus PPIs provides an unprecedented opportunity to investigate the regulatory pattern of viral infection. However, we are still lack of knowledge about the regulatory patterns of human–virus interactions. We collected 27,293 experimentally validated human–virus PPIs, covering 8 virus families, 140 viral proteins and 6059 human proteins. Functional enrichment analysis revealed that the viral interacting proteins were likely to be enriched in cell cycle and immune-related pathways. Moreover, we analysed the topological features of the viral interacting proteins and found that they were likely to locate in central regions of human PPI network. Based on network proximity analyses of diseases genes and human–virus interactions in the human interactome, we revealed the associations between complex diseases and viral infections. Network analysis also implicated potential antiviral drugs that were further validated by text mining. Finally, we presented the Human–Virus Protein-Protein Interaction database (HVPPI, http://bio-bigdata.hrbmu.edu.cn/HVPPI), that provides experimentally validated human–virus PPIs as well as seamlessly integrates online functional analysis tools. In summary, comprehensive understanding the regulatory pattern of human–virus interactome will provide novel insights into fundamental infectious mechanism discovery and new antiviral therapy development.

Novel cancer subtyping method based on patient-specific gene regulatory network

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

Verification of the Potential Targets of the Herbal Prescription Sochehwan for Drug Repurposing Processes as Deduced by Network Pharmacology

Network pharmacology (NP) is a useful, emerging means of understanding the complex pharmacological mechanisms of traditional herbal medicines. Sochehwan (SCH) is a candidate herbal prescription for drug repurposing as it has been suggested to have beneficial effects on metabolic syndrome. In this study, NP was adopted to complement the shortcomings of literature-based drug repurposing strategies in traditional herbal medicine. We conducted in vitro studies to confirm the effects of SCH on potential pharmacological targets identified by NP analysis. Herbal compounds and molecular targets of SCH were explored and screened from a traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and an oriental medicine advanced searching integrated system (OASIS). Forty-seven key targets selected from a protein-protein interaction (PPI) network were analyzed with gene ontology (GO) term enrichment and KEGG pathway enrichment analysis to identify relevant categories. The tumor necrosis factor (TNF) and mitogen-activated protein kinase (MAPK) signaling pathways were presented as significant signaling pathways with lowest p-values by NP analysis, which were downregulated by SCH treatment. The signal transducer and activator of transcription 3 (STAT3) was identified as a core key target by NP analysis, and its phosphorylation ratio was confirmed to be significantly suppressed by SCH. In conclusion, the NP-based approach used for target prediction and experimental data obtained from Raw 264.7 cells strongly suggested that SCH can attenuate inflammatory status by modulating the phosphorylation status of STAT3.

Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis

Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of this virus is very similar to the SARS-CoV-2. Therefore, the study of SARS-CoV disease and the identification of effective drugs to treat this disease can be new clues for the treatment of SARS-Cov-2. This study aimed to discover novel potential drugs for SARS-CoV disease in order to treating SARS-Cov-2 disease based on a novel systems biology approach. To this end, gene co-expression network analysis was applied. First, the gene co-expression network was reconstructed for 1441 genes, and then two gene modules were discovered as significant modules. Next, a list of miRNAs and transcription factors that target gene co-expression modules' genes were gathered from the valid databases, and two sub-networks formed of transcription factors and miRNAs were established. Afterward, the list of the drugs targeting obtained sub-networks' genes was retrieved from the DGIDb database, and two drug-gene and drug-TF interaction networks were reconstructed. Finally, after conducting different network analyses, we proposed five drugs, including FLUOROURACIL, CISPLATIN, SIROLIMUS, CYCLOPHOSPHAMIDE, and METHYLDOPA, as candidate drugs for SARS-CoV-2 coronavirus treatment. Moreover, ten miRNAs including miR-193b, miR-192, miR-215, miR-34a, miR-16, miR-16, miR-92a, miR-30a, miR-7, and miR-26b were found to be significant miRNAs in treating SARS-CoV-2 coronavirus.

Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents

Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It​ offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus–drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

Drug repositioning by merging active subnetworks validated in cancer and COVID-19

Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.

Integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action

An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the primary drug targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary, pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is a first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

A data-driven methodology towards evaluating the potential of drug repurposing hypotheses

Drug repurposing has become a widely used strategy to accelerate the process of finding treatments. While classical de novo drug development involves high costs, risks, and time-consuming paths, drug repurposing allows to reuse already-existing and approved drugs for new indications. Numerous research has been carried out in this field, both in vitro and in silico. Computational drug repurposing methods make use of modern heterogeneous biomedical data to identify and prioritize new indications for old drugs. In the current paper, we present a new complete methodology to evaluate new potentially repurposable drugs based on disease-gene and disease-phenotype associations, identifying significant differences between repurposing and non-repurposing data. We have collected a set of known successful drug repurposing case studies from the literature and we have analysed their dissimilarities with other biomedical data not necessarily participating in repurposing processes. The information used has been obtained from the DISNET platform. We have performed three analyses (at the genetical, phenotypical, and categorization levels), to conclude that there is a statistically significant difference between actual repurposing-related information and non-repurposing data. The insights obtained could be relevant when suggesting new potential drug repurposing hypotheses.

