Drug repurposing in idiopathic pulmonary fibrosis filtered by a bioinformatics-derived composite score

Identification of Potential Differentially-Methylated/Expressed Genes in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. DNA methylation can regulate gene expression. Understanding the potential molecular mechanism of COPD is of great importance. The aim of this study was to find differentially methylated/expressed genes in COPD. DNA methylation and gene expression profiles in COPD were downloaded from the dataset, followed by functional analysis of differentially-methylated/expressed genes. The potential diagnostic value of these differentially-methylated/expressed genes was determined by receiver operating characteristic (ROC) analysis. Expression validation of differentially-methylated/expressed genes was performed by in vitro experiment and extra online datasets. Totally, 81 hypermethylated-low expression genes and 121 hypomethylated-high expression genes were found in COPD. Among which, 9 core hypermethylated-low expression genes (CD247, CCR7, CD5, IKZF1, SLAMF1, IL2RB, CD3E, CD7 and IL7R) and 8 core hypomethylated-high expression genes (TREM1, AQP9, CD300LF, CLEC12A, NOD2, IRAK3, NLRP3 and LYZ) were identified in the protein-protein interaction (PPI) network. Moreover, these genes had a potential diagnostic utility for COPD. Some signaling pathways were identified in COPD, including T cell receptor signaling pathway, cytokine-cytokine receptor interaction, hematopoietic cell lineage, HTLV-I infection, endocytosis and Jak-STAT signaling pathway. In conclusion, differentially-methylated/expressed genes and involved signaling pathways are likely to be associated with the process of COPD.

Niclosamide Attenuates Inflammation-Associated Profibrotic Responses in Human Subepithelial Lung Myofibroblasts

Niclosamide is a commonly used helminthicidic drug for the treatment of human parasitosis by helminths. Recently, efforts have been focusing on repurposing this drug for the treatment of other diseases, such as idiopathic pulmonary fibrosis. Subepithelial lung myofibroblasts (SELMs) isolated from tissue biopsies of patients undergoing surgery for lung cancer were stimulated with TNF-α (50 ng/mL), IL-1α (5 ng/mL), added alone or in combination, and TGF-β₁ (5 ng/mL). After treatment with niclosamide at 30 nM and 100 nM concentrations, expression of collagen type I, collagen type III, and fibronectin was studied by total RNA isolation and qRT-PCR and protein collagen secretion with the use of Sircol collagen assay. The migration of SELMs was assessed by a wound-healing assay. Niclosamide had no effect on baseline SELM fibrotic factor expression. When stimulated with TGF-β₁, IL-1α, and/or TNF-α, SELM expression of collagen type I, type III, and fibronectin were upregulated, as was the secretion of total collagen in the culture medium. Treatment with niclosamide attenuated the effects of cytokine stimulation leading to a notable decrease in the mRNA expression of collagen type I, type III, and fibronectin in a concentration-dependent manner. SELM collagen secretion was also reduced by niclosamide at 100 nM concentration when examined at the protein level. Migration of both TGF-β₁ stimulated and unstimulated SELMs was also inhibited by niclosamide. In this study, we highlight the anti-fibrotic properties of niclosamide on SELMs under stimulation with pro-fibrotic and pro-inflammatory cytokines, thus proposing this compound as a possible new therapeutic agent against lung fibrosis.

A polo-like kinase inhibitor identified by computational repositioning attenuates pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease with few effective therapeutic options. Recently, drug repositioning, which involves identifying novel therapeutic potentials for existing drugs, has been popularized as a new approach for the development of novel therapeutic reagents. However, this approach has not yet been fully utilized in the field of pulmonary fibrosis.

METHODS

The present study identified novel therapeutic options for pulmonary fibrosis using a systematic computational approach for drug repositioning based on integration of public gene expression signatures of drug and diseases (in silico screening approach).