Identification of fibronectin 1 (FN1) and complement component 3 (C3) as immune infiltration-related biomarkers for diabetic nephropathy using integrated bioinformatic analysis

Immune cell infiltration (ICI) plays a pivotal role in the development of diabetic nephropathy (DN). Evidence suggests that immune-related genes play an important role in the initiation of inflammation and the recruitment of immune cells. However, the underlying mechanisms and immune-related biomarkers in DN have not been elucidated. Therefore, this study aimed to explore immune-related biomarkers in DN and the underlying mechanisms using bioinformatic approaches. In this study, four DN glomerular datasets were downloaded, merged, and divided into training and test cohorts. First, we identified 55 differentially expressed immune-related genes; their biological functions were mainly enriched in leukocyte chemotaxis and neutrophil migration. The CIBERSORT algorithm was then used to evaluate the infiltrated immune cells; macrophages M1/M2, T cells CD8, and resting mast cells were strongly associated with DN. The ICI-related gene modules as well as 25 candidate hub genes were identified to construct a protein-protein interactive network and conduct molecular complex detection using the GOSemSim algorithm. Consequently, FN1, C3, and VEGFC were identified as immune-related biomarkers in DN, and a related transcription factor-miRNA-target network was constructed. Receiver operating characteristic curve analysis was estimated in the test cohort; FN1 and C3 had large area under the curve values (0.837 and 0.824, respectively). Clinical validation showed that FN1 and C3 were negatively related to the glomerular filtration rate in patients with DN. Six potential therapeutic small molecule compounds, such as calyculin, phenamil, and clofazimine, were discovered in the connectivity map. In conclusion, FN1 and C3 are immune-related biomarkers of DN.

In silico drug repurposing in COVID-19: A network-based analysis

The SARS-CoV-2 pandemic is a worldwide public health emergency. Despite the beginning of a vaccination campaign, the search for new drugs to appropriately treat COVID-19 patients remains a priority. Drug repurposing represents a faster and cheaper method than de novo drug discovery. In this study, we examined three different network-based approaches to identify potentially repurposable drugs to treat COVID-19. We analyzed transcriptomic data from whole blood cells of patients with COVID-19 and 21 other related conditions, as compared with those of healthy subjects. In addition to conventionally used drugs (e.g., anticoagulants, antihistaminics, anti-TNFα antibodies, corticosteroids), unconventional candidate compounds, such as SCN5A inhibitors and drugs active in the central nervous system, were identified. Clinical judgment and validation through clinical trials are always mandatory before use of the identified drugs in a clinical setting.

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1β, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.

Drug Repurposing: A Network-based Approach to Amyotrophic Lateral Sclerosis

The continuous adherence to the conventional "one target, one drug" paradigm has failed so far to provide effective therapeutic solutions for heterogeneous and multifactorial diseases as amyotrophic lateral sclerosis (ALS), a rare progressive and chronic, debilitating neurological disease for which no cure is available. The present study is aimed at finding innovative solutions and paradigms for therapy in ALS pathogenesis, by exploiting new insights from Network Medicine and drug repurposing strategies. To identify new drug-ALS disease associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the proximity of disease-associated genes to drug targets in the human interactome. We prioritized 403 SAveRUNNER-predicted drugs according to decreasing values of network similarity with ALS. Among catecholamine, dopamine, serotonin, histamine, and GABA receptor modulators, as well as angiotensin-converting enzymes, cyclooxygenase isozymes, and serotonin transporter inhibitors, we found some interesting no customary ALS drugs, including amoxapine, clomipramine, mianserin, and modafinil. Furthermore, we strengthened the SAveRUNNER predictions by a gene set enrichment analysis that confirmed modafinil as a drug with the highest score among the 121 identified drugs with a score > 0. Our results contribute to gathering further proofs of innovative solutions for therapy in ALS pathogenesis.

The risk of pancreatic adenocarcinoma following SARS-CoV family infection

COVID 19 disease has become a global catastrophe over the past year that has claimed the lives of over two million people around the world. Despite the introduction of vaccines against the disease, there is still a long way to completely eradicate it. There are concerns about the complications following infection with SARS-CoV-2. This research aimed to evaluate the possible correlation between infection with SARS-CoV viruses and cancer in an in-silico study model. To do this, the relevent dataset was selected from GEO database. Identification of differentially expressed genes among defined groups including SARS-CoV, SARS-dORF6, SARS-BatSRBD, and H1N1 were screened where the |Log FC| ≥ 1and p < 0.05 were considered statistically significant. Later, the pathway enrichment analysis and gene ontology (GO) were used by Enrichr and Shiny GO databases. Evaluation with STRING online was applied to predict the functional interactions of proteins, followed by Cytoscape analysis to identify the master genes. Finally, analysis with GEPIA2 server was carried out to reveal the possible correlation between candidate genes and cancer development. The results showed that the main molecular function of up- and down-regulated genes was "double-stranded RNA binding" and actin-binding, respectively. STRING and Cytoscape analysis presented four genes, PTEN, CREB1, CASP3, and SMAD3 as the key genes involved in cancer development. According to TCGA database results, these four genes were up-regulated notably in pancreatic adenocarcinoma. Our findings suggest that pancreatic adenocarcinoma is the most probably malignancy happening after infection with SARS-CoV family.