RESULTS

Among the top compounds predicted to be therapeutic for IPF by the in silico approach, we selected BI2536, a polo-like kinase (PLK) 1/2 inhibitor, as a candidate for treating pulmonary fibrosis using an in silico analysis. However, BI2536 accelerated mortality and weight loss rate in an experimental mouse model of pulmonary fibrosis. Because immunofluorescence staining revealed that PLK1 expression was dominant in myofibroblasts while PLK2 expression was dominant in lung epithelial cells, we next focused on the anti-fibrotic effect of the selective PLK1 inhibitor GSK461364. Consequently, GSK461364 attenuated pulmonary fibrosis with acceptable mortality and weight loss in mice.

CONCLUSIONS

These findings suggest that targeting PLK1 may be a novel therapeutic approach for pulmonary fibrosis by inhibiting lung fibroblast proliferation without affecting lung epithelial cells. In addition, while in silico screening is useful, it is essential to fully determine the biological activities of candidates by wet-lab validation studies.

Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.

Eicosatetraynoic Acid and Butyrate Regulate Human Intestinal Organoid Mitochondrial and Extracellular Matrix Pathways Implicated in Crohn's Disease Strictures

BACKGROUND

Perturbagen analysis of Crohn's disease (CD) ileal gene expression data identified small molecules including eicosatetraynoic acid (ETYA), which may exert an antifibrotic effect. We developed a patient-specific human intestinal organoid (HIO) model system to test small molecule regulation of mitochondrial and wound-healing functions implicated in stricturing behavior.

METHODS

HIOs were made from CD induced pluripotent stem cells with and without a loss-of-function haplotype in the DUOX2 gene implicated in ileal homeostasis and characterized under basal conditions and following exposure to butyrate and ETYA using RNA sequencing, flow cytometry, and immunofluorescent and polarized light microscopy. Mitochondrial activity was measured using high-resolution respirometry and tissue stiffness using atomic force microscopy.

RESULTS

HIOs expressed core mitochondrial and extracellular matrix (ECM) genes and enriched biologic functions implicated in CD ileal strictures; ECM gene expression was suppressed by both butyrate and ETYA, with butyrate also suppressing genes regulating epithelial proliferation. Consistent with this, butyrate, but not ETYA, exerted a profound effect on HIO epithelial mitochondrial function, reactive oxygen species production, and cellular abundance. Butyrate and ETYA suppressed HIO expression of alpha smooth muscle actin expressed by myofibroblasts, type I collagen, and collagen protein abundance. HIOs exhibited tissue stiffness comparable to normal human ileum; this was reduced by chronic ETYA exposure in HIOs carrying the DUOX2 loss-of-function haplotype.

CONCLUSIONS

ETYA regulates ECM genes implicated in strictures and suppresses collagen content and tissue stiffness in an HIO model. HIOs provide a platform to test personalized therapeutics, including small molecules prioritized by perturbagen analysis.

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

Drug Repurposing Prediction and Validation From Clinical Big Data for the Effective Treatment of Interstitial Lung Disease

Interstitial lung diseases (ILDs) are a group of respiratory disorders characterized by chronic inflammation and fibrosis of the pulmonary interstitial tissues. Although the etiology of ILD remains unclear, some drug treatments are among the primary causes of ILD. In the present study, we analyzed the FDA Adverse Event Reporting System and JMDC Inc. insurance claims to identify a coexisting drug that reduced the incidence of ILD associated with the use of an anti-arrhythmic agent, amiodarone, and found that the thrombin inhibitor dabigatran prevented the amiodarone-induced ILD in both clinical datasets. In an experimental validation of the hypothesis, long-term oral treatment of mice with amiodarone caused a gradual decrease in body weight caused by respiratory insufficiency. In the lungs of amiodarone-treated mice, infiltration of macrophages was observed in parallel with a delayed upregulation of the platelet-derived growth factor receptor α gene. In contrast, co-treatment with dabigatran significantly attenuated these amiodarone-induced changes indicative of ILD. These results suggest that dabigatran is effective in preventing drug-induced ILD. This combinatorial approach of drug repurposing based on clinical big data will pave the way for finding a new treatment with high clinical predictability and a well-defined molecular mechanism.

Mouse Models of Lung Fibrosis

The drug discovery pipeline, from discovery of therapeutic targets through preclinical and clinical development phases, to an approved product by health authorities, is a time-consuming and costly process, where a lead candidates' success at reaching the final stage is rare. Although the time from discovery to final approval has been reduced over the last decade, there is still potential to further optimize and streamline the evaluation process of each candidate as it moves through the different development phases. In this book chapter, we describe our preclinical strategies and overall decision-making process designed to evaluate the tolerability and efficacy of therapeutic candidates suitable for patients diagnosed with fibrotic lung disease. We also describe the benefits of conducting preliminary discovery trials, to aid in the selection of suitable primary and secondary outcomes to be further evaluated and assessed in subsequent internal and external validation studies. We outline all relevant research methodologies and protocols routinely performed by our research group and hope that these strategies and protocols will be a useful guide for biomedical and translational researchers aiming to develop safe and beneficial therapies for patients with fibrotic lung disease.

Therapeutic Targeting of Repurposed Anticancer Drugs in Alzheimer's Disease: Using the Multiomics Approach

AIM/HYPOTHESIS

The complexity and heterogeneity of multiple pathological features make Alzheimer's disease (AD) a major culprit to global health. Drug repurposing is an inexpensive and reliable approach to redirect the existing drugs for new indications. The current study aims to study the possibility of repurposing approved anticancer drugs for AD treatment. We proposed an in silico pipeline based on "omics" data mining that combines genomics, transcriptomics, and metabolomics studies. We aimed to validate the neuroprotective properties of repurposed drugs and to identify the possible mechanism of action of the proposed drugs in AD.

RESULTS

We generated a list of AD-related genes and then searched DrugBank database and Therapeutic Target Database to find anticancer drugs related to potential AD targets. Specifically, we researched the available approved anticancer drugs and excluded the information of investigational and experimental drugs. We developed a computational pipeline to prioritize the anticancer drugs having a close association with AD targets. From data mining, we generated a list of 2914 AD-related genes and obtained 49 potential druggable targets by functional enrichment analysis. The protein-protein interaction (PPI) studies for these genes revealed 641 interactions. We found that 15 AD risk/direct PPI genes were associated with 30 approved oncology drugs. The computational validation of candidate drug-target interactions, structural and functional analysis, investigation of related molecular mechanisms, and literature-based analysis resulted in four repurposing candidates, of which three drugs were epidermal growth factor receptor (EGFR) inhibitors.

CONCLUSION

Our computational drug repurposing approach proposed EGFR inhibitors as potential repurposing drugs for AD. Consequently, our proposed framework could be used for drug repurposing for different indications in an economical and efficient way.

PathWalks: identifying pathway communities using a disease-related map of integrated information

MOTIVATION

Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways.

RESULTS

We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vagkaratzas/PathWalks.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Repurposing drug molecules for new pulmonary therapeutic interventions

Drug repurposing with novel strategies has substantially contributed to the identification and analysis of new molecules for better pulmonary intervention. This review would offer insights into the drug repurposing for effective pulmonary therapy. The review begins by explaining the relevant background knowledge of drug repurposing, the need for drug repurposing, and their potential advantages in treating pulmonary diseases. This article takes into account clinical trial problems, drug delivery challenges, regulatory issues, and human ergonomics along with chemistry manufacturing and control strategies for effective pulmonary drug repurposing. This article elaborates on pulmonary drug repurposing with help of strengths, weaknesses, opportunities, and threat analysis. In brief, this article is the first inclusive account of drug repurposing for better pulmonary therapy. Graphical abstract.

The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

BACKGROUND

Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments.

OBJECTIVE

The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials.

METHODS

To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called "diversity." A diversity score for a given drug was calculated by measuring how "diverse" a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease.

RESULTS

From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride.

CONCLUSIONS

The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

Mucosal Inflammatory and Wound Healing Gene Programs Reveal Targets for Stricturing Behavior in Pediatric Crohn's Disease

BACKGROUND AND AIMS

Ileal strictures are the major indication for resective surgery in Crohn's disease (CD). We aimed to define ileal gene programs present at diagnosis linked with future stricturing behavior during five year follow-up, and to identify potential small molecules to reverse these gene signatures.

METHODS

Antimicrobial serologies and pre-treatment ileal gene expression were assessed in a representative subset of 249 CD patients within the RISK multicenter pediatric CD inception cohort study, including 113 that are unique to this report. These data were used to define genes associated with stricturing behavior and for model testing to predict stricturing behavior. A bioinformatics approach to define small molecules which may reverse the stricturing gene signature was applied.

RESULTS

19 of the 249 patients developed isolated B2 stricturing behavior during follow-up, while 218 remained B1 inflammatory. Using deeper RNA sequencing than in our prior report, we have now defined an inflammatory gene signature including an oncostatin M co-expression signature, tightly associated with extra-cellular matrix (ECM) gene expression in those who developed stricturing complications. We further computationally prioritize small molecules targeting macrophage and fibroblast activation and angiogenesis which may reverse the stricturing gene signature. A model containing ASCA and CBir1 serologies and a refined eight ECM gene set was significantly associated with stricturing development by year five after diagnosis (AUC (95th CI) = 0.82 (0.7-0.94)).

CONCLUSION

An ileal gene program for macrophage and fibroblast activation is linked to stricturing complications in treatment naïve pediatric CD, and may inform novel small molecule therapeutic approaches.

Suppression of miR-30a-3p Attenuates Hepatic Steatosis in Non-alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) have a high prevalence in humans in the past two decades. Here, we elucidated the functions of miR-30a-3p in the development of NAFLD and identified its potential targets. HepG-2 cells and NAFLD patients' blood samples were used in our study. Bioinformatics analysis as well as luciferase reporter assays were employed to distinguish peroxisome proliferator-activated receptor alpha (PPAR-α) as a target gene. Western blotting showed the expressions of lipid metabolic proteins and the target gene PPAR-α. Oil red O staining and triglyceride activity tested the fatty deposits after treatment with miR-30a-3p. miR-30a-3p was substantially up-regulated in NAFLD. Bioinformatics analyses showed that PPAR-α was a possible target of miR-30a-3p, linked with signaling pathways in NAFLD. PPAR-α as a novel target of miR-30a-3p, and suppression of its levels. The lipid metabolic-related proteins ACC, p-GSK-3β and FASN were up-regulated after transfecting with miR-30a-3p mimic, but the proteins CPT1, p-AMPK and UCP2 were down-regulated. miR-30a-3p inhibitor could diminish the protein manifestation of ACC, p-GSK-3β and FASN; and augment the protein manifestation of CPT1, p-AMPK and UCP2. On the contrary, overexpression of miR-30a-3p had adverse impacts on the performance of hepatocellular lipid accumulation and Triglyceride (TG) activity. Co-treatment with miR-30a-3p mimic and overexpression PPAR-α could revise the hepatic steatosis and the TG level induced by fat milk. Our findings suggest that miR-30a-3p/PPAR-α may be developed as a potential agent in NAFLD, which is enough to attenuate triglyceride accumulation and hepatic steatosis.

Pan-transcriptome-based candidate therapeutic discovery for idiopathic pulmonary fibrosis

BACKGROUND

There are two US Food and Drug Administration (FDA)-approved drugs, pirfenidone and nintedanib, for treatment of patients with idiopathic pulmonary fibrosis (IPF). However, neither of these drugs provide a cure. In addition, both are associated with several drug-related adverse events. Hence, the pursuit for newer IPF therapeutics continues. Recent studies show that joint analysis of systems-biology-level information with drug-disease connectivity are effective in discovery of biologically relevant candidate therapeutics.

METHODS

Publicly available gene expression signatures from patients with IPF were used to query a large-scale perturbagen signature library to discover compounds that can potentially reverse dysregulated gene expression in IPF. Two methods were used to calculate IPF-compound connectivity: gene expression-based connectivity and feature-based connectivity. Identified compounds were further prioritized if their shared mechanism(s) of action were IPF-related.

RESULTS

We found 77 compounds as potential candidate therapeutics for IPF. Of these, 39 compounds are either FDA-approved for other diseases or are currently in phase II/III clinical trials suggesting their repurposing potential for IPF. Among these compounds are multiple receptor kinase inhibitors (e.g. nintedanib, currently approved for IPF, and sunitinib), aurora kinase inhibitor (barasertib), epidermal growth factor receptor inhibitors (erlotinib, gefitinib), calcium channel blocker (verapamil), phosphodiesterase inhibitors (roflumilast, sildenafil), PPAR agonists (pioglitazone), histone deacetylase inhibitors (entinostat), and opioid receptor antagonists (nalbuphine). As a proof of concept, we performed in vitro validations with verapamil using lung fibroblasts from IPF and show its potential benefits in pulmonary fibrosis.

CONCLUSIONS

As about half of the candidates discovered in this study are either FDA-approved or are currently in clinical trials for other diseases, rapid translation of these compounds as potential IPF therapeutics is possible. Further, the integrative connectivity analysis framework in this study can be adapted in early phase drug discovery for other common and rare diseases with transcriptomic profiles.The reviews of this paper are available via the supplemental material section.

Targeting metabolic dysregulation for fibrosis therapy

Fibrosis is the abnormal deposition of extracellular matrix, which can lead to organ dysfunction, morbidity, and death. The disease burden caused by fibrosis is substantial, and there are currently no therapies that can prevent or reverse fibrosis. Metabolic alterations are increasingly recognized as an important pathogenic process that underlies fibrosis across many organ types. As a result, metabolically targeted therapies could become important strategies for fibrosis reduction. Indeed, some of the pathways targeted by antifibrotic drugs in development - such as the activation of transforming growth factor-β and the deposition of extracellular matrix - have metabolic implications. This Review summarizes the evidence to date and describes novel opportunities for the discovery and development of drugs for metabolic reprogramming, their associated challenges, and their utility in reducing fibrosis. Fibrotic therapies are potentially relevant to numerous common diseases such as cirrhosis, non-alcoholic steatohepatitis, chronic renal disease, heart failure, diabetes, idiopathic pulmonary fibrosis, and scleroderma.

EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge

Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and pharmaceutical companies because it reduces the cost and duration of the drug development pipeline and the likelihood of unforeseen adverse events. To date there has not been a systematic effort to identify such opportunities, in part because of the lack of a comprehensive resource for an enormous amount of unsystematic drug repositioning information to support scientists who could benefit from this endeavor. To address this challenge, we developed a new database, the Experimental Knowledge-Based Drug Repositioning Database (EK-DRD), by using text and data mining as well as manual curation. EK-DRD contains experimentally validated drug repositioning annotation for 1861 FDA-approved and 102 withdrawn small-molecule drugs. Annotation was done at four levels using 30 944 target assay records, 3999 cell assay records, 585 organism assay records, and 8889 clinical trial records. Additionally, approximately 1799 repositioning protein or target sequences coupled with 856 related diseases and 1332 pathways are linked to the drug entries. Our web-based software displays a network of integrative relationships between drugs, their repositioning targets, and related diseases. The database is fully searchable and supports extensive text, sequence, chemical structure, and relational query searches. EK-DRD is freely accessible at http://www.idruglab.com/drd/index.php .

A Web Tool for Ranking Candidate Drugs Against a Selected Disease Based on a Combination of Functional and Structural Criteria

Drug repurposing techniques allow existing drugs to be tested against diseases outside their initial spectrum, resulting in reduced cost and eliminating the long time-frames of new drug development. In silico drug repurposing further speeds up the process either by proposing drugs suitable to invert the transcriptomic profile of a disease or by indicating drugs based on their common targets or structural similarity with other drugs with similar mode of action.

Such methods usually return a number of potential repurposed drugs that need to be tested against the disease in in vitro, pre-clinical and clinical studies. Thus, it is crucial to have a more sophisticated candidate drug ranking in order to start testing from the most promising chemical substances. As a means to enhance the above decision process, we present CoDReS (Composite Drug Reranking Scoring), a drug (re-)ranking web-based tool, which combines an initial drug ranking (i.e. repurposing score or hypothesis/potentiality score) with a functional score of each drug considered in conjunction with the disease under study as well as with a structural score derived from potential drugability violations. Furthermore, a structural similarity clustering is applied on the considered drugs and a handful of structural exemplars are suggested for further in vitro and in vivo validation. The user is able to filter the results further, through structural similarity examination of the candidate drugs with drugs that have failed against the queried disease where related clinical trials have been carried out.

CoDReS is publicly available online at http://bioinformatics.cing.ac.cy/codres.

Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis

BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with an unknown etiology. Gene expression microarray data have provided some insights into the molecular mechanisms of IPF. This study aimed to identify key genes and significant signaling pathways involved in IPF using bioinformatics analysis. MATERIAL AND METHODS Differentially expressed genes (DEGs) were identified using integrated analysis of gene expression data with a robust rank aggregation (RRA) method. The Connectivity Map (CMAP) was used to identify gene-expression signatures associated with IPF. Weighted gene coexpression network analysis (WGCNA) was used to explore the functional modules involved in the pathogenesis of IPF. RESULTS A total of 191 patients with IPF and 101 normal controls from six genome-wide expression datasets were included. CMAP predicted several small molecular agents as potential gene targets in IPF. Several functional modules were detected that showed the highest correlation with IPF, including an extracellular matrix (ECM) component, and a myeloid leukocyte migration and activation component involved in the immune response. Hub genes were identified in the key functional modules that might have a role in the progression of IPF. CONCLUSIONS WGCNA was used to identify functional modules and hub genes involved in the pathogenesis of IPF.

Niclosamide alleviates pulmonary fibrosis in vitro and in vivo by attenuation of epithelial-to-mesenchymal transition, matrix proteins & Wnt/β-catenin signaling: A drug repurposing study

Drug repurposing off late has been emerging as an inspiring alternative approach to conventional, exhaustive and arduous process of drug discovery. It is a process of identifying new therapeutic values for a drug already established for the treatment of a certain condition. Our current study is aimed at repurposing the old anti-helimenthic drug Niclosamide as an anti-fibrotic drug against pulmonary fibrosis (PF). PF is most common lethal interstitial lung disease hallmarked by deposition of extracelluar matrix and scarring of lung. Heterogenous nature, untimely diagnosis and lack of appropriate treatment options make PF an inexorable lung disorder. Prevailing void in PF treatment and drug repositioning strategy of drugs kindled our interest to demonstrate the anti-fibrotic activity of Niclosamide. Our study is aimed at investigating the anti-fibrotic potential of Niclosamide in TGF-β1 induced in vitro model of PF and 21-day model of Bleomycin induced PF in vivo respectively. Our study results showed that Niclosamide holds the potential to exert anti-fibrotic effect by hampering fibroblast migration, attenuating EMT, inhibiting fibrotic signaling and by regulating WNT/β-catenin signaling as evident from protein expression studies. Our study findings can give new directions to development of Niclosamide as an anti-fibrotic agent for treatment of pulmonary fibrosis.

An Application of Computational Drug Repurposing Based on Transcriptomic Signatures

Drug repurposing is a methodology where already existing drugs are tested against diseases outside their initial usage, in order to reduce the high cost and long periods of new drug development. In silico drug repurposing further speeds up the process, by testing a large number of drugs against the biological signatures of known diseases. In this chapter, we present a step-by-step methodology of a transcriptomics-based computational drug repurposing pipeline providing a comprehensive guide to the whole procedure, from proper dataset selection to short list derivation of repurposed drugs which might act as inhibitors against the studied disease. The presented pipeline contains the selection and curation of proper transcriptomics datasets, statistical analysis of the datasets in order to extract the top over- and under-expressed gene identifiers, appropriate identifier conversion, drug repurposing analysis, repurposed drugs filtering, cross-tool screening, drug-list re-ranking, and results' validation